@article{RadivoievychKolpGrebinyketal.2023, author = {Radivoievych, Aleksandar and Kolp, Benjamin and Grebinyk, Sergii and Prylutska, Svitlana and Ritter, Uwe and Zolk, Oliver and Gl{\"o}kler, J{\"o}rn and Frohme, Marcus and Grebinyk, Anna}, title = {Silent Death by Sound: C60 Fullerene Sonodynamic Treatment of Cancer Cells}, series = {International Journal of Molecular Sciences}, volume = {24}, journal = {International Journal of Molecular Sciences}, number = {2}, publisher = {MDPI}, issn = {1422-0067}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-16877}, year = {2023}, abstract = {The acoustic pressure waves of ultrasound (US) not only penetrate biological tissues deeper than light, but they also generate light emission, termed sonoluminescence. This promoted the idea of its use as an alternative energy source for photosensitizer excitation. Pristine C60 fullerene (C60), an excellent photosensitizer, was explored in the frame of cancer sonodynamic therapy (SDT). For that purpose, we analyzed C60 effects on human cervix carcinoma HeLa cells in combination with a low-intensity US treatment. The time-dependent accumulation of C60 in HeLa cells reached its maximum at 24 h (800 ± 66 ng/106 cells). Half of extranuclear C60 is localized within mitochondria. The efficiency of the C60 nanostructure's sonoexcitation with 1 MHz US was tested with cell-based assays. A significant proapoptotic sonotoxic effect of C60 was found for HeLa cells. C60′s ability to induce apoptosis of carcinoma cells after sonoexcitation with US provides a promising novel approach for cancer treatment.}, language = {en} } @article{KoberSchaeferHollertetal.2023, author = {Kober, Liane and Schaefer, Paul and Hollert, Henner and Frohme, Marcus}, title = {A novel strategy for high-throughput sample collection, analysis and visualization of explosives' concentrations for contaminated areas}, series = {International Journal of Environmental Science and Technology}, volume = {20}, journal = {International Journal of Environmental Science and Technology}, number = {2}, publisher = {Springer Nature}, issn = {1735-2630}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-15981}, pages = {1399 -- 1410}, year = {2023}, abstract = {The use of explosives has led to a widespread distribution of 2,4,6-trinitrotoluene (TNT) and its by- and degradation products in the soil on former production and testing sites. The investigation of those large contaminated sites is so far based on a few selected soil samples, due to high costs of conventional HPLC and GC analysis, although huge differences in concentrations can already be found in small areas and different collection depths. We introduce a novel high-throughput screening system for those areas, which combines a smartphone-based collection of GPS data and soil characteristics with a fast MALDI-TOF MS quantification of explosives in soil sample extracts and finally a heatmap visualization of the explosives' spread in soil and an analysis of correlation between concentrations and soil characteristics. The analysis of a 400 m2 area presented an extensive contamination with TNT and lower concentrations of the degradation and by-products aminodinitrotoluenes (ADNT) and dinitrotoluenes (DNT) next to a former production facility for TNT. The contamination decreased in deeper soil levels and depended on the soil type. Pure humus samples showed significantly lower contaminations compared to sand and humus/sand mixtures, which is likely to be caused by an increased binding potential of the humic material. No correlation was found between the vegetation and the concentration of explosives. Since the results were obtained and visualized within several hours, the MALDI-TOF MS based comprehensive screening and heatmap analysis might be valuable for a fast and high-throughput characterization of contaminated areas.}, language = {en} } @article{D'EliaTruuLahtietal.2023, author = {D'Elia, Domenica and Truu, Jaak and Lahti, Leo and Berland, Magali and Papoutsoglou, Georgios and Ceci, Michelangelo and Zomer, Aldert and Lopes, Marta B. and Ibrahimi, Eliana and Gruca, Aleksandra and Nechyporenko, Alina and Frohme, Marcus and Klammsteiner, Thomas and Carrillo de Santa Pau, Enrique and Marcos-Zambrano, Laura Judith and Hron, Karel and Pio, Gianvito and Simeon, Andrea and Suharoschi, Ramona and Moreno-Indias, Isabel and Temko, Andriy and Nedyalkova, Miroslava and Apostol, Elena-Simona and Truică, Ciprian-Octavian and Shigdel, Rajesh and Telalović, Jasminka Hasić and Bongcam-Rudloff, Erik and Przymus, Piotr