Non-psychotropic cannabinoids as inhibitors of TET1 protein.

Non-psychotropic cannabinoids (e.g., cannabidiol, cannabinol and cannabigerol) are contained in numerous alimentary and medicinal products. Therefore, predicting and studying their possible side effects, such as changes in DNA methylation, is an important task for assessing the safety of these products. Interference with TET enzymes by chelating ferrous ions can contribute to the altered methylation pattern. All tested cannabinoids displayed a strong affinity for Fe(II) ions. Cannabidiol and cannabinol exhibited potent inhibitory activities (IC50 = 4.8 and 6.27 µM, respectively) towards the TET1 protein, whereas cannabigerol had no effect on the enzyme activity. An in silico molecular docking study revealed marked binding potential within the catalytic cavity for CBD/CBN, but some affinity was also found for CBG, thus the total lack of activity remains unexplained. These results imply that cannabinoids could affect the activity of the TET1 protein not only due to their affinity for Fe(II) but also due to other types of interactions (e.g., hydrophobic interactions and hydrogen bonding).

Targeting of the Mitochondrial TET1 Protein by Pyrrolo[3,2-b]pyrrole Chelators.

Targeting of epigenetic mechanisms, such as the hydroxymethylation of DNA, has been intensively studied, with respect to the treatment of many serious pathologies, including oncological disorders. Recent studies demonstrated that promising therapeutic strategies could potentially be based on the inhibition of the TET1 protein (ten-eleven translocation methylcytosine dioxygenase 1) by specific iron chelators. Therefore, in the present work, we prepared a series of pyrrolopyrrole derivatives with hydrazide (1) or hydrazone (2-6) iron-binding groups. As a result, we determined that the basic pyrrolo[3,2-b]pyrrole derivative 1 was a strong inhibitor of the TET1 protein (IC50 = 1.33 µM), supported by microscale thermophoresis and molecular docking. Pyrrolo[3,2-b]pyrroles 2-6, bearing substituted 2-hydroxybenzylidene moieties, displayed no significant inhibitory activity. In addition, in vitro studies demonstrated that derivative 1 exhibits potent anticancer activity and an exclusive mitochondrial localization, confirmed by Pearson's correlation coefficient of 0.92.

Evaluation of a novel deep learning-based classifier for perifissural nodules.

OBJECTIVES: To evaluate the performance of a novel convolutional neural network (CNN) for the classification of typical perifissural nodules (PFN). METHODS: Chest CT data from two centers in the UK and The Netherlands (1668 unique nodules, 1260 individuals) were collected. Pulmonary nodules were classified into subtypes, including "typical PFNs" on-site, and were reviewed by a central clinician. The dataset was divided into a training/cross-validation set of 1557 nodules (1103 individuals) and a test set of 196 nodules (158 individuals). For the test set, three radiologically trained readers classified the nodules into three nodule categories: typical PFN, atypical PFN, and non-PFN. The consensus of the three readers was used as reference to evaluate the performance of the PFN-CNN. Typical PFNs were considered as positive results, and atypical PFNs and non-PFNs were grouped as negative results. PFN-CNN performance was evaluated using the ROC curve, confusion matrix, and Cohen's kappa. RESULTS: Internal validation yielded a mean AUC of 91.9% (95% CI 90.6-92.9) with 78.7% sensitivity and 90.4% specificity. For the test set, the reader consensus rated 45/196 (23%) of nodules as typical PFN. The classifier-reader agreement (k = 0.62-0.75) was similar to the inter-reader agreement (k = 0.64-0.79). Area under the ROC curve was 95.8% (95% CI 93.3-98.4), with a sensitivity of 95.6% (95% CI 84.9-99.5), and specificity of 88.1% (95% CI 81.8-92.8). CONCLUSION: The PFN-CNN showed excellent performance in classifying typical PFNs. Its agreement with radiologically trained readers is within the range of inter-reader agreement. Thus, the CNN-based system has potential in clinical and screening settings to rule out perifissural nodules and increase reader efficiency. KEY POINTS: • Agreement between the PFN-CNN and radiologically trained readers is within the range of inter-reader agreement. • The CNN model for the classification of typical PFNs achieved an AUC of 95.8% (95% CI 93.3-98.4) with 95.6% (95% CI 84.9-99.5) sensitivity and 88.1% (95% CI 81.8-92.8) specificity compared to the consensus of three readers.

