AI-Driven Optimization of PCL/PEG Electrospun Scaffolds for Enhanced In Vivo Wound Healing

Here, an artificial intelligence (AI)-based approach was employed to optimize the production of electrospun scaffolds for in vivo wound healing applications. By combining polycaprolactone (PCL) and poly(ethylene glycol) (PEG) in various concentration ratios, dissolved in chloroform (CHCl3) and dimethylformamide (DMF), 125 different polymer combinations were created. From these polymer combinations, electrospun nanofiber meshes were produced and characterized structurally and mechanically via microscopic techniques, including chemical composition and fiber diameter determination. Subsequently, these data were used to train a neural network, creating an AI model to predict the optimal scaffold production solution. Guided by the predictions and experimental outcomes of the AI model, the most promising scaffold for further in vitro analyses was identified. Moreover, we enriched this selected polymer combination by incorporating antibiotics, aiming to develop electrospun nanofiber scaffolds tailored for in vivo wound healing applications. Our study underscores three noteworthy conclusions: (i) the application of AI is pivotal in the fields of material and biomedical sciences, (ii) our methodology provides an effective blueprint for the initial screening of biomedical materials, and (iii) electrospun PCL/PEG antibiotic-bearing scaffolds exhibit outstanding results in promoting neoangiogenesis and facilitating in vivo wound treatment.

Biological Activity of 6,7-Dehydroxyroyleanone and Derivatives Obtained from Plectranthus aliciae (Codd) A.J.Paton

The Plectranthus genus (Lamiaceae) is known to be rich in abietane diterpenes. The bioactive 6,7-dehydroxyroyleanone (DHR, 1) was previously isolated from Plectranthus madagascariensis var. madagascariensis and var. aliciae. This study aimed to explore the occurrence of DHR, 1, in P. aliciae and the potential bioactivities of new semisynthetic derivatives from DHR, 1. Several extraction methods were evaluated, and the hydrodistillation, using a Clevenger apparatus, afforded the highest yield (77.8 mg/g of 1 in the essential oil). Three new acyl derivatives (2-4) were successfully prepared from 1 (yields of 86-95%). Compounds 1-4 showed antioxidant activity, antibacterial effects, potent cytotoxic activity against several cell lines, and enhanced anti-inflammatory activity that surpassed dexamethasone (positive control). These findings encourage further exploration of derivatives 2-4 for potential mechanisms of antitumoral, antioxidant, and anti-inflammatory capabilities, studying both safety and efficacy.

Application of parallel artificial membrane permeability assay technique and chemometric modeling for blood-brain barrier permeability prediction of protein kinase inhibitors

Aim: This study aims to investigate the passive diffusion of protein kinase inhibitors through the blood-brain barrier (BBB) and to develop a model for their permeability prediction. Materials & methods: We used the parallel artificial membrane permeability assay to obtain logPe values of each of 34 compounds and calculated descriptors for these structures to perform quantitative structure-property relationship modeling, creating different regression models. Results: The logPe values have been calculated for all 34 compounds. Support vector machine regression was considered the most reliable, and CATS2D_09_DA, CATS2D_04_AA, B04[N-S] and F07[C-N] descriptors were identified as the most influential to passive BBB permeability. Conclusion: The quantitative structure-property relationship-support vector machine regression model that has been generated can serve as an efficient method for preliminary screening of BBB permeability of new analogs.

Resveratrol/Selenium Nanocomposite with Antioxidative and Antibacterial Properties

In this work, we synthesized a new composite material comprised of previously formulated resveratrol nanobelt-like particles (ResNPs) and selenium nanoparticles (SeNPs), namely ResSeNPs. Characterization was provided by FESEM and optical microscopy, as well as by UV-Vis and FTIR spectroscopy, the last showing hydrogen bonds between ResNPs and SeNPs. DPPH, TBA, and FRAP assays showed excellent antioxidative abilities with ResNPs and SeNPs contributing mainly to lipid peroxidation inhibition and reducing/scavenging activity, respectively. The antibacterial effect against common medicinal implant colonizers pointed to notably higher activity against Staphylococcus isolates (minimal inhibitory concentrations 0.75-1.5%) compared to tested gram-negative species (Escherichia coli and Pseudomonas aeruginosa). Antibiofilm activity against S. aureus, S. epidermidis, and P. aeruginosa determined in a crystal violet assay was promising (up to 69%), but monitoring of selected biofilm-related gene expression (pelA and algD) indicated the necessity of the involvement of a larger number of genes in the analysis in order to further establish the underlying mechanism. Although biocompatibility screening showed some cytotoxicity and genotoxicity in MTT and alkaline comet assays, respectively, it is important to note that active antioxidative and antibacterial/antibiofilm concentrations were non-cytotoxic and non-genotoxic in normal MRC-5 cells. These results encourage further composite improvements and investigation in order to adapt it for specific biomedical purposes.

Evaluation of novel dendrimer-gold complex nanoparticles for theranostic application in oncology

Aim: Despite some successful examples of therapeutic nanoparticles reaching clinical stages, there is still a significant need for novel formulations in order to improve the selectivity and efficacy of cancer treatment. Methods: The authors developed two novel dendrimer-gold (Au) complex-based nanoparticles using two different synthesis routes: complexation method (formulation A) and precipitation method (formulation B). Using a biomimetic cancer-on-a-chip model, the authors evaluated the possible cytotoxicity and internalization by colorectal cancer cells of dendrimer-Au complex-based nanoparticles. Results: The results showed promising capabilities of these nanoparticles for selectively targeting cancer cells and delivering drugs, particularly for the formulation A nanoparticles. Conclusion: This work highlights the potential of dendrimer-Au complex-based nanoparticles as a new strategy to improve the targeting of anticancer drugs.

Analysis of modified surface topographies of titanium-based hip implants using finite element method

BACKGROUND

In order to ensure the proper function of the cementless hip implant, the connection between the femoral bone and the implant has to be as strong as possible. According to experimental studies, implants with a rough surface reduce micro-movements between femoral bone and implant, which helps form a stronger connection between them.

OBJECTIVE

The goal of this study was to analyze how half-cylinder surface topographies of different diameter values affect shear stress values and their distribution on the surface of the hip implant and trabecular femoral bone.