and Jordamović, Naida Babić and Falquet, Laurent and Tarazona, Sonia and Sampri, Alexia and Isola, Gaetano and P{\´e}rez-Serrano, David and Trajkovik, Vladimir and Klucar, Lubos and Loncar-Turukalo, Tatjana and Havulinna, Aki S. and Jansen, Christian and Bertelsen, Randi J. and Claesson, Marcus Joakim}, title = {Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action}, series = {Frontiers in Microbiology}, volume = {14}, journal = {Frontiers in Microbiology}, publisher = {Frontiers}, issn = {1664-302X}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-18004}, year = {2023}, abstract = {The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish "gold standard" protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory 'omics' features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices.}, language = {en} } @article{RadivoievychPrylutskaZolketal.2023, author = {Radivoievych, Aleksandar and Prylutska, Svitlana and Zolk, Oliver and Ritter, Uwe and Frohme, Marcus and Grebinyk, Anna}, title = {Comparison of Sonodynamic Treatment Set-Ups for Cancer Cells with Organic Sonosensitizers and Nanosonosensitizers}, series = {Pharmaceutics}, volume = {15}, journal = {Pharmaceutics}, number = {11}, publisher = {MDPI}, issn = {1999-4923}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-18223}, year = {2023}, abstract = {Cancer sonodynamic therapy (SDT) is the therapeutic strategy of a high-frequency ultrasound (US) combined with a special sonosensitizer that becomes cytotoxic upon US exposure. The growing number of newly discovered sonosensitizers and custom US in vitro treatment solutions push the SDT field into a need for systemic studies and reproducible in vitro experimental set-ups. In the current research, we aimed to compare two of the most used and suitable SDT in vitro set-ups—"sealed well" and "transducer in well"—in one systematic study. We assessed US pressure, intensity, and temperature distribution in wells under US irradiation. Treatment efficacy was evaluated for both set-ups towards cancer cell lines of different origins, treated with two promising sonosensitizer candidates—carbon nanoparticle C60 fullerene (C60) and herbal alkaloid berberine. C60 was found to exhibit higher sonotoxicity toward cancer cells than berberine. The higher efficacy of sonodynamic treatment with a "transducer in well" set-up than a "sealed well" set-up underlined its promising application for SDT in vitro studies. The "transducer in well" set-up is recommended for in vitro US treatment investigations based on its US-field homogeneity and pronounced cellular effects. Moreover, SDT with C60 and berberine could be exploited as a promising combinative approach for cancer treatment.}, language = {en} } @article{ShchotkinaPalamarchukSkorokhodetal.2023, author = {Shchotkina, Nataliia and Palamarchuk, Y. and Skorokhod, Iryna and Dolinchuk, Liudmyla and Sokol, Anatoliy and Motronenko, Valentina and Besarab, A. and Gorchakova, N. and Frohme, Marcus and Herzog, Michael}, title = {Features of technological regulation for cardiac bioimplants}, series = {Cell and Organ Transplantology}, volume = {11}, journal = {Cell and Organ Transplantology}, number = {1}, publisher = {Institute of Cell Therapy}, address = {Kiev}, issn = {2311-021X}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-18248}, pages = {26 -- 33}, year = {2023}, abstract = {Patients with congenital heart defects and cardiovascular diseases are required new approaches to surgical intervention. The use of biological cardiac implants, which are made from the extracellular matrix, is a promising trend in modern regenerative medicine. These bioimplants can completely replace defective tissue or organs, and when manufactured with strict protocols and quality control measures, can be safe and effective for therapeutic applications. The process of manufacturing bioimplants involves various risks that need to be assessed and mitigated with ongoing monitoring and evaluation necessary to ensure the highest standards of quality. Overall, this study was successfully evaluated the requirements for introducing a new medical device into practice and created a technical file that meets all necessary documentation for certification.