Assessing the Accuracy of a Deep Learning Method to Risk Stratify Indeterminate Pulmonary Nodules.

Rationale: The management of indeterminate pulmonary nodules (IPNs) remains challenging, resulting in invasive procedures and delays in diagnosis and treatment. Strategies to decrease the rate of unnecessary invasive procedures and optimize surveillance regimens are needed.Objectives: To develop and validate a deep learning method to improve the management of IPNs.Methods: A Lung Cancer Prediction Convolutional Neural Network model was trained using computed tomography images of IPNs from the National Lung Screening Trial, internally validated, and externally tested on cohorts from two academic institutions.Measurements and Main Results: The areas under the receiver operating characteristic curve in the external validation cohorts were 83.5% (95% confidence interval [CI], 75.4-90.7%) and 91.9% (95% CI, 88.7-94.7%), compared with 78.1% (95% CI, 68.7-86.4%) and 81.9 (95% CI, 76.1-87.1%), respectively, for a commonly used clinical risk model for incidental nodules. Using 5% and 65% malignancy thresholds defining low- and high-risk categories, the overall net reclassifications in the validation cohorts for cancers and benign nodules compared with the Mayo model were 0.34 (Vanderbilt) and 0.30 (Oxford) as a rule-in test, and 0.33 (Vanderbilt) and 0.58 (Oxford) as a rule-out test. Compared with traditional risk prediction models, the Lung Cancer Prediction Convolutional Neural Network was associated with improved accuracy in predicting the likelihood of disease at each threshold of management and in our external validation cohorts.Conclusions: This study demonstrates that this deep learning algorithm can correctly reclassify IPNs into low- or high-risk categories in more than a third of cancers and benign nodules when compared with conventional risk models, potentially reducing the number of unnecessary invasive procedures and delays in diagnosis.

External validation of a convolutional neural network artificial intelligence tool to predict malignancy in pulmonary nodules.

BACKGROUND: Estimation of the risk of malignancy in pulmonary nodules detected by CT is central in clinical management. The use of artificial intelligence (AI) offers an opportunity to improve risk prediction. Here we compare the performance of an AI algorithm, the lung cancer prediction convolutional neural network (LCP-CNN), with that of the Brock University model, recommended in UK guidelines. METHODS: A dataset of incidentally detected pulmonary nodules measuring 5-15 mm was collected retrospectively from three UK hospitals for use in a validation study. Ground truth diagnosis for each nodule was based on histology (required for any cancer), resolution, stability or (for pulmonary lymph nodes only) expert opinion. There were 1397 nodules in 1187 patients, of which 234 nodules in 229 (19.3%) patients were cancer. Model discrimination and performance statistics at predefined score thresholds were compared between the Brock model and the LCP-CNN. RESULTS: The area under the curve for LCP-CNN was 89.6% (95% CI 87.6 to 91.5), compared with 86.8% (95% CI 84.3 to 89.1) for the Brock model (p≤0.005). Using the LCP-CNN, we found that 24.5% of nodules scored below the lowest cancer nodule score, compared with 10.9% using the Brock score. Using the predefined thresholds, we found that the LCP-CNN gave one false negative (0.4% of cancers), whereas the Brock model gave six (2.5%), while specificity statistics were similar between the two models. CONCLUSION: The LCP-CNN score has better discrimination and allows a larger proportion of benign nodules to be identified without missing cancers than the Brock model. This has the potential to substantially reduce the proportion of surveillance CT scans required and thus save significant resources.

