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  • 1
    Online Resource
    Online Resource
    Towards Machine Learning-Aided Lung Cancer Clinical Routines: Approaches and Open Challenges
    MDPI AG ; 2022
    In:  Journal of Personalized Medicine Vol. 12, No. 3 ( 2022-03-16), p. 480-
    Details
    In: Journal of Personalized Medicine, MDPI AG, Vol. 12, No. 3 ( 2022-03-16), p. 480-
    Abstract: Advancements in the development of computer-aided decision (CAD) systems for clinical routines provide unquestionable benefits in connecting human medical expertise with machine intelligence, to achieve better quality healthcare. Considering the large number of incidences and mortality numbers associated with lung cancer, there is a need for the most accurate clinical procedures; thus, the possibility of using artificial intelligence (AI) tools for decision support is becoming a closer reality. At any stage of the lung cancer clinical pathway, specific obstacles are identified and “motivate” the application of innovative AI solutions. This work provides a comprehensive review of the most recent research dedicated toward the development of CAD tools using computed tomography images for lung cancer-related tasks. We discuss the major challenges and provide critical perspectives on future directions. Although we focus on lung cancer in this review, we also provide a more clear definition of the path used to integrate AI in healthcare, emphasizing fundamental research points that are crucial for overcoming current barriers.
    Type of Medium: Online Resource
    ISSN: 2075-4426
    URL: Article
    DOI: 10.3390/jpm12030480
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2662248-8
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  • 2
    Online Resource
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    Comprehensive Perspective for Lung Cancer Characterisation Based on AI Solutions Using CT Images
    MDPI AG ; 2020
    In:  Journal of Clinical Medicine Vol. 10, No. 1 ( 2020-12-31), p. 118-
    Details
    In: Journal of Clinical Medicine, MDPI AG, Vol. 10, No. 1 ( 2020-12-31), p. 118-
    Abstract: Lung cancer is still the leading cause of cancer death in the world. For this reason, novel approaches for early and more accurate diagnosis are needed. Computer-aided decision (CAD) can be an interesting option for a noninvasive tumour characterisation based on thoracic computed tomography (CT) image analysis. Until now, radiomics have been focused on tumour features analysis, and have not considered the information on other lung structures that can have relevant features for tumour genotype classification, especially for epidermal growth factor receptor (EGFR), which is the mutation with the most successful targeted therapies. With this perspective paper, we aim to explore a comprehensive analysis of the need to combine the information from tumours with other lung structures for the next generation of CADs, which could create a high impact on targeted therapies and personalised medicine. The forthcoming artificial intelligence (AI)-based approaches for lung cancer assessment should be able to make a holistic analysis, capturing information from pathological processes involved in cancer development. The powerful and interpretable AI models allow us to identify novel biomarkers of cancer development, contributing to new insights about the pathological processes, and making a more accurate diagnosis to help in the treatment plan selection.
    Type of Medium: Online Resource
    ISSN: 2077-0383
    URL: Article
    DOI: 10.3390/jcm10010118
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2662592-1
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  • 3
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    Online Resource
    Phenolic Compounds from Irradiated Olive Wastes: Optimization of the Heat-Assisted Extraction Using Response Surface Methodology
    MDPI AG ; 2021
    In:  Chemosensors Vol. 9, No. 8 ( 2021-08-19), p. 231-
    Details
    In: Chemosensors, MDPI AG, Vol. 9, No. 8 ( 2021-08-19), p. 231-
    Abstract: Olive pomace, an environmentally detrimental residue generated during olive oil extraction, contains bioactive compounds in demand by the food industry. To valorize this waste product a suitable yield for the extraction process is required. Heat-assisted extraction of bioactive compounds from olive pomace was optimized by a circumscribed central composite design and response surface methodology. Our previous studies indicated that irradiation could improve 2.4-fold the extractability of the main phenolic compounds from olive pomace. The effect of extraction time, temperature and solvent concentration on the yield of polyphenols from irradiated olive pomace at 5 kGy was tested. Hydroxytyrosol-1-β-glucoside, hydroxytyrosol, tyrosol and caffeic acid were quantified by High Performance Liquid Chromatography to calculate the total polyphenol content. The optimal general conditions by RSM modeling were extraction time of 120 min, temperature of 85 °C, and 76% of ethanol in water. Using these selected conditions, 19.04 ± 1.50 mg/g dry weight, 148.88 ± 8.73 mg/g extract of total polyphenols were obtained, representing a yield of 13.7%, which was consistent with the value predicted by the model. This work demonstrated the potential of residues from the olive oil industry as a suitable alternative to obtain compounds that could be used as ingredients for the food industry.