METHODS

Nine models with different half-cylinder diameter values (200 μm, 400 μm, and 500 μm) and distances between half-cylinders were created for the analysis using the finite element method. Each model consisted of three layers: implant, trabecular, and cortical femoral bone.

RESULTS

For all three diameter values, the highest shear stress value, for the implant layer, was located after the first half-cylinder on the side where force was defined. For the trabecular bone, the first half-cylinder was under lower amounts of shear stress.

CONCLUSION

If we only consider shear stress values, we can say that models with 400 μm and 500 μm diameter values are a better choice than models with 100 μm diameter values.

SGABU computational platform for multiscale modeling: Bridging the gap between education and research

BACKGROUND AND OBJECTIVE

In accordance with the latest aspirations in the field of bioengineering, there is a need to create a web accessible, but powerful cloud computational platform that combines datasets and multiscale models related to bone modeling, cancer, cardiovascular diseases and tissue engineering. The SGABU platform may become a powerful information system for research and education that can integrate data, extract information, and facilitate knowledge exchange with the goal of creating and developing appropriate computing pipelines to provide accurate and comprehensive biological information from the molecular to organ level.

METHODS

The datasets integrated into the platform are obtained from experimental and/or clinical studies and are mainly in tabular or image file format, including metadata. The implementation of multiscale models, is an ambitious effort of the platform to capture phenomena at different length scales, described using partial and ordinary differential equations, which are solved numerically on complex geometries with the use of the finite element method. The majority of the SGABU platform's simulation pipelines are provided as Common Workflow Language (CWL) workflows. Each of them requires creating a CWL implementation on the backend and a user-friendly interface using standard web technologies. Platform is available at https://sgabu-test.unic.kg.ac.rs/login.

RESULTS

The main dashboard of the SGABU platform is divided into sections for each field of research, each one of which includes a subsection of datasets and multiscale models. The datasets can be presented in a simple form as tabular data, or using technologies such as Plotly.js for 2D plot interactivity, Kitware Paraview Glance for 3D view. Regarding the models, the usage of Docker containerization for packing the individual tools and CWL orchestration for describing inputs with validation forms and outputs with tabular views for output visualization, interactive diagrams, 3D views and animations.

CONCLUSIONS

In practice, the structure of SGABU platform means that any of the integrated workflows can work equally well on any other bioengineering platform. The key advantage of the SGABU platform over similar efforts is its versatility offered with the use of modern, modular, and extensible technology for various levels of architecture.

An Effective, Green Synthesis Procedure for Obtaining Coumarin-Hydroxybenzohydrazide Derivatives and Assessment of Their Antioxidant Activity and Redox Status

In this study, green synthesis of two derivatives of coumarin-hydroxybenzohydrazide, (E)-2,4-dioxo-3-(1-(2-(2,3,4-trihydroxybenzoyl)hydrazyl)ethylidene)-chroman-7-yl acetate (C-HB1), and (E)-2,4-dioxo-3-(1-(2-(3,4,5-trihydroxybenzoyl)hydrazyl)ethylidene)chroman-7-yl acetate (C-HB2) is reported. Using vinegar and ethanol as a catalyst and solvent, the reactions were carried out between 3-acetyl-4-hydroxy-coumarin acetate and corresponding trihydroxybenzoyl hydrazide. The antioxidant potential of these compounds was investigated using the DPPH and ABTS assays, as well as the FRAP test. The obtained results reveal that even at very low concentrations, these compounds show excellent radical scavenging potential. The IC50 values for C-HB1 and C-HB2 in relation to the DPPH radical are 6.4 and 2.5 μM, respectively, while they are 4.5 and 2.0 μM in relation to the ABTS radical. These compounds have antioxidant activity that is comparable to well-known antioxidants such as gallic acid, NDGA, and trolox. These results are in good correlation with theoretical parameters describing these reactions. Moreover, it was found that inhibition of DPPH● follows HAT, while inactivation of ABTS+● follows SET-PT and HAT mechanisms. Additionally, coumarin-hydroxybenzohydrazide derivatives induced moderate cytotoxic activity and show significant potential to modulate redox status in HCT-116 colorectal cancer cells. The cytotoxicity was achieved via their prooxidative activity and ability to induce oxidative stress in cancer cells by increasing O2˙- concentrations, indicated by increased MDA and GSH levels. Thus, ROS manipulation can be a potential target for cancer therapies by coumarins, as cancer cells possess an altered redox balance in comparison to normal cells. According to the ADMET analysis, the compounds investigated show good pharmacokinetic and toxicological profiles similar to vitamin C and gallic acid, which makes them good candidates for application in various fields of industry and medicine.

Fractional Flow Reserve-Based Patient Risk Classification

Cardiovascular diseases (CVDs) are a leading cause of death. If not treated in a timely manner, cardiovascular diseases can cause a plethora of major life complications that can include disability and a loss of the ability to work. Globally, acute myocardial infarction (AMI) is responsible for about 3 million deaths a year. The development of strategies for prevention, but also the early detection of cardiovascular risks, is of great importance. The fractional flow reserve (FFR) is a measurement used for an assessment of the severity of coronary artery stenosis. The goal of this research was to develop a technique that can be used for patient fractional flow reserve evaluation, as well as for the assessment of the risk of death via gathered demographic and clinical data. A classification ensemble model was built using the random forest machine learning algorithm for the purposes of risk prediction. Referent patient classes were identified by the observed fractional flow reserve value, where patients with an FFR higher than 0.8 were viewed as low risk, while those with an FFR lower than 0.8 were identified as high risk. The final classification ensemble achieved a 76.21% value of estimated prediction accuracy, thus achieving a mean prediction accuracy of 74.1%, 77.3%, 78.1% and 83.6% over the models tested with 5%, 10%, 15% and 20% of the test samples, respectively. Along with the machine learning approach, a numerical approach was implemented through a 3D reconstruction of the coronary arteries for the purposes of stenosis monitoring. Even with a small number of available data points, the proposed methodology achieved satisfying results. However, these results can be improved in the future through the introduction of additional data, which will, in turn, allow for the utilization of different machine learning algorithms.