Applications of Blockchain Technology for Data-Sharing in Oncology: Results from a Systematic Literature Review.

BACKGROUND: Timely sharing of electronic health records across providers, while ensuring data security and privacy, is essential for prompt care of cancer patients, as well as for the development of medical research and the enhancement of personalized medicine. Yet, it is not trivial to achieve efficient consent management, data exchange, and access-control policy enforcement, in particular, in decentralized settings, and given the gravity of the condition such as cancer. Using blockchain technology (BCT) has been recently advocated by research communities and gained momentum from the industry perspective. However, most of the proposed solutions are at the level of a prototype, and blockchain-based healthcare data management systems are not in place yet. SUMMARY: This paper presents a systematic literature review, aiming to analyze the motivations, advantages, and limitations, as well as barriers and future challenges faced when applying the state-of-the-art distributed ledger technology in oncology. We then discuss its outcomes and propose the direction of the future research that can help to attain integration and adoption of the BCT for data-sharing, medical research, and the pharmaceutical supply chain in oncology, as well as in healthcare in general. Key Messages:BCT has the potential to enhance data-sharing (for primary care and medical research), as well as to attain optimization of the pharmaceutical supply chain by bringing properties such as transparency, traceability, and immutability to the applications. However, BCT itself cannot guarantee data privacy and security. Thus, it is never proposed as a stand-alone technology, but as a combined technology with cryptographic techniques. Regardless of the number of existing prototypes of blockchain-based healthcare systems, due to the existing barriers of the adoption (e.g., legal, social, and technological limitations), there is a lack of evaluation in real-world settings. Aiming to overcome these limitations, we propose future research directions that include design of the privacy-preserving hybrid data storage, interoperable infrastructures and architecture, and are compliant with the international laws and regulations.

MoO3/Al2O3 catalysts for chemical-looping oxidative dehydrogenation of ethane.

MoO3/γ-Al2O3 catalysts containing 0.3-3 monolayer (ML) equivalents of MoO3 were prepared, characterized, and tested for ethane oxidative dehydrogenation (ODH) in cyclic redox and co-feed modes. Submonolayer catalysts contain highly dispersed (2D) polymolybdate structures; a complete monolayer and bulk Al2(MoO4)3 are present at >1ML loadings. High ethylene selectivity (>90%) in chemical looping (CL) ODH correlates with Mo+VI to Mo+V reduction; COx selectivity is <10% under these conditions. Mo+V and Mo+IV species trigger CH4 production resulting in much higher conversion albeit with <20% selectivity. In CL-ODH, submonolayer catalysts exhibit ethylene selectivities that decrease linearly from 96% at near-zero conversion to 70% at 45% conversion. >1ML catalysts provide higher conversions albeit with 10%-18% lower selectivity and greater selectivity loss with increasing conversion. In co-feed mode, ethylene selectivity drops to <50% at 46% conversion for a 0.6ML catalyst, but selectivity is virtually unaltered for a 3ML catalyst. We infer that at <1ML loadings, small domain size and strong Mo-O-Al bonds decrease 2D polymolybdate reducibility and enhance ethylene selectivity in CL-ODH.

Plant Nitrilase Homologues in Fungi: Phylogenetic and Functional Analysis with Focus on Nitrilases in Trametes versicolor and Agaricus bisporus.

