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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.
Evaluation of medical images is of key importance in the work of medical staff today. Especially this problem pays important role in otolaryngology.
The aim of our work was to develop an automatic complex method for assessing the state of the human paranasal sinuses.
Our research included 10 people of different sex and age, who were divided into groups, taking into account the recommendations of the WHO for 2019-2021. The structure of the mucous membrane of the maxillary sinus were calculated and compared.
In the course of our research, an algorithm was developed for the automatic assessment of the state of the mucous membrane of the maxillary sinus and its bone walls according to the data of the spiral computed tomography. The difference between obtained data in the manual and automatic mode is minimal.
Solutions to the 3D Model Problem of Pressure Measurement in the Area of Maxillary Sinus Anastomosis
(2020)
The ostiomeatal complex (OMC) is a key area that determines the occurrence of inflammatory processes in the paranasal sinuses (PNSs). The aim of our work was to develop a procedure for studying the OMC components in the preoperative period that allows for identification the impact of anatomical peculiarities on change of physiological pressure in the maxillary sinus. Materials and methods: The study was carried out on the basis of the otorhinolaryngological department of Kharkiv Regional Clinical Hospital in 2019-2020. It involved 100 patients of both sexes aged 20-59 years with chronic non-polyposis maxillary sinusitis. Results: The sizes of the uncinate process, the middle turbinate and the natural anastomosis were determined using the calculation of uncertainty. Basing on the data obtained, all the patients were divided into three groups. Conclusions: Changes in the size of the natural anastomosis (both an increase and its narrowing) lead to changes in pressure in the area of the anastomosis, and a decrease in ventilation in the paranasal sinuses. SCT study with subsequent 3D modeling is an informative, accurate and effective method for assessment of OMC and PNSs condition. It allows surgeons to presume the method and volume of surgery as early as at the preoperative stage, without resorting to invasive research methods.
Processing biological materials with 3-dimensional (3D) printers has attracted increasing interest in several research areas. At the same time, off-the-shelf solutions are increasingly available in a wider variety to meet the current market demand. However, most of the available bioprinters are closed source, thus, modifications are quite challenging or require an avoidable consultation process with the manufacturer. Furthermore, the entry prices for basic machines amount to several thousands of euros. Whereas, high-end 3D bio printers with a vast array of features are available for several hundred thousand euros. Due to the immense potential of this tool in the field of biotechnology it is important to extend the availability of this technology for research purposes in terms of adaptability and price. This ongoing work focuses on open-source 3D printer heads with the ability to extrude biological materials. The print heads include several techniques to process low as well as high viscose biomaterials such as agar and gelatin. Additionally, obstacles such as continuous substrate tempering or integration into existing 3D printers are addressed. The work presented is open source and thus freely adaptable to any user’s specific needs. Our goal is to process a diverse range of biomaterials with different print techniques.
The sample preparation for biological and chemical probes involves following a strict workflow to eliminate any contamination to the sample beforehand. Furthermore, it is time consuming and must be carried out by trained personnel such as a nurse or other supervisors, making it therefore expensive. The development of novel sample preparation techniques paired with modern sample analysis systems is focused on improving the operability while keeping a constant quality of results. This is important to analyse samples, which cannot be determined with current screening conditions. The analysis of analytes is required to receive a more detailed picture of the patient and to fully understand its complexity. Possible samples for in-depth analysis of chemical origin can be cholesterol or glucose. More complex samples, such as blood or saliva, require a sophisticated system, which analyses the samples for their individual compounds.
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.
Hintergrund: Etablierte Protein- und Nukleinsäure-basierte Methoden für den spezifischen Pathogennachweis sind nur unter standardisierten Laborbedingungen von geschultem Personal durchführbar und daher mit einem hohen Zeit- und Kostenaufwand verbunden. In der Nukleinsäure-basierten Diagnostik kann durch die Einführung der isothermalen Amplifikation eine schnelle und kostengünstige Alternative zur Polymerase-Kettenreaktion (PCR) verwendet werden. Die Loop-mediated isothermal amplification (LAMP) bietet aufgrund der hohen Amplifikationseffizienz vielfältige Detektionsmöglichkeiten, die sowohl für Schnelltest- als auch für Monitoring-Anwendungen geeignet sind.