Correction: Qamar et al. Treatment of Peripheral Artery Disease Using Injectable Biomaterials and Drug-Coated Balloons: Safety and Efficacy Perspective. Pharmaceutics 2023, 15, 1813

There was an error in the original publication [...].

The Catalytic Effect of Vanadium on Sorption Properties of MgH2-Based Nanocomposites Obtained Using Low Milling Time

The effects of catalysis using vanadium as an additive (2 and 5 wt.%) in a high-energy ball mill on composite desorption properties were examined. The influence of microstructure on the dehydration temperature and hydrogen desorption kinetics was monitored. Morphological and microstructural studies of the synthesized sample were performed by X-ray diffraction (XRD), laser particle size distribution (PSD), and scanning electron microscopy (SEM) methods, while differential scanning calorimetry (DSC) determined thermal properties. To further access amorph species in the milling blend, the absorption spectra were obtained by FTIR-ATR analysis (Fourier transform infrared spectroscopy attenuated total reflection). The results show lower apparent activation energy (Eapp) and H2 desorption temperature are obtained for milling bland with 5 wt.% added vanadium. The best explanation of hydrogen desorption reaction shows the Avrami-Erofeev model for parameter n = 4. Since the obtained value of apparent activation energy is close to the Mg-H bond-breaking energy, one can conclude that breaking this bond would be the rate-limiting step of the process.

Treatment of Peripheral Artery Disease Using Injectable Biomaterials and Drug-Coated Balloons: Safety and Efficacy Perspective

The possibility of injectable biomaterials being used in the therapy of peripheral artery disease (PAD) is investigated in this article. We conducted a thorough review of the literature on the use and efficacy of biomaterials (BMs) and drug-coated balloons (DCBs). These BMs included hydrogels, collagen scaffolds, and nanoparticles. These BMs could be used alone or in combination with growth factors, stem cells, or gene therapy. The treatment of peripheral artery disease with DCBs is increasingly common in the field of interventional angiology. Studies have been carried out to examine the effectiveness of paclitaxel-coated balloons such as PaccocathTM in lowering the frequency with which further revascularization operations are required. PCB angioplasty and angioplasty without paclitaxel did not significantly vary in terms of mortality, according to the findings of a recent meta-analysis that included the results of four randomized controlled studies. On the other hand, age was found to be a factor that predicted mortality. There was a correlation between the routine utilization of scoring balloon angioplasty along with DCBs and improved clinical outcomes in de novo lesions. In both preclinical and clinical testing, the SelutionTM DCB has demonstrated efficacy and safety, but further research is required to determine whether or not it is effective and safe over the long term. In addition, we reviewed the difficulties involved in bringing injectable BMs-based medicines to clinical trials, including the approval processes required by regulatory bodies. Injectable BMs have a significant amount of therapeutic promise for PAD, which highlights the need for more research and clinical studies to be conducted in this field. In conclusion, this research focuses on the potential of injectable BMs and DCBs in the treatment of PAD as well as the hurdles that must be overcome in order to translate these treatments into clinical trials. In this particular field, there is a demand for further research as well as clinical trials.

Synthesis and characterization of innovative resveratrol nanobelt-like particles and assessment of their bioactivity, antioxidative and antibacterial properties

Recently, many studies have shown various beneficial effects of polyphenol resveratrol (Res) on human health. The most important of these effects include cardioprotective, neuroprotective, anti-cancer, anti-inflammatory, osteoinductive, and anti-microbial effects. Resveratrol has cis and trans isoforms, with the trans isoform being more stable and biologically active. Despite the results of in vitro experiments, resveratrol has limited potential for application in vivo due to its poor water solubility, sensitivity to oxygen, light, and heat, rapid metabolism, and therefore low bioavailability. The possible solution to overcome these limitations could be the synthesis of resveratrol in nanoparticle form. Accordingly, in this study, we have developed a simple, green solvent/non-solvent physicochemical method to synthesize stable, uniform, carrier-free resveratrol nanobelt-like particles (ResNPs) for applications in tissue engineering. UV-visible spectroscopy (UV-Vis) was used to identify the trans isoform of ResNPs which remained stable for at least 63 days. The additional qualitative analysis was performed by Fourier transform infrared spectroscopy (FTIR), while X-ray diffraction (XRD) determined the monoclinic structure of resveratrol with a significant difference in the intensity of diffraction peaks between commercial and nano-belt form. The morphology of ResNPs was evaluated by optical microscopy and field-emission scanning electron microscope (FE-SEM) that revealed a uniform nanobelt-like structure with an individual thickness of less than 1 μm. Bioactivity was confirmed using Artemia salina in vivo toxicity assay, while 2,2-diphenyl-1-picrylhydrazylhydrate (DPPH) reduction assay showed the good antioxidative potential of concentrations of 100 μg/ml and lower. Microdilution assay on several reference strains and clinical isolates showed promising antibacterial potential on Staphylococci, with minimal inhibitory concentration (MIC) being 800 μg/ml. Bioactive glass-based scaffolds were coated with ResNPs and characterized to confirm coating potential. All of the above make these particles a promising bioactive, easy-to-handle component in various biomaterial formulations.

Machine learning and physical based modeling for cardiac hypertrophy

Background and objective

Predicting the long-term expansion and remodeling of the left ventricle in patients is challenging task but it has the potential to be clinically very useful.

Methods

In our study, we present machine learning models based on random forests, gradient boosting, and neural networks, used to track cardiac hypertrophy. We collected data from multiple patients, and then the model was trained using the patient's medical history and present level of cardiac health. We also demonstrate a physical-based model, using the finite element procedure to simulate the development of cardiac hypertrophy.

Results

Our models were used to forecast the evolution of hypertrophy over six years. The machine learning model and finite element model provided similar results.

Conclusions

The finite element model is much slower, but it's more accurate compared to the machine learning model since it's based on physical laws guiding the hypertrophy process. On the other hand, the machine learning model is fast but the results can be less trustworthy in some cases. Both of our models, enable us to monitor the development of the disease. Because of its speed machine learning model is more likely to be used in clinical practice. Further improvements to our machine learning model could be achieved by collecting data from finite element simulations, adding them to the dataset, and retraining the model. This can result in a fast and more accurate model combining the advantages of physical-based and machine learning modeling.