Fungi contain many plant-nitrilase (NLase) homologues according to database searches. In this study, enzymes NitTv1 from Trametes versicolor and NitAb from Agaricus bisporus were purified and characterized as the representatives of this type of fungal NLase. Both enzymes were slightly more similar to NIT4 type than to NIT1/NIT2/NIT3 type of plant NLases in terms of their amino acid sequences. Expression of the synthetic genes in Escherichia coli Origami B (DE3) was induced with 0.02 mM isopropyl ß-D-1-thiogalactopyranoside at 20 °C. Purification of NitTv1 and NitAb by cobalt affinity chromatography gave ca. 6.6 mg and 9.6 mg of protein per 100 mL of culture medium, respectively. Their activities were determined with 25 mM of nitriles in 50 mM Tris/HCl buffer, pH 8.0, at 30 °C. NitTv1 and NitAb transformed ß-cyano-L-alanine (ß-CA) with the highest specific activities (ca. 132 and 40 U mg-1, respectively) similar to plant NLase NIT4. ß-CA was transformed into Asn and Asp as in NIT4 but at lower Asn:Asp ratios. The fungal NLases also exhibited significant activities for (aryl)aliphatic nitriles such as 3-phenylpropionitrile, cinnamonitrile and fumaronitrile (substrates of NLase NIT1). NitTv1 was more stable than NitAb (at pH 5-9 vs. pH 5-7). These NLases may participate in plant-fungus interactions by detoxifying plant nitriles and/or producing plant hormones. Their homology models elucidated the molecular interactions with various nitriles in their active sites.

Teaching a difficult topic using a problem-based concept resembling a computer game: development and evaluation of an e-learning application for medical molecular genetics.

BACKGROUND: Genetic testing rapidly penetrates into all medical specialties and medical students must acquire skills in this area. However, many of them consider it difficult. Furthermore, many find these topics less appealing and not connected to their future specialization in different fields of clinical medicine. Student-centred strategies such as problem-based learning, gamification and the use of real data can increase the appeal of a difficult topic such as genetic testing, a field at the crossroads of genetics, molecular biology and bioinformatics. METHODS: We designed an electronic teaching application which students registered in the undergraduate Medical Biology course can access online. A study was carried out to assess the influence of implementation of the new method. We performed pretest/posttest evaluation and analyzed the results using the sign test with median values. We also collected students' personal comments. RESULTS: The newly developed interactive application simulates the process of molecular genetic diagnostics of a hereditary disorder in a family. Thirteen tasks guide students through clinical and laboratory steps needed to reach the final diagnosis. Genetics and genomics are fields strongly dependent on electronic databases and computer-based data analysis tools. The tasks employ publicly available internet bioinformatic resources used routinely in medical genetics departments worldwide. Authenticity is assured by the use of modified and de-identified clinical and laboratory data from real families analyzed in our previous research projects. Each task contains links to databases and data processing tools needed to solve the task, and an answer box. If the entered answer is correct, the system allows the user to proceed to the next task. The solving of consecutive tasks arranged into a single narrative resembles a computer game, making the concept appealing. There was a statistically significant improvement of knowledge and skills after the practical class, and most comments on the application were positive. A demo version is available at https://medbio.lf2.cuni.cz/demo_m/ . Full version is available on request from the authors. CONCLUSIONS: Our concept proved to be appealing to the students and effective in teaching medical molecular genetics. It can be modified for training in the use of electronic information resources in other medical specialties.

Genetic and Functional Diversity of Nitrilases in Agaricomycotina.

Nitrilases participate in the nitrile metabolism in microbes and plants. They are widely used to produce carboxylic acids from nitriles. Nitrilases were described in bacteria, Ascomycota and plants. However, they remain unexplored in Basidiomycota. Yet more than 200 putative nitrilases are found in this division via GenBank. The majority of them occur in the subdivision Agaricomycotina. In this work, we analyzed their sequences and classified them into phylogenetic clades. Members of clade 1 (61 proteins) and 2 (25 proteins) are similar to plant nitrilases and nitrilases from Ascomycota, respectively, with sequence identities of around 50%. The searches also identified five putative cyanide hydratases (CynHs). Representatives of clade 1 and 2 (NitTv1 from Trametes versicolor and NitAg from Armillaria gallica, respectively) and a putative CynH (NitSh from Stereum hirsutum) were overproduced in Escherichia coli. The substrates of NitTv1 were fumaronitrile, 3-phenylpropionitrile, ß-cyano-l-alanine and 4-cyanopyridine, and those of NitSh were hydrogen cyanide (HCN), 2-cyanopyridine, fumaronitrile and benzonitrile. NitAg only exhibited activities for HCN and fumaronitrile. The substrate specificities of these nitrilases were largely in accordance with substrate docking in their homology models. The phylogenetic distribution of each type of nitrilase was determined for the first time.