}, language = {en} } @article{SchillingBeyerleinChien2023, author = {Schilling, Vincent and Beyerlein, Peter and Chien, Jeremy}, title = {A Bioinformatics Analysis of Ovarian Cancer Data Using Machine Learning}, series = {Algorithms}, volume = {16}, journal = {Algorithms}, number = {7}, publisher = {MDPI}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-17751}, year = {2023}, abstract = {The identification of biomarkers is crucial for cancer diagnosis, understanding the underlying biological mechanisms, and developing targeted therapies. In this study, we propose a machine learning approach to predict ovarian cancer patients' outcomes and platinum resistance status using publicly available gene expression data. Six classical machine-learning algorithms are compared on their predictive performance. Those with the highest score are analyzed by their feature importance using the SHAP algorithm. We were able to select multiple genes that correlated with the outcome and platinum resistance status of the patients and validated those using Kaplan-Meier plots. In comparison to similar approaches, the performance of the models was higher, and different genes using feature importance analysis were identified. The most promising identified genes that could be used as biomarkers are TMEFF2, ACSM3, SLC4A1, and ALDH4A1.}, language = {en} } @inproceedings{AlekseevaReshetnikFrohmeetal.2023, author = {Alekseeva, Victoriia and Reshetnik, Viktor and Frohme, Marcus and Kachailo, Irina and Murizyna, Irina and Nechyporenko, Alina}, title = {Investigation of the Impact of Insulin Resistance on the Bone Density of the Upper Wall of the Maxillary Sinus}, series = {Proceedings of the 3rd International Workshop of IT-professionals on Artificial Intelligence, ProfIT AI 2023, Waterloo, Canada, November 20-22, 2023}, volume = {3641}, booktitle = {Proceedings of the 3rd International Workshop of IT-professionals on Artificial Intelligence, ProfIT AI 2023, Waterloo, Canada, November 20-22, 2023}, editor = {Chumachenko, Dmytro and Kaur, Jasleen and Yakovlev, Sergiy and Morita, Plinio P.}, publisher = {CEUR-WS.org}, url = {http://nbn-resolving.de/urn:nbn:de:0074-3641-3}, pages = {216 -- 223}, year = {2023}, abstract = {The aim of our study was to investigate the impact of insulin resistance on the bone density of the upper wall of the maxillary sinus. Materials and Methods: The study included 100 female participants aged 18 to 44 years, divided into two groups. The first group consisted of individuals with insulin resistance, while the control group comprised individuals without signs of insulin resistance. In each group, we conducted an investigation of the radiological density of the upper wall of the maxillary sinus using uncertainty calculations. Results of the study suggest a potential influence of insulin resistance on the density of bone tissue around the nasal sinuses, specifically the upper wall of the maxillary sinus in our case. This parameter was found to be minimal in the group of individuals with insulin resistance. It is particularly noteworthy that both minimum and maximum bone density decreased in this group. Conclusions. The research focused on how insulin resistance affects the density of the upper wall of the maxillary sinus. By employing uncertainty calculations, the study revealed that insulin resistance is associated with a decrease in the minimum density of the upper wall of the maxillary sinus. This tendency may act as a catalyst for the emergence of significant inflammatory alterations in the nasal sinuses, serving as a foundation for the initiation of complications.}, language = {en} } @inproceedings{ReshetnikAlekseevaDevosetal.2023, author = {Reshetnik, Viktor and Alekseeva, Victoriia and Devos, Anastasiia and Nazaryan, Rosana and Gargin, Vitaliy and Nechyporenko, Alina}, title = {Implementation of the Uncertainty Calculation for the Detection of Negative Effect of Smoking on the Bone Density of Paranasal Sinuses}, series = {Proceedings of the 3rd International Workshop of IT-professionals on Artificial Intelligence, ProfIT AI 2023, Waterloo, Canada, November 20-22, 2023}, booktitle = {Proceedings of the 3rd International Workshop of IT-professionals on Artificial Intelligence, ProfIT AI 2023, Waterloo, Canada, November 20-22, 2023}, number = {3641}, editor = {Chumachenko, Dmytro and Kaur, Jasleen and Yakovlev, Sergiy and Morita, Plinio P.