Combined Biological and Numerical Modeling Approach for Better Understanding of the Cancer Viability and Apoptosis

Nowadays, biomedicine is a multidisciplinary science that requires a very broad approach to the study and analysis of various phenomena essential for a better understanding of human health. This study deals with the use of numerical simulations to better understand the processes of cancer viability and apoptosis in treatment with commercial chemotherapeutics. Starting from many experiments examining cell viability in real-time, determining the type of cell death and genetic factors that control these processes, a lot of numerical results were obtained. These in vitro test results were used to create a numerical model that gives us a new angle of observation of the proposed problem. Model systems of colon and breast cancer cell lines (HCT-116 and MDA-MB-231), as well as a healthy lung fibroblast cell line (MRC-5), were treated with commercial chemotherapeutics in this study. The results indicate a decrease in viability and the appearance of predominantly late apoptosis in the treatment, a strong correlation between parameters. A mathematical model was created and employed for a better understanding of investigated processes. Such an approach is capable of accurately simulating the behavior of cancer cells and reliably predicting the growth of these cells.

Cardiac hypertrophy simulations using parametric and echocardiography-based left ventricle model with shell finite elements

In our paper, we simulated cardiac hypertrophy with the use of shell elements in parametric and echocardiography-based left ventricle (LV) models. The hypertrophy has an impact on the change in the wall thickness, displacement field and the overall functioning of the heart. We computed both eccentric and concentric hypertrophy effects and tracked changes in the ventricle shape and wall thickness. Thickening of the wall was developed under the influence of concentric hypertrophy, while the eccentric hypertrophy produces wall thinning. To model passive stresses we used the recently developed material modal based on the Holzapfel experiments. Also, our specific shell composite finite element models for heart mechanics are much smaller and simpler to use with respect to conventional 3D models. Furthermore, the presented modeling approach of the echocardiography-based LV can serve as the basis for practical applications since it relies on the true patient-specific geometry and experimental constitutive relationships. Our model gives an insight into hypertrophy development in realistic heart geometries, and it has the potential to test medical hypotheses regarding hypertrophy evolution in a healthy and heart with a disease, under the influence of different conditions and parameters.

Numerical modelling of WNT/β-catenin signal pathway in characterization of EMT of colorectal carcinoma cell lines after treatment with Pt(IV) complexes

BACKGROUND AND OBJECTIVE

Colorectal cancer (CRC) is at the top of the most common cancer types in the world, with significant mortality rates among both men and women. Deregulation of Wnt/β-catenin pathway and cell-cell junctions' components, acquisition of invasive phenotype, epithelial-mesenchymal transition (EMT) and invasion are important for development and progression of colorectal cancer. Numerical simulation presents method for estimation of the Wnt pathway via its individual components in cells, thus providing information about EMT, migratory and invasive potential. By using this numerical model, the effectiveness of treatment in EMT suppression can be assessed. Furthermore, the model can be adapted to ``every'' cell type, application time or duration of treatment can be also modified.

METHODS

We characterized colorectal cancer (CRC) cell lines (HCT-116, SW-480) from the aspect of EMT, via markers β-catenin and E-cadherin using numerical modeling. To confirm the numerical model, cells were treated with sublethal concentrations of platinum(IV) complexes and their ligands. We confirmed β-catenin regulated expression of mesenchymal markers: N-cadherin, Vimentin and MMP-9, and decreased E-cadherin expression. Treatment-induced changes were determined in the protein expression of tested markers and results showed cell-specific responses. Molecular docking was performed to investigate exact effects of treatments on E-cadherin and β-catenin in cell-cell junctions and individually in tested cells.

RESULTS

The application of the numerical model via β-catenin and E-cadherin (experimentally measured), is largely valid for the categorization of EMT progression in cells. This numerical modeling better characterizes cells with single cell migration, higher expression of mesenchymal markers, and advanced mesenchymal phenotype like HCT-116 cell line. The model was validated for the treatments and results show HCT-116 cells as more sensitive to applied compounds, among which ligands were more potent in reducing migration and invasiveness. Anti-migratory/invasive effects were due to increased E-cadherin, cytoplasmic β-catenin expression and suppressed mesenchymal markers. In silico methods showed higher affinity of tested chemicals towards free β-catenin, which is the key for regulation of migratory/invasive potential.

CONCLUSIONS

Our study shows that, no matter individual properties of cell lines and EMT degree, de novo formation of intercellular junctions stands in the basis of anti-migratory/invasive process.

The Effects of BSA-Stabilized Selenium Nanoparticles and Sodium Selenite Supplementation on the Structure, Oxidative Stress Parameters and Selenium Redox Biology in Rat Placenta

The chemical element selenium (Se) is a nonmetal that is in trace amounts indispensable for normal cellular functioning. During pregnancy, a low Se status can increase the risk of oxidative stress. However, elevated concentrations of Se in the body can also cause oxidative stress. This study aimed to compare the effects of BSA-stabilized Se nanoparticles (SeNPs, Se0) (BSA-bovine serum albumin) and inorganic sodium selenite (NaSe, Se+4) supplementation on the histological structure of the placenta, oxidative stress parameters and the total placental Se concentration of Wistar rats during pregnancy. Pregnant females were randomized into four groups: (i) intact controls; (ii) controls that were dosed by daily oral gavage with 8.6% bovine serum albumin (BSA) and 0.125 M vit C; (iii) the SeNP group that was administered 0.5 mg of SeNPs stabilized with 8.6% BSA and 0.125 M vit C/kg bw/day by oral gavage dosing; (iv) the NaSe group, gavage dosed with 0.5 mg Na2SeO3/kg bw/day. The treatment of pregnant females started on gestational day one, lasted until day 20, and on day 21 of gestation, the fetuses with the placenta were removed from the uterus. Our findings show that the mode of action of equivalent concentrations of Se in SeNPs and NaSe depended on its redox state and chemical structure. Administration of SeNPs (Se0) increased fetal lethality and induced changes in the antioxidative defense parameters in the placenta. The accumulation of Se in the placenta was highest in SeNP-treated animals. All obtained data indicate an increased bioavailability of Se in its organic nano form and Se0 redox state in comparison to its inorganic sodium selenite form and Se+4 redox state.