    Type of Medium: Online Resource
    ISSN: 2227-9040
    URL: Article
    DOI: 10.3390/chemosensors9080231
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2704218-2
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  • 4
    Online Resource
    Online Resource
    Sharing Biomedical Data: Strengthening AI Development in Healthcare
    MDPI AG ; 2021
    In:  Healthcare Vol. 9, No. 7 ( 2021-06-30), p. 827-
    Details
    In: Healthcare, MDPI AG, Vol. 9, No. 7 ( 2021-06-30), p. 827-
    Abstract: Artificial intelligence (AI)-based solutions have revolutionized our world, using extensive datasets and computational resources to create automatic tools for complex tasks that, until now, have been performed by humans. Massive data is a fundamental aspect of the most powerful AI-based algorithms. However, for AI-based healthcare solutions, there are several socioeconomic, technical/infrastructural, and most importantly, legal restrictions, which limit the large collection and access of biomedical data, especially medical imaging. To overcome this important limitation, several alternative solutions have been suggested, including transfer learning approaches, generation of artificial data, adoption of blockchain technology, and creation of an infrastructure composed of anonymous and abstract data. However, none of these strategies is currently able to completely solve this challenge. The need to build large datasets that can be used to develop healthcare solutions deserves special attention from the scientific community, clinicians, all the healthcare players, engineers, ethicists, legislators, and society in general. This paper offers an overview of the data limitation in medical predictive models; its impact on the development of healthcare solutions; benefits and barriers of sharing data; and finally, suggests future directions to overcome data limitations in the medical field and enable AI to enhance healthcare. This perspective is dedicated to the technical requirements of the learning models, and it explains the limitation that comes from poor and small datasets in the medical domain and the technical options that try or can solve the problem related to the lack of massive healthcare data.
    Type of Medium: Online Resource
    ISSN: 2227-9032
    URL: Article
    DOI: 10.3390/healthcare9070827
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2721009-1
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  • 5
    Online Resource
    Online Resource
    Microbiota Assessment of Fresh-Cut Apples Packaged in Two Different Films
    MDPI AG ; 2023
    In:  Microorganisms Vol. 11, No. 5 ( 2023-04-28), p. 1157-
    Details
    In: Microorganisms, MDPI AG, Vol. 11, No. 5 ( 2023-04-28), p. 1157-
    Abstract: The aim of this work was to assess the natural microbiota of packed fresh-cut apples during refrigerated storage. Two different films were tested for the package, a biodegradable (PLA) film and a conventional and commercial one (OPP). Two antioxidant additives were applied, a natural olive pomace extract and the commercial ascorbic acid used by the industries. The results revealed lower bacteria counts in samples with olive pomace extract and PLA films than in those with ascorbic acid and OPP films after 5 and 12 days of storage. These findings suggest that the use of such natural extracts as additives in fruits could delay the growth of mesophilic bacteria. The characterization and identification of the bacterial isolates from fresh-cut apple samples showed that the most prevalent species were Citrobacter freundii, Staphylococcus warneri, Pseudomonas oryzihabitans, Alcalinogenes faecalis, Corynebacterium jeikeium, Micrococcus spp., Pantoea aglomerans and Bacillus spp. Furthermore, an increase in the microbial diversity during the storage time at refrigerated temperatures was observed, except for the sample treated with olive pomace extract and packaged in OPP film. The highest microbial diversity was found for samples with ascorbic acid as an additive. This could indicate a negative effect of ascorbic acid on the microbial inhibition of apple slices. The natural olive pomace extract demonstrated potential as an antimicrobial additive for fresh-cut apples.