}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-19140}, pages = {276 -- 283}, year = {2023}, abstract = {The aim was to implement uncertainty calculation for detecting the negative effects of smoking on the bone density of the paranasal sinus. Materials and Methods: A total of 100 male participants aged 20 to 44 were included in the study and divided into two groups. The first group comprised individuals with minimal harmful habits, while the second group consisted of individuals who had been smoking for at least 10 years, consuming 1 to 2 packs of cigarettes per day. Results Bone density has a negative impact on the bone tissue of the upper wall of the maxillary sinus. The findings suggest that individuals with a pronounced decrease in minimum density, as well as those with a marked difference between minimum and maximum density values, may require heightened medical attention due to potential associations with undiagnosed diseases or specific structural characteristics in the skull. Conclusions. The uncertainty calculation was implemented for the detection of negative effect of smoking on the bone density of paranasal sinuses. The calculated difference between maximum and minimum density during the research suggests significant medical implications, especially considering the heterogeneity of the trabecular bone structure in the skull. Individuals with a marked difference may require heightened medical attention, potentially associated with undiagnosed diseases or specific structural characteristics in the skull.}, language = {en} } @article{MarcosZambranoLopezMolinaBakirGungoretal.2023, author = {Marcos-Zambrano, Laura Judith and L{\´o}pez-Molina, V{\´i}ctor Manuel and Bakir-Gungor, Burcu and Frohme, Marcus and Karaduzovic-Hadziabdic, Kanita and Klammsteiner, Thomas and Ibrahimi, Eliana and Lahti, Leo and Loncar-Turukalo, Tatjana and Dhamo, Xhilda and Simeon, Andrea and Nechyporenko, Alina and Pio, Gianvito and Przymus, Piotr and Sampri, Alexia and Trajkovik, Vladimir and Lacruz-Pleguezuelos, Blanca and Aasmets, Oliver and Araujo, Ricardo and Anagnostopoulos, Ioannis and Aydemir, {\"O}nder and Berland, Magali and Calle, M. Luz and Ceci, Michelangelo and Duman, Hatice and G{\"u}ndoğdu, Aycan and Havulinna, Aki S. and Kaka Bra, Kardokh Hama Najib and Kalluci, Eglantina and Karav, Sercan and Lode, Daniel and Lopes, Marta B. and May, Patrick and Nap, Bram and Nedyalkova, Miroslava and Paci{\^e}ncia, In{\^e}s and Pasic, Lejla and Pujolassos, Meritxell and Shigdel, Rajesh and Sus{\´i}n, Antonio and Thiele, Ines and Truică, Ciprian-Octavian and Wilmes, Paul and Yilmaz, Ercument and Yousef, Malik and Claesson, Marcus Joakim and Truu, Jaak and Carrillo de Santa Pau, Enrique}, title = {A toolbox of machine learning software to support microbiome analysis}, series = {Frontiers in Microbiology}, volume = {14}, journal = {Frontiers in Microbiology}, publisher = {Frontiers}, issn = {1664-302X}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-18271}, year = {2023}, abstract = {The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.}, language = {en} } @article{PfeilSiptrothPospisiletal.2023, author = {Pfeil, Juliane and Siptroth, Julienne and Pospisil, Heike and Frohme, Marcus and Hufert, Frank T. and Moskalenko, Olga and Yateem, Murad and Nechyporenko, Alina}, title = {Classification of Microbiome Data from Type 2 Diabetes Mellitus Individuals with Deep Learning Image Recognition}, series = {Big Data and Cognitive Computing}, volume = {7}, journal = {Big Data and Cognitive Computing}, number = {1}, publisher = {MDPI}, issn = {2504-2289}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-17184}, year = {2023}, abstract = {Microbiomic analysis of human gut samples is a beneficial tool to examine the general well-being and various health conditions. The balance of the intestinal flora is important to prevent chronic gut infections and adiposity, as well as pathological alterations connected to various diseases. The evaluation of microbiome data based on next-generation sequencing (NGS) is complex and their interpretation is often challenging and can be ambiguous. Therefore, we developed an innovative approach for the examination and classification of microbiomic data into healthy and diseased by visualizing the data as a radial heatmap in order to apply deep learning (DL) image classification. The differentiation between 674 healthy and 272 type 2 diabetes mellitus (T2D) samples was chosen as a proof of concept. The residual network with 50 layers (ResNet-50) image classification model was trained and optimized, providing discrimination with 96\% accuracy. Samples from healthy persons were detected with a specificity of 97\% and those from T2D individuals with a sensitivity of 92\%. Image classification using DL of NGS microbiome data enables precise discrimination between healthy and diabetic individuals. In the future, this tool could enable classification of different diseases and imbalances of the gut microbiome and their causative genera.}, language = {en} }