Ein wesentliches Ziel dieser Arbeit war die Verbesserung der Anwendbarkeit der LAMP und die Entwicklung einer neuen Methode für den einfachen, schnellen und günstigen Nachweis von Pathogenen mittels alternativer DNA- oder Pyrophosphat-abhängiger Detektionsverfahren. Hier wurden zunächst direkte und indirekte Detektionsmethoden untersucht und darauf aufbauend ein Verfahren entwickelt, mit dem neue Metallionen-abhängige Fluoreszenzfarbstoffe für die selektive Detektion von Pyrophosphat in der LAMP und anderen enzymatischen Reaktionen identifiziert werden können. Als Alternative für die DNA-basierte Detektion in der digitalen LAMP sollten die zuvor etablierten Farbstoffe für den Pyrophosphatnachweis in einer Emulsion getestet werden. Abschließend wurde ein neuer Reaktionsmechanismus für die effiziente Generierung hochmolekularer DNA unter isothermalen Bedingungen als Alternative zur LAMP entwickelt.
Ergebnisse: Für den Nachweis RNA- und DNA-basierter Phythopathogene konnte die Echtzeit- und Endpunktdetektion mit verschiedenen Farbstoffen in einem geschlossenen System etabliert werden. Hier wurde Berberin als DNA-interkalierender Fluoreszenzfarbstoff mit vergleichbarer Sensitivität zu SYBR Green und EvaGreen erfolgreich in der LAMP mit Echtzeitdetektion eingesetzt. Ein Vorteil von Berberin gegenüber den anderen Farbstoffen ist die Toleranz der DNA-Polymerase auch bei hohen Farbstoffkonzentrationen. Berberin kann daher auch in der geschlossenen LAMP-Reaktion ohne zusätzliche Anpassung der Reaktionsbedingungen für die Endpunktdetektion verwendet werden. Darüber hinaus konnte Hydroxynaphtholblau (HNB), das für den kolorimetrischen Endpunktnachweis bekannt ist, erstmals auch für die fluorimetrische Detektion der LAMP in Echtzeit eingesetzt werden. Zusätzlich konnten in der Arbeit weitere Metallionen-abhängige Farbstoffe zur indirekten Detektion der LAMP über das Pyrophosphat identifiziert werden. Dafür wurde eine iterative Methode entwickelt, mit der potenzielle Farbstoffe hinsichtlich ihrer Enzymkompatibilität und ihrer spektralen Eigenschaften bei An- oder Abwesenheit von Manganionen selektiert werden können. Mithilfe eines kombinatorischen Screenings im Mikrotiterplattenformat konnte die komplexe Konzentrationsabhängigkeit zwischen den einzelnen Komponenten für einen fluorimetrischen Verdrängungsnachweis untersucht werden. Durch die Visualisierung des Signal-Rausch-Verhältnis’ als Intensitätsmatrix (heatmap) konnten zunächst Alizarinrot S und Tetrazyklin unter simulierten Reaktionsbedingungen selektiert werden. In der anschließenden enzymatischen LAMP-Reaktion konnte insbesondere Alizarinrot S als günstiger, nicht-toxischer und robuster Fluoreszenzfarbstoff identifiziert werden und zeigte eine Pyrophosphat-abhängige Zunahme der Fluoreszenzintensität. Die zuvor etablierten Farbstoffe (HNB, Calcein und Alizarinrot S) konnten anschließend erfolgreich für die indirekte, fluorimetrische Detektion von Pyrophosphat in einer LAMP-optimierten Emulsion eingesetzt werden. Die Stabilität und Homogenität der generierten Emulsion wurde durch den Zusatz des Emulgators Poloxamer 188 verbessert. Durch die fluoreszenzmikroskopische Analyse der Emulsion war eine eindeutige Diskriminierung der positiven und negativen Tröpfchen vor allem bei Einsatz von Calcein und Alizarinrot S möglich. Aufgrund des komplexen Primer-Designs und der hohen Wahrscheinlichkeit unspezifischer Amplifikation in der LAMP wurde eine neue Bst DNA-Polymerase-abhängige isothermale Amplifikationsreaktion entwickelt. Durch die Integration einer spezifischen Linkerstruktur (abasische Stelle oder Hexaethylenglykol) zwischen zwei Primersequenzen konnte ein bifunktioneller Primer die effiziente Regenerierung der Primerbindungsstellen gewährleisten. Der neue Primer induziert nach der spezifischen Hybridisierung auf dem Templat die Rückfaltung zu einer Haarnadelstruktur und blockiert gleichzeitig die Polymeraseaktivität am Gegenstrang, wodurch eine autozyklische Amplifikation trotz konstanter Reaktionstemperatur möglich ist. Die Effizienz der „Hinge-initiated Primer dependent Amplification“ (HIP) konnte abschließend durch die Verkürzung der Distanz zwischen einem modifizierten Hinge-Primer und einem PCR-ähnlichen Primer verbessert werden.