The unique value of cardiovascular magnetic resonance in patients with suspected acute coronary syndrome and culprit-free coronary angiograms.

BACKGROUND: Patients with chest pain, elevated troponin, and unobstructed coronary disease present a clinical dilemma. The purpose of this study was to investigate the incremental diagnostic value of cardiovascular magnetic resonance (CMR) in a cohort of patients with suspected acute coronary syndrome (ACS) and unobstructed coronary arteries. RESULTS: Data files of patients meeting the inclusion criteria in two cardiology centres were searched and analysed. The inclusion criteria included: 1) thoracic pain suspected with ACS; 2) a significant increase in the high-sensitive Troponin T value; 3) ECG changes; 4) coronary arteries without any significant stenosis; 5) a CMR examination included in the diagnostic process; 6) an uncertain diagnosis before the CMR exam; and 7) the absence of known CMR and contrast media contraindications. Special attention was paid to the benefits of CMR in determining the final diagnosis. In total, 136 patients who underwent coronary angiography for chest pain were analysed. The most frequent underlying causes were myocarditis (38%) and perimyocarditis (18%), followed by angiographically unrecognised acute myocardial infarction (18%) and Takotsubo cardiomyopathy (15%). The final diagnosis remained unclear in 6% of the patients. The contribution of CMR in determining the final diagnosis determination was crucial in 57% of the patients. In another 35% of the patients, CMR confirmed the suspicion and, only 8% of the CMR examinations did not help at all and had no influence on diagnosis or treatment. CONCLUSION: CMR provided a powerful incremental diagnostic value in the cohort of patients with suspected ACS and unobstructed coronary arteries. CMR is highly recommended to be incorporated as an inalienable part of the diagnostic algorithms in these patients.

The relational modeling of hierarchical data in biodiversity databases.

The unifying element of all biodiversity data is the issue of taxon hierarchy modeling. We compared 25 existing databases in terms of handling taxa hierarchy and presentation of this data. We used documentation or demo installations of databases as a source of information and next in line was the analysis of structures using R packages provided by inspected platforms. If neither of these was available, we used the public interface of individual databases. For almost half (12) of the databases analyzed, we did not find any formalized taxa hierarchy data structure, providing only biological information about taxon membership in higher ranks, which is not fully formalizable and thus not generally usable. The least effective Adjacency List model (storing parentId of a taxon) dominates among the remaining providers. This study demonstrates the lack of attention paid by current biodiversity databases to modeling taxon hierarchy, particularly to making it available to researchers in the form of a hierarchical data structure within the data provided. For biodiversity relational databases, the Closure Table type is the most suitable of the known data models, which also corresponds to the ontology concept. However, its use is rather sporadic within the biodiversity databases ecosystem.

A Fast HPLC/UV Method for Determination of Ketoprofen in Cellular Media.

A simple, sensitive and quick HPLC method was developed for the determination of ketoprofen in cell culture media (EMEM, DMEM, RPMI). Separation was performed using a gradient on the C18 column with a mobile phase of acetonitrile and miliQ water acidified by 0.1 % (v/v) formic acid. The method was validated for parameters including linearity, accuracy, precision, limit of quantitation and limit of detection, as well as robustness. The response was found linear over the range of 3-100 µg/mL as demonstrated by the acquired value of correlation coefficient R2=0.9997. The described method is applicable for determination of various pharmacokinetic aspects of ketoprofen in vitro.

Long-term impact of COVID-19 hospitalisation among individuals with pre-existing airway diseases in the UK: a multicentre, longitudinal cohort study - PHOSP-COVID.