    Type of Medium: Online Resource
    ISSN: 2076-2607
    URL: Article
    DOI: 10.3390/microorganisms11051157
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2720891-6
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  • 6
    Online Resource
    Online Resource
    Effects of Spray Drying, Freeze Drying and Gamma Irradiation on the Antioxidant Activities of Camel and Cow Milk Fractions
    MDPI AG ; 2023
    In:  Processes Vol. 11, No. 3 ( 2023-03-16), p. 897-
    Details
    In: Processes, MDPI AG, Vol. 11, No. 3 ( 2023-03-16), p. 897-
    Abstract: This work aimed to establish an integrated approach to investigate the total phenolic content and antioxidant activities of dried skim camel and cow milk and their fractions. The milk fractions were obtained by acid or enzymatic coagulation followed by spray drying (inlet temperature/outlet temperature: 125 ± 2 °C/90 ± 2 °C) or freeze drying (−50 °C, 0.05 mbar) coupled or not to gamma irradiation (at 5, 11, 22 kGy). The results showed that the total phenolic content (measured in gallic acid equivalent, GAE) varied depending on the drying technique. The freeze-drying process corresponded to the highest values of total phenolic compounds, with 247.23 ± 2.08 mg GAE/100 g powder for the β-casein fraction of camel milk (βC CaM) and 621.13 ± 4.16 mg GAE/100 g powder for the β-casein fraction of cow milk (βC CoM). Compared to spray-dried fractions, freeze-dried fractions showed generally higher ferric reducing antioxidant power for both camel milk and cow milk. The highest values of free radical scavenging activity were seen in the spray-dried β-casein fractions of camel milk (βC CaM) and cow milk (βC CoM) and in the freeze-dried acid whey of cow and camel milk (AW CaM and AW CaM). Freeze-dried acid whey (AW CaM and AW CoM) appeared to be less sensitive to gamma irradiation at 5 and 11 kGy.
    Type of Medium: Online Resource
    ISSN: 2227-9717
    URL: Article
    DOI: 10.3390/pr11030897
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2720994-5
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  • 7
    Online Resource
    Online Resource
    Machine Learning and Feature Selection Methods for EGFR Mutation Status Prediction in Lung Cancer
    MDPI AG ; 2021
    In:  Applied Sciences Vol. 11, No. 7 ( 2021-04-06), p. 3273-
    Details
    In: Applied Sciences, MDPI AG, Vol. 11, No. 7 ( 2021-04-06), p. 3273-
    Abstract: The evolution of personalized medicine has changed the therapeutic strategy from classical chemotherapy and radiotherapy to a genetic modification targeted therapy, and although biopsy is the traditional method to genetically characterize lung cancer tumor, it is an invasive and painful procedure for the patient. Nodule image features extracted from computed tomography (CT) scans have been used to create machine learning models that predict gene mutation status in a noninvasive, fast, and easy-to-use manner. However, recent studies have shown that radiomic features extracted from an extended region of interest (ROI) beyond the tumor, might be more relevant to predict the mutation status in lung cancer, and consequently may be used to significantly decrease the mortality rate of patients battling this condition. In this work, we investigated the relation between image phenotypes and the mutation status of Epidermal Growth Factor Receptor (EGFR), the most frequently mutated gene in lung cancer with several approved targeted-therapies, using radiomic features extracted from the lung containing the nodule. A variety of linear, nonlinear, and ensemble predictive classification models, along with several feature selection methods, were used to classify the binary outcome of wild-type or mutant EGFR mutation status. The results show that a comprehensive approach using a ROI that included the lung with nodule can capture relevant information and successfully predict the EGFR mutation status with increased performance compared to local nodule analyses. Linear Support Vector Machine, Elastic Net, and Logistic Regression, combined with the Principal Component Analysis feature selection method implemented with 70% of variance in the feature set, were the best-performing classifiers, reaching Area Under the Curve (AUC) values ranging from 0.725 to 0.737. This approach that exploits a holistic analysis indicates that information from more extensive regions of the lung containing the nodule allows a more complete lung cancer characterization and should be considered in future radiogenomic studies.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    URL: Article
    DOI: 10.3390/app11073273
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2704225-X
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  • 8
    Online Resource
    Online Resource