Schlussfolgerung: Die LAMP hat sich aufgrund der hohen Robustheit und Effizienz zu einer leistungsfähigen Alternative für die klassische PCR in der molekularbiologischen Diagnostik entwickelt. Unterschiedliche Detektionsverfahren verbessern die Leistungsfähigkeit der qualitativen und quantitativen LAMP für die Feldanwendungen und für die Diagnostik, da die neuen DNA- und Pyrophosphat-abhängigen Nachweismethoden in einer geschlossenen Reaktion eingesetzt werden können und so eine einfache Pathogendiagnostik ermöglichen. Die gezeigten Methoden können darüber hinaus zu einer Kostensenkung und Zeitersparnis gegenüber den herkömmlichen Methoden beitragen. Ein attraktives Ziel stellt die Weiterentwicklung der HIP für den Pathogennachweis als Alternative zur LAMP dar. Hierbei können die neuen LAMP-Detektionsverfahren ebenfalls Anwendung finden. Die Verwendung von Bst DNA-Polymerase-abhängigen Reaktionen ermöglicht darüber hinaus die Integration einer robusten isothermalen Amplifikation in mikrofluidische Systeme. Durch die Kombination der Probenvorbereitung, Amplifikation und Detektion sind zukünftige Anwendungen mit kurzer Analysezeit und geringem apparativen Aufwand insbesondere in der Pathogendiagnostik möglich.
Ziel dieser Arbeit war die Erhöhung der Temperaturtoleranz des Cyanobakteriums Synechocystis sp. PCC 6803 mittels ungerichteter Mutagenese und adaptiver Evolution. Trotz des erneuten Interesses an Cyanobakterien und Mikroalgen in den letzten Jahren, gibt es nur relativ wenige aktuelle Studien zum Einsatz dieser Methoden an Cyanobakterien. Zur Analyse eines mittels Mutagenese erzeugten Gemischs an Stämmen, ist es von großem Vorteil Hochdurchsatz-Methoden zur Kultivierung und zum Screening einsetzen zu können. Auf Basis eines Pipettierroboters wurde solch eine Plattform für phototrophe Mikroorganismen neu entwickelt und folgend stetig verbessert. Die Kultivierung erfolgt in 2,2ml Deepwell-Mikrotiterplatten innerhalb einer speziell angefertigten Kultivierungskammer. Schüttelbedingungen, Beleuchtung, Temperatur und CO2-Atmosphäre sind hierbei vollständig einstellbar.Die Plattform erlaubt semi-kontinuierliche Kultivierungen mit automatisierten Verdünnungen von hunderten Kulturen gleichzeitig. Automatisierte Messungen des Wachstums, des Absorptionsspektrums, der Chlorophyllkonzentration, MALDI-TOF-MS sowie eines neu entwickelten Vitalitätsassays wurden etabliert. Für die Mutagenese wurden die Letalität- und die nicht-letale Punktmutationsrate von ultravioletter Strahlung und Methylmethansulfonat für Synechocystis charakterisiert. Synechocystis wurde mit den so ermittelten optimalen Dosen mehrfach behandelt und anschließend einer in vivo Selektion unterzogen. Somit wurde dessen Temperaturtoleranz um bis zu 3°C erhöht. Über die Screeningplattform wurden die thermotolerantesten monoklonalen Stämme identifiziert. Nach einer Validierung wurde das vollständige Genom der Stämme sequenziert. Hierdurch wurden erstmals Mutationen in verschiedenen Genen mit der Langzeittemperaturtoleranz von Synechocystis in Verbindung gebracht. Bei einigen dieser Gene ist es sehr unwahrscheinlich, dass sie mittels anderer Verfahren hätten identifiziert werden können.
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.
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.
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.
Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action
(2023)
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.
Background
Diabetes mellitus type 2 is a common disease that poses a challenge to the healthcare system. The disease is very often diagnosed late. A better understanding of the relationship between the gut microbiome and type 2 diabetes can support early detection and form an approach for therapies. Microbiome analysis offers a potential opportunity to find markers for this disease. Next-generation sequencing methods can be used to identify the bacteria present in the stool sample and to generate a microbiome profile through an analysis pipeline. Statistical analysis, e.g., using Student’s t-test, allows the identification of significant differences. The investigations are not only focused on single bacteria, but on the determination of a comprehensive profile. Also, the consideration of the functional microbiome is included in the analyses. The dataset is not from a clinical survey, but very extensive.
Results
By examining 946 microbiome profiles of diabetes mellitus type 2 sufferers (272) and healthy control persons (674), a large number of significant genera (25) are revealed. It is possible to identify a large profile for type 2 diabetes disease. Furthermore, it is shown that the diversity of bacteria per taxonomic level in the group of persons with diabetes mellitus type 2 is significantly reduced compared to a healthy control group. In addition, six pathways are determined to be significant for type 2 diabetes describing the fermentation to butyrate. These parameters tend to have high potential for disease detection.