Gender-specific differences in hemodialysis patients: a multicenter longitudinal study from Serbia

PURPOSE

The study was undertaken with the aim to determine gender-specific differences in incident hemodialysis (HD) patient and their changes over time.

METHODS

The retrospective longitudinal closed cohort study involved 441 incident patients starting HD in 2014 and followed for 1-59 (median 43, IQR 40) months. Demographic, clinical data, treatment characteristics, laboratory findings and outcome were abstracted from the patients' medical records.

RESULTS

The relative number of males on HD was about twice that of females throughout the five years investigated. At the beginning of the study, no significant differences were found in the main demographic and clinical characteristics except that diabetes was more often the underlying disease in men than in women. Systolic blood pressure decreased over time significantly more in females than in males. Throughout the study spKt/V was significantly higher in females than in males, but it increased in patients of both genders. There were no gender differences for comorbidities, vascular access and the majority of laboratory findings except for higher serum levels of creatinine and CRP in men than in women. Relatively more females were treated with erythropoiesis stimulating agents and phosphate binders than males. Age and malignancy were selected as significant predictors of mortality for both genders, and, in addition, polycystic kidney disease, serum level of albumin and CRP for men, but spKt/V for women.

CONCLUSION

Some significant gender differences were observed throughout, while others appeared during the study but none of them were due to gender inequalities in the applied treatment.

In vitro colistin susceptibility of pandrug-resistant Acinetobacter baumannii is restored in the presence of selenium nanoparticles

AIMS

To investigate the synergistic activity of colistin and selenium nanoparticles (SeNPs) against pandrug-resistant (PDR) Acinetobacter baumannii.

METHODS AND RESULTS

Chequerboard and time-kill assays were employed to explore the potential synergistic interactions between colistin and SeNPs against A. baumannii isolates (8), previously determined as colistin-resistant (MIC range 16-256 μg ml^-1 ). Also, whole-genome sequencing (WGS) and gene expression analyses were used to elucidate the mechanisms of colistin resistance. Exceptionally strong synergistic activity (FICI range 0.004-0.035) of colistin and SeNPs against colistin-resistant isolates was revealed. Colistin (0.5 or 1 μg ml^-1 ) used in combination with SeNPs (0.5 μg ml^-1 ) was able to reduce initial inoculum during the first 4 h of incubation, in contrast to colistin (0.5, 1 or 2 μg ml^-1 ) alone.

CONCLUSIONS

These findings propose colistin/SeNPs combination as a new option to fight PDR A. baumannii, the therapeutic possibilities of which should be proved in future in vivo studies.

SIGNIFICANCE AND IMPACT OF STUDY

Here we present the first evidence of synergy between colistin and selenium compounds against bacteria in general. Also, WGS and gene expression analyses provide some new insights into A. baumannii colistin resistance mechanisms.

Synthesis and characterization of a collagen-based composite material containing selenium nanoparticles

Multidrug-resistant bacterial strains represent an emerging global health threat and a great obstacle for bone tissue engineering. One of the major components of the extracellular matrix of the bone is a collagen protein, while selenium is an element that has antimicrobial potential, and is also important for bone metabolism and bone health. Here we represent the incorporation of selenium nanoparticles (SeNPs) synthesized by the green chemical reduction method into collagen gels to produce a composite material, collagen/SeNPs, with antimicrobial properties. The samples were comprehensively characterized by zeta potential measurements, dynamic light scattering inductively coupled plasma-mass spectrometry (ICP-MS), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), optical microscopy, field-emission scanning electron microscopy (FE-SEM), and differential scanning calorimetry The cytotoxicity of the SeNPS, as well as collagen/SeNPs, was tested on the MRC-5 cells. It was revealed that collagen/SeNPS expressed a lower cytotoxic effect. Collagen/SeNPs showed significant antibacterial activity against all tested Gram-positive strains, the major causative agents of orthopedic infections as well as Candida albicans. Furthermore, three-dimensional β-tricalcium phosphate (3D-TCP) scaffolds were fabricated by a well-established 3D printing (lithography) method, and afterward preliminary coated by newly-synthesized SeNPs or collagen/SeNPs. In addition, uncoated 3D-TCP scaffolds as well as coated by collagen/SeNPs were subjected to biofilm formation. The production of Staphylococcus aureus biofilm on coated scaffolds by collagen/SeNPs was significantly reduced compared to the uncoated ones.

Disease Progression of Hypertrophic Cardiomyopathy: Modeling Using Machine Learning

BACKGROUND

Cardiovascular disorders in general are responsible for 30% of deaths worldwide. Among them, hypertrophic cardiomyopathy (HCM) is a genetic cardiac disease that is present in about 1 of 500 young adults and can cause sudden cardiac death (SCD).

OBJECTIVE

Although the current state-of-the-art methods model the risk of SCD for patients, to the best of our knowledge, no methods are available for modeling the patient's clinical status up to 10 years ahead. In this paper, we propose a novel machine learning (ML)-based tool for predicting disease progression for patients diagnosed with HCM in terms of adverse remodeling of the heart during a 10-year period.

METHODS

The method consisted of 6 predictive regression models that independently predict future values of 6 clinical characteristics: left atrial size, left atrial volume, left ventricular ejection fraction, New York Heart Association functional classification, left ventricular internal diastolic diameter, and left ventricular internal systolic diameter. We supplemented each prediction with the explanation that is generated using the Shapley additive explanation method.

RESULTS

The final experiments showed that predictive error is lower on 5 of the 6 constructed models in comparison to experts (on average, by 0.34) or a consortium of experts (on average, by 0.22). The experiments revealed that semisupervised learning and the artificial data from virtual patients help improve predictive accuracies. The best-performing random forest model improved R² from 0.3 to 0.6.

CONCLUSIONS

By engaging medical experts to provide interpretation and validation of the results, we determined the models' favorable performance compared to the performance of experts for 5 of 6 targets.

Development of SGABU Platform for Multiscale Modeling

SGABU platform was created as a computational platform for multiscale modelling in biomedical engineering. This is one of the few proposed integrated platforms that include different areas of bioengineering. The platform includes already developed solutions, various datasets and models related to cancer, cardiovascular, bone disorders, and tissue engineering. The biggest obstacle in designing a platform of this type is the use of different tools for each of the layers of architecture for models which are created using different technologies and their integration and visualization within a platform. This study describes the technologies that were used for building the platform and methods for data and models visualization. The goal was to build the most flexible system capable of executing tools of various nature and connecting them into a platform.