    Sugarcane Straw as a Source of Arabinoxylans: Optimization and Economic Viability of a Two-Step Alkaline Extraction
    MDPI AG ; 2023
    In:  Foods Vol. 12, No. 12 ( 2023-06-06), p. 2280-
    Details
    In: Foods, MDPI AG, Vol. 12, No. 12 ( 2023-06-06), p. 2280-
    Abstract: Sugarcane processing produces a significant amount of byproducts in the form of straw and bagasse, which are rich in cellulose, hemicellulose, and lignin. This work aims to provide a valorization approach to sugarcane straw by optimizing a two-step alkaline extraction of arabinoxylans by a response surface methodology to evaluate a potential industrial-scale production. Sugarcane straws were delignified using an alkaline–sulfite pretreatment, followed by alkaline extraction and precipitation of arabinoxylan, a two-step process optimized using a response surface methodology. A KOH concentration of (2.93–17.1%) and temperature (18.8–61.2 °C) were chosen as independent variables, and the arabinoxylan yield (%) as a response variable. The model application shows that KOH concentration, temperature, and the interaction between both independent variables are significant in extracting arabinoxylans from straw. The best-performing condition was further characterized by FTIR, DSC, and chemical and molecular weight evaluation. The straws arabinoxylans presented high purities levels, ca. 69.93%, and an average molecular weight of 231 kDa. The overall estimated production cost of arabinoxylan from straw was 0.239 €/g arabinoxylan. This work demonstrates a two-step alkaline extraction of the arabinoxylans method, as well as their chemical characterization and economic viability analysis, that can be used as a model for industrial scale-up production.
    Type of Medium: Online Resource
    ISSN: 2304-8158
    URL: Article
    DOI: 10.3390/foods12122280
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2704223-6
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  • 9
    Online Resource
    Online Resource
    Effect of Olive Pomace Extract Application and Packaging Material on the Preservation of Fresh-Cut Royal Gala Apples
    MDPI AG ; 2023
    In:  Foods Vol. 12, No. 9 ( 2023-05-08), p. 1926-
    Details
    In: Foods, MDPI AG, Vol. 12, No. 9 ( 2023-05-08), p. 1926-
    Abstract: The efficiency of natural olive pomace extracts for enhancing the quality of fresh-cut apples was compared with commercial ascorbic acid and two different packaging films (biodegradable polylactic acid (PLA) and oriented polypropylene (OPP)) were tested. The composition of atmosphere inside the packages, the physicochemical parameters (firmness, weight loss and color), the microbial load, total phenolic content and antioxidant activity of fresh-cut apples were evaluated throughout 12 days of storage at 4 °C. After 12 days of refrigerated storage, a significant decrease in O2 was promoted in PLA films, and the weight loss of the whole packaging was higher in PLA films (5.4%) than in OPP films (0.2%). Natural olive pomace extracts reduced the load of mesophilic bacteria (3.4 ± 0.1 log CFU/g and 2.4 ± 0.1 log CFU/g for OPP and PLA films, respectively) and filamentous fungi (3.3 ± 0.1 log CFU/g and 2.44 ± 0.05 log CFU/g for OPP and PLA films, respectively) growth in fresh-cut apples after five days of storage at 4 °C, and no detection of coliforms was verified throughout the 12 days of storage. In general, the olive pomace extract preserved or improved the total phenolic index and antioxidant potential of the fruit, without significant changes in their firmness. Moreover, this extract seemed to be more effective when combined with the biodegradable PLA film packaging. This work can contribute to the availability of effective natural food additives, the sustainability of the olive oil industries and the reduction of environmental impact. It can also be useful in meeting the food industries requirements to develop new functional food products.
    Type of Medium: Online Resource
    ISSN: 2304-8158
    URL: Article
    DOI: 10.3390/foods12091926
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2704223-6
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  • 10
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    Online Resource
    How Sensors Might Help Define the External Exposome
    MDPI AG ; 2017
    In:  International Journal of Environmental Research and Public Health Vol. 14, No. 4 ( 2017-04-18), p. 434-
    Details
    In: International Journal of Environmental Research and Public Health, MDPI AG, Vol. 14, No. 4 ( 2017-04-18), p. 434-
    Type of Medium: Online Resource
    ISSN: 1660-4601
    URL: Article
    DOI: 10.3390/ijerph14040434
    Language: English
    Publisher: MDPI AG
    Publication Date: 2017
    detail.hit.zdb_id: 2175195-X
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