Conclusions
With this investigation of the gut microbiome of persons with diabetes type 2 disease, we present significant bacteria and pathways characteristic of this disease.
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.
Background
Type 2 diabetes mellitus is a prevalent disease that contributes to the development of various health issues, including kidney failure and strokes. As a result, it poses a significant challenge to the worldwide healthcare system. Research into the gut microbiome has enabled the identification and description of various diseases, with bacterial pathways playing a critical role in this context. These pathways link individual bacteria based on their biological functions. This study deals with the classification of microbiome pathway profiles of type 2 diabetes mellitus patients.
Methods
Pathway profiles were determined by next-generation sequencing of 16S rDNA from stool samples, which were subsequently assigned to bacteria. Then, the involved pathways were assigned by the identified gene families. The classification of type 2 diabetes mellitus is enabled by a constructed neural network. Furthermore, a feature importance analysis was performed via a game theoretic approach (SHapley Additive exPlanations). The study not only focuses on the classification using neural networks, but also on identifying crucial bacterial pathways.
Results
It could be shown that a neural network classification of type 2 diabetes mellitus and a healthy comparison group is possible with an excellent prediction accuracy. It was possible to create a ranking to identify the pathways that have a high impact on the model prediction accuracy. In this way, new associations between the alteration of, e.g. a biosynthetic pathway and the presence of diabetes mellitus type 2 disease can also be discovered. The basis is formed by 946 microbiome pathway profiles from diabetes mellitus type 2 patients (272) and healthy comparison persons (674).
Conclusion
With this study of the gut microbiome, we present an approach using a neural network to obtain a classification of healthy and type 2 diabetes mellitus and to identify the critical features. Intestinal bacteria pathway profiles form the basis.
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.
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.
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.
This study aimed to evaluate the probiotic strain Lacticaseibacillus (L.) paracasei subsp. paracasei F19 (F19) with the yeast Saccharomyces cerevisiae US-05 (US-05), using Spondias mombin L. (‘taperebá’ or ‘cajá’) juice and by-product, in four sour-type beer formulations: control, with bagasse, juice, and juice and bagasse. The viability of F19 was evaluated by pour-plating and PMA-qPCR. Fermentability, in addition to physicochemical and sensory parameters, and aroma and flavor, were evaluated during brewery by using Headspace Solid-Phase Microextraction (HS-SPME) coupled with gas chromatography–mass spectrometry (GC–MS). F19 was successful in fermenting bagasse in a MRS medium (9.28 log CFU/mL in 24 h) but had a low viability in hopped wort, growing better in formulations without bagasse or juice. No difference between formulations was observed regarding sensory acceptability, and the HS-SPME/GC-MS revealed different flavors and aroma compounds. In conclusion, the production of a potential probiotic sour beer with F19 and US-05 is feasible regarding probiotic viability. However, S. mombin, as juice or bagasse, threatened probiotic survival. Different flavors and aroma compounds were detected, whereas no difference between formulations was found regarding sensory acceptability. The moderate alcohol content achieved is important for bacterial survival and for the development of a probiotic beer with health claims.
The development of precision cancer medicine relies on novel formulation strategies for targeted drug delivery to increase the therapeutic outcome. Biocompatible polymer nanoparticles, namely dextran-graft-polyacrylamide (D-g-PAA) copolymers, represent one of the innovative non-invasive approaches for drug delivery applications in cancer therapy. In this study, the star-like D-g-PAA copolymer in anionic form (D-g-PAAan) was developed for pH-triggered targeted drug delivery of the common chemotherapeutic drugs – doxorubicin (Dox) and cisplatin (Cis). The initial D-g-PAA copolymer was synthesized by the radical graft polymerization method, and then alkaline-hydrolyzed to get this polymer in anionic form for further use for drug encapsulation. The acidification of the buffer promoted the release of loaded drugs. D-g-PAAan nanoparticles increased the toxic potential of the drugs against human and mouse lung carcinoma cells (A549 and LLC), but not against normal human lung cells (HEL299). The drug-loaded D-g-PAAan-nanoparticles promoted further oxidative stress and apoptosis induction in LLC cells. D-g-PAAan-nanoparticles improved Dox accumulation and drugs’ toxicity in a 3D LLC multi-cellular spheroid model. The data obtained indicate that the strategy of chemotherapeutic drug encapsulation within the branched D-g-PAAan nanoparticle allows not only to realize pH-triggered drug release but also to potentiate its cytotoxic, prooxidant and proapoptotic effects against lung carcinoma cells.