Background: The long-term outcomes of COVID-19 hospitalisation in individuals with pre-existing airway diseases are unknown. Methods: Adult participants hospitalised for confirmed or clinically suspected COVID-19 and discharged between 5 March 2020 and 31 March 2021 were recruited to the Post-hospitalisation COVID-19 (PHOSP-COVID) study. Participants attended research visits at 5 months and 1 year post discharge. Clinical characteristics, perceived recovery, burden of symptoms and health-related quality of life (HRQoL) of individuals with pre-existing airway disease (i.e., asthma, COPD or bronchiectasis) were compared to the non-airways group. Results: A total of 615 out of 2697 (22.8%) participants had a history of pre-existing airway diseases (72.0% diagnosed with asthma, 22.9% COPD and 5.1% bronchiectasis). At 1 year, the airways group participants were less likely to feel fully recovered (20.4% versus 33.2%, p<0.001), had higher burden of anxiety (29.1% versus 22.0%, p=0.002), depression (31.2% versus 24.7%, p=0.006), higher percentage of impaired mobility using short physical performance battery ≤10 (57.4% versus 45.2%, p<0.001) and 27% had a new disability (assessed by the Washington Group Short Set on Functioning) versus 16.6%, p=0.014. HRQoL assessed using EQ-5D-5L Utility Index was lower in the airways group (mean±SD 0.64±0.27 versus 0.73±0.25, p<0.001). Burden of breathlessness, fatigue and cough measured using a study-specific tool was higher in the airways group. Conclusion: Individuals with pre-existing airway diseases hospitalised due to COVID-19 were less likely to feel fully recovered, had lower physiological performance measurements, more burden of symptoms and reduced HRQoL up to 1 year post-hospital discharge.

Identifying key features of digital resources used during online science practicals.

As in our everyday lives, we use digital elements as part of formal and informal education. To serve their educational purpose well, systematic research is desirable to identify and measure their characteristics. This study focuses on science practicals, which are complex and vary in organizational settings and specific arrangements, including usage of digital elements. We describe the digital resources on which the online instruction of science practicals during the COVID-19 forced lockdowns was built, and their key characteristics were identified. Data were collected from science teachers in Slovakia, Czechia, Slovenia, France, and Spain. The teachers shared the web resources they used and that they would recommend, together with a description of the resources. We recorded 89 inputs representing 50 unique resources. Teachers preferred free resources, mostly for knowledge revision, and newly discovered 36% of them due to forced distant teaching. The best evaluated resources were those supporting interaction (especially among peers), focused on teaching subjects and/or ICT, ready to use, and with a clear structure. The resource most frequently mentioned and used in more than half of the countries was PhET (Interactive Simulations for Science and Math) which provides free simulations of scientific principles. Other characteristics mentioned in the literature (e.g., supporting creativity and independent solving, connecting different levels of organization, authenticity, flexibility) were not that important for the overall rating.

TET protein inhibitors: Potential and limitations.

TET proteins (methylcytosine dioxygenases) play an important role in the regulation of gene expression. Dysregulation of their activity is associated with many serious pathogenic states such as oncological diseases. Regulation of their activity by specific inhibitors could represent a promising therapeutic strategy. Therefore, this review describes various types of TET protein inhibitors in terms of their inhibitory mechanism and possible applicability. The potential and possible limitations of this approach are thoroughly discussed in the context of TET protein functionality in living systems. Furthermore, possible therapeutic strategies based on the inhibition of TET proteins are presented and evaluated, especially in the field of oncological diseases.

Respiratory sequelae of COVID-19: pulmonary and extrapulmonary origins, and approaches to clinical care and rehabilitation.