Carotid Artery Segmentation Using Convolutional Neural Network in Ultrasound Images

Cardiovascular disease (CVD) is one of the leading causes of death in urban areas. Carotid artery segmentation is the initial step in the automated diagnosis of carotid artery disease. The segmentation of carotid wall and lumen region boundaries are used as an essential part in assessing plaque morphology. In this paper, two types of Convolutional Neural Network (CNN) architectures are used for segmentation: U-Net and SegNet. The models used in this paper are applied on 257 ultrasound images containing a transverse section of the vessel acquired by ultrasound. Ultrasound imaging is noninvasive, completely unharming for the patient and a low-cost imaging method, but the main challenge when working with this kind of images is a very low signal to noise ratio and the process of imaging is highly dependent on the device operator. Different models are tested for various ranges of hyperparameter values and compared using different metrics. The model presented in this paper achieved over 94% Dice Coefficient for wall and lumen segmentation when trained during 100 epochs.

Patch-based Convolutional Neural Network for Atherosclerotic Carotid Plaque Semantic Segmentation

Atherosclerotic plaque deposition within the coronary vessel wall leads to arterial stenosis and if not adequately treated, it may potentially have deteriorating consequences, such as a debilitating stroke, thus making early detection of the most importance. The manual plaque components annotation process is both time and resource consuming, therefore, an automatic and accurate segmentation tool is necessary. The main aim of this paper is to present the model for identification and segmentation of the atherosclerotic plaque components such as lipid core, fibrous and calcified tissue, by using Convolutional Neural Network on patch-based segments of ultrasound images. There was some research done on the topic of plaque components segmentation, but not in ultrasound imaging data. Due to the size of some plaque components being only a couple of millimeters, we argue that training a neural network on smaller image patches will perform better than a classifier based on the whole image. Besides the size of components, this decision is motivated by the observation that plaque components are not uniformly distributed throughout the whole carotid wall and that a locality-sensitive segmentation is likely to obtain better segmentation accuracy. Our model achieved good results in the segmentation of fibrous tissue but had difficulties in the segmentation of lipid and calcified tissue due to the quality of ultrasound images.

Numerical Analysis of the Impact of Vibration on the Lumbar Spine of the Driver

The human body, as a complex biomechanical system, is daily exposed to oscillatory movements. Vibrations are observed as small displacements of points compared to the dimensions of the system. The body's sensitivity to vibration depends on many factors, such as body position, muscle tension, frequency, amplitude, and direction of vibration. Comfort is one of the important factors in the study of the quality of the vehicle. Exposure to vibrations over a long time can seriously and permanently damage some organs of the body. In this paper, the influence of vibrations on the lumbar part of the human body was investigated. Vibrations were determined experimentally while the vehicle was moving on the highway for the case of two different speeds. A 3D computer model of a lumbar spine was developed using CT scans. The acceleration values obtained by the experiment were the input values for the numerical analysis of the lumbar spine using the Ansys software package.

Artificial intelligence approach towards assessment of condition of COVID-19 patients - Identification of predictive biomarkers associated with severity of clinical condition and disease progression

BACKGROUND AND OBJECTIVES

Although ML has been studied for different epidemiological and clinical issues as well as for survival prediction of COVID-19, there is a noticeable shortage of literature dealing with ML usage in prediction of disease severity changes through the course of the disease. In that way, predicting disease progression from mild towards moderate, severe and critical condition, would help not only to respond in a timely manner to prevent lethal results, but also to minimize the number of patients in hospitals where this is not necessary.

METHODS

We present a methodology for the classification of patients into 4 distinct categories of the clinical condition of COVID-19 disease. Classification of patients is based on the values of blood biomarkers that were assessed by Gradient boosting regressor and which were selected as biomarkers that have the greatest influence in the classification of patients with COVID-19.

RESULTS

The results show that among several tested algorithms, XGBoost classifier achieved best results with an average accuracy of 94% and an average F1-score of 94.3%. We have also extracted 10 best features from blood analysis that are strongly associated with patient condition and based on those features we can predict the severity of the clinical condition.

CONCLUSIONS

The main advantage of our system is that it is a decision tree-based algorithm which is easier to interpret, instead of the use of black box models, which are not appealing in medical practice.

Epidemiological Predictive Modeling of COVID-19 Infection: Development, Testing, and Implementation on the Population of the Benelux Union

Since the outbreak of coronavirus disease-2019 (COVID-19), the whole world has taken interest in the mechanisms of its spread and development. Mathematical models have been valuable instruments for the study of the spread and control of infectious diseases. For that purpose, we propose a two-way approach in modeling COVID-19 spread: a susceptible, exposed, infected, recovered, deceased (SEIRD) model based on differential equations and a long short-term memory (LSTM) deep learning model. The SEIRD model is a compartmental epidemiological model with included components: susceptible, exposed, infected, recovered, deceased. In the case of the SEIRD model, official statistical data available online for countries of Belgium, Netherlands, and Luxembourg (Benelux) in the period of March 15 2020 to March 15 2021 were used. Based on them, we have calculated key parameters and forward them to the epidemiological model, which will predict the number of infected, deceased, and recovered people. Results show that the SEIRD model is able to accurately predict several peaks for all the three countries of interest, with very small root mean square error (RMSE), except for the mild cases (maximum RMSE was 240.79 ± 90.556), which can be explained by the fact that no official data were available for mild cases, but this number was derived from other statistics. On the other hand, LSTM represents a special kind of recurrent neural network structure that can comparatively learn long-term temporal dependencies. Results show that LSTM is capable of predicting several peaks based on the position of previous peaks with low values of RMSE. Higher values of RMSE are observed in the number of infected cases in Belgium (RMSE was 535.93) and Netherlands (RMSE was 434.28), and are expected because of thousands of people getting infected per day in those countries. In future studies, we will extend the models to include mobility information, variants of concern, as well as a medical intervention, etc. A prognostic model could help us predict epidemic peaks. In that way, we could react in a timely manner by introducing new or tightening existing measures before the health system is overloaded.