Although the exact prevalence of post-COVID-19 condition (also known as long COVID) is unknown, more than a third of patients with COVID-19 develop symptoms that persist for more than 3 months after SARS-CoV-2 infection. These sequelae are highly heterogeneous in nature and adversely affect multiple biological systems, although breathlessness is a frequently cited symptom. Specific pulmonary sequelae, including pulmonary fibrosis and thromboembolic disease, need careful assessment and might require particular investigations and treatments. COVID-19 outcomes in people with pre-existing respiratory conditions vary according to the nature and severity of the respiratory disease and how well it is controlled. Extrapulmonary complications such as reduced exercise tolerance and frailty might contribute to breathlessness in post-COVID-19 condition. Non-pharmacological therapeutic options, including adapted pulmonary rehabilitation programmes and physiotherapy techniques for breathing management, might help to attenuate breathlessness in people with post-COVID-19 condition. Further research is needed to understand the origins and course of respiratory symptoms and to develop effective therapeutic and rehabilitative strategies.

Software infrastructure and data pipelines established for technical interoperability within a cross-border cooperation for the flora of the Bohemian Forest.

Background: The timely and geographical resolutions, as well as the quantity and taxon concepts of records on the occurrence of plants near national borders is often ambiguous. This is due to the regional focus and different approaches of the contributing national and regional databases and networks of the neighbouring countries. Careful data transformation between national data providers is essential for understanding distribution patterns and its dynamics for organisms in areas along the national borders. Sharing occurrence data through the international data aggregator Global Biodiversity Information Facility (GBIF) is also complicated and has to consider that the underlying taxonomic concept and geographic information system of each single GBIF dataset might be different. In addition, some regional data providers have a restrictive (non-cc) licensing policy which does not allow data publication via the GBIF network. Therefore, it is necessary to investigate new ways to make data fit for use for a better and comprehensive understanding of the Flora of the Bohemian Forest. New information: In this paper, we present a bilateral technical interoperability solution for vascular plant occurrence data for the area between the Czech Republic and Bavaria. We describe the initial state of data providers in both countries and the factual and technical challenges in finding a sustainable concept to establish mutual data sharing. The resulting solution for a functional infrastructure and an agreed data pipeline is described in a step-by-step approach. The new distributed infrastructure allows botanists and other stakeholders from both countries to work within the cross-border context of historical and current plants' distribution.

Pladias platform: Technical description of the database structure.

Background: Digitising and aggregating local floristic data is a critical step in the study of biodiversity. The integrative web-based platform Pladias, designed to cover a wide range of data on vascular plants, was recently developed in the Czech Republic. The combination of occurrence data with species characteristics opens many opportunities for data analysis and synthesis. New information: This article describes the relational structure of the Pladias database service (PladiasDB) and the context of the platform architecture. The structure is relatively complex, as our goal was to cover: (i) species occurrence records, including their management, validation and export of revised species distribution maps, (ii) data on species characteristics with quality control tools using defined data types and (iii) separate user interfaces (UI) for professionals and the general public. We discuss the approaches chosen to model individual elements in PladiasDB and summarise the experience gained during the first five years of operation of the Pladias platform.

Low Concentrated Fractionalized Nanofibers as Suitable Fillers for Optimization of Structural-Functional Parameters of Dead Space Gel Implants after Rectal Extirpation.

Dead space after rectal resection in colorectal surgery is an area with a high risk of complications. In this study, our goal was to develop a novel 3D implant based on composite hydrogels enriched with fractionalized nanofibers. We employed, as a novel approach in abdominal surgery, the application of agarose gels functionalized with fractionalized nanofibers on pieces dozens of microns large with a well-preserved nano-substructure. This retained excellent cell accommodation and proliferation, while nanofiber structures in separated islets allowed cells a free migration throughout the gel. We found these low-concentrated fractionalized nanofibers to be a good tool for structural and biomechanical optimization of the 3D hydrogel implants. In addition, this nano-structuralized system can serve as a convenient drug delivery system for a controlled release of encapsulated bioactive substances from the nanofiber core. Thus, we present novel 3D nanofiber-based gels for controlled release, with a possibility to modify both their biomechanical properties and drug release intended for 3D lesions healing after a rectal extirpation, hysterectomy, or pelvic exenteration.