Turbulent finite element model applied for blood flow calculation in arterial bifurcation

BACKGROUND AND OBJECTIVE

Due to the relatively low fluid velocities in major arteries and veins, blood flow is by default laminar, however, turbulence can occur as a result of stenosis or other obstacles. Hemodynamic parameters like Wall Shear Stress or Oscillatory Shear Index can be used for plaque formation prediction, and these parameters are depended on the nature of the flow. Implementation of the k-ω turbulent flow in the Finite Element solver aims to improve numerical analysis of cardio-vascular condition development and progression. Calculation of turbulent fluid flow in this paper is performed using a two-equation turbulent finite element model that can calculate values in the viscous sublayer.

METHODS

Implicit integration of the equations is used for determining the fluid velocity, turbulent kinetic energy and dissipation of turbulent kinetic energy. These values are calculated in the finite element nodes for each step of the incremental-iterative procedure. Developed turbulent finite element model with the customized generation of finite element meshes is used for calculating complex blood flow problems.

RESULTS

Turbulent model is verified on an example of fluid flow in the backward-facing step channel and analysis results correspond well with the experimental ones from the literature. Further, a turbulent model is applied for the simulation of blood flow through artery bifurcation. Verification of numerical examples obtained using different commercial software packages (Ansys, COMSOL Multiphysics) ensuring usage and accuracy of PAK in-house solver.

CONCLUSIONS

Analysis results show that turbulence cannot be neglected in the modelling of cardio-vascular conditions and that cardiologists can use the proposed tools and methods for investigating the hemodynamic conditions inside the bifurcation of arteries. Appropriate agreement between experimental results, and results obtained using commercial solutions and the k-ω turbulent flow in the Finite Element solver PAK, validate methodology presented in this paper. However, small deviations between the results underline the importance of the proper boundary condition prescription and mesh size and node distribution, which is also discussed in this paper. Due to the implicit integration implemented in PAK solver, time step size has an insignificant influence on the analysis results, assuming the initial time increments are sufficiently small to ensure proper discretization of velocity and pressure pulsatile functions.

Metal- and metalloid-based compounds to target and reverse cancer multidrug resistance

Drug resistance remains the major cause of cancer treatment failure especially at the late stage of the disease. However, based on their versatile chemistry, metal and metalloid compounds offer the possibility to design fine-tuned drugs to circumvent and even specifically target drug-resistant cancer cells. Based on the paramount importance of platinum drugs in the clinics, two main areas of drug resistance reversal strategies exist: overcoming resistance to platinum drugs as well as multidrug resistance based on ABC efflux pumps. The current review provides an overview of both aspects of drug design and discusses the open questions in the field. The areas of drug resistance covered in this article involve: 1) Altered expression of proteins involved in metal uptake, efflux or intracellular distribution, 2) Enhanced drug efflux via ABC transporters, 3) Altered metabolism in drug-resistant cancer cells, 4) Altered thiol or redox homeostasis, 5) Altered DNA damage recognition and enhanced DNA damage repair, 6) Impaired induction of apoptosis and 7) Altered interaction with the immune system. This review represents the first collection of metal (including platinum, ruthenium, iridium, gold, and copper) and metalloid drugs (e.g. arsenic and selenium) which demonstrated drug resistance reversal activity. A special focus is on compounds characterized by collateral sensitivity of ABC transporter-overexpressing cancer cells. Through this approach, we wish to draw the attention to open research questions in the field. Future investigations are warranted to obtain more insights into the mechanisms of action of the most potent compounds which target specific modalities of drug resistance.

A machine learning-based risk stratification model for ventricular tachycardia and heart failure in hypertrophic cardiomyopathy

BACKGROUND

Machine learning (ML) and artificial intelligence are emerging as important components of precision medicine that enhance diagnosis and risk stratification. Risk stratification tools for hypertrophic cardiomyopathy (HCM) exist, but they are based on traditional statistical methods. The aim was to develop a novel machine learning risk stratification tool for the prediction of 5-year risk in HCM. The goal was to determine if its predictive accuracy is higher than the accuracy of the state-of-the-art tools.

METHOD

Data from a total of 2302 patients were used. The data were comprised of demographic characteristics, genetic data, clinical investigations, medications, and disease-related events. Four classification models were applied to model the risk level, and their decisions were explained using the SHAP (SHapley Additive exPlanations) method. Unwanted cardiac events were defined as sustained ventricular tachycardia occurrence (VT), heart failure (HF), ICD activation, sudden cardiac death (SCD), cardiac death, and all-cause death.

RESULTS

The proposed machine learning approach outperformed the similar existing risk-stratification models for SCD, cardiac death, and all-cause death risk-stratification: it achieved higher AUC by 17%, 9%, and 1%, respectively. The boosted trees achieved the best performing AUC of 0.82. The resulting model most accurately predicts VT, HF, and ICD with AUCs of 0.90, 0.88, and 0.87, respectively.

CONCLUSIONS

The proposed risk-stratification model demonstrates high accuracy in predicting events in patients with hypertrophic cardiomyopathy. The use of a machine-learning risk stratification model may improve patient management, clinical practice, and outcomes in general.

Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review

COVID-19 is one of the greatest challenges humanity has faced recently, forcing a change in the daily lives of billions of people worldwide. Therefore, many efforts have been made by researchers across the globe in the attempt of determining the models of COVID-19 spread. The objectives of this review are to analyze some of the open-access datasets mostly used in research in the field of COVID-19 regression modeling as well as present current literature based on Artificial Intelligence (AI) methods for regression tasks, like disease spread. Moreover, we discuss the applicability of Machine Learning (ML) and Evolutionary Computing (EC) methods that have focused on regressing epidemiology curves of COVID-19, and provide an overview of the usefulness of existing models in specific areas. An electronic literature search of the various databases was conducted to develop a comprehensive review of the latest AI-based approaches for modeling the spread of COVID-19. Finally, a conclusion is drawn from the observation of reviewed papers that AI-based algorithms have a clear application in COVID-19 epidemiological spread modeling and may be a crucial tool in the combat against coming pandemics.

Comparative Study of the Antimicrobial Activity of Selenium Nanoparticles With Different Surface Chemistry and Structure

Although selenium nanoparticles (SeNPs) have gained attention in the scientific community mostly through investigation of their anticancer activity, a great potential of this nanomaterial was recognized recently regarding its antimicrobial activity. The particle form, size, and surface chemistry have been recognized as crucial parameters determining the interaction of nanomaterials with biological entities. Furthermore, considering a narrow boundary between beneficial and toxic effects for selenium per se, it is clear that investigations of biomedical applications of SeNPs are very demanding and must be done with great precautions. The goal of this work is to evaluate the effects of SeNPs surface chemistry and structure on antimicrobial activity against several common bacterial strains, including Staphylococcus aureus (ATCC 6538), Enterococcus faecalis (ATCC 29212), Bacillus subtilis (ATCC 6633), and Kocuria rhizophila (ATCC 9341), as well as Escherichia coli (ATCC 8739), Salmonella Abony (NCTC 6017), Klebsiella pneumoniae (NCIMB 9111) and Pseudomonas aeruginosa (ATCC 9027), and the standard yeast strain Candida albicans (ATCC 10231). Three types of SeNPs were synthesized by chemical reduction approach using different stabilizers and reducing agents: (i) bovine serum albumin (BSA) + ascorbic acid, (ii) chitosan + ascorbic acid, and (iii) with glucose. A thorough physicochemical characterization of the obtained SeNPs was performed to determine the effects of varying synthesis parameters on their morphology, size, structure, and surface chemistry. All SeNPs were amorphous, with spherical morphology and size in the range 70–300 nm. However, the SeNPs obtained under different synthesis conditions, i.e. by using different stabilizers as well as reducing agents, exhibited different antimicrobial activity as well as cytotoxicity which are crucial for their applications. In this paper, the antimicrobial screening of the selected systems is presented, which was determined by the broth microdilution method, and inhibitory influence on the production of monomicrobial and dual-species biofilm was evaluated. The potential mechanism of action of different systems is proposed. Additionally, the cytotoxicity of SeNPs was examined on the MRC-5 cell line, in the same concentration interval as for antimicrobial testing. It was shown that formulation SeNPs-BSA expressed a significantly lower cytotoxic effect than the other two formulations.

Estimation of COVID-19 Epidemiology Curve of the United States Using Genetic Programming Algorithm

Estimation of the epidemiology curve for the COVID-19 pandemic can be a very computationally challenging task. Thus far, there have been some implementations of artificial intelligence (AI) methods applied to develop epidemiology curve for a specific country. However, most applied AI methods generated models that are almost impossible to translate into a mathematical equation. In this paper, the AI method called genetic programming (GP) algorithm is utilized to develop a symbolic expression (mathematical equation) which can be used for the estimation of the epidemiology curve for the entire U.S. with high accuracy. The GP algorithm is utilized on the publicly available dataset that contains the number of confirmed, deceased and recovered patients for each U.S. state to obtain the symbolic expression for the estimation of the number of the aforementioned patient groups. The dataset consists of the latitude and longitude of the central location for each state and the number of patients in each of the goal groups for each day in the period of 22nd January 2020-3rd December 2020. The obtained symbolic expressions for each state are summed up to obtain symbolic expressions for estimation of each of the patient groups (confirmed, deceased and recovered). These symbolic expressions are combined to obtain the symbolic expression for the estimation of the epidemiology curve for the entire U.S. The obtained symbolic expressions for the estimation of the number of confirmed, deceased and recovered patients for each state achieved R2 score in the ranges 0.9406-0.9992, 0.9404-0.9998 and 0.9797-0.99955, respectively. These equations are summed up to formulate symbolic expressions for the estimation of the number of confirmed, deceased and recovered patients for the entire U.S. with achieved R2 score of 0.9992, 0.9997 and 0.9996, respectively. Using these symbolic expressions, the equation for the estimation of the epidemiology curve for the entire U.S. is formulated which achieved R2 score of 0.9933. Investigation showed that GP algorithm can produce symbolic expressions for the estimation of the number of confirmed, recovered and deceased patients as well as the epidemiology curve not only for the states but for the entire U.S. with very high accuracy.

Automatic Evaluation of the Lung Condition of COVID-19 Patients Using X-ray Images and Convolutional Neural Networks

COVID-19 represents one of the greatest challenges in modern history. Its impact is most noticeable in the health care system, mostly due to the accelerated and increased influx of patients with a more severe clinical picture. These facts are increasing the pressure on health systems. For this reason, the aim is to automate the process of diagnosis and treatment. The research presented in this article conducted an examination of the possibility of classifying the clinical picture of a patient using X-ray images and convolutional neural networks. The research was conducted on the dataset of 185 images that consists of four classes. Due to a lower amount of images, a data augmentation procedure was performed. In order to define the CNN architecture with highest classification performances, multiple CNNs were designed. Results show that the best classification performances can be achieved if ResNet152 is used. This CNN has achieved AUCmacro¯ and AUCmicro¯ up to 0.94, suggesting the possibility of applying CNN to the classification of the clinical picture of COVID-19 patients using an X-ray image of the lungs. When higher layers are frozen during the training procedure, higher AUCmacro¯ and AUCmicro¯ values are achieved. If ResNet152 is utilized, AUCmacro¯ and AUCmicro¯ values up to 0.96 are achieved if all layers except the last 12 are frozen during the training procedure.

2D and 3D silver-based coordination polymers with thiomorpholine-4-carbonitrile and piperazine-1,4-dicarbonitrile: structure, intermolecular interactions, photocatalysis, and thermal behavior

The reaction of the thiomorpholine-4-carbonitrile and piperazine-1,4-dicarbonitrile ligands afforded four Ag(i) coordination polymers with excellent photocatalytic activity in the degradation of the mordant blue 9 dye.

Methoxy-Substituted Hydroxychalcone Reduces Biofilm Production, Adhesion and Surface Motility of Acinetobacter baumannii by Inhibiting ompA Gene Expression

An increasing lack of available therapeutic options against Acinetobacter baumannii urged researchers to seek alternative ways to fight this extremely resistant nosocomial pathogen. Targeting its virulence appears to be a promising strategy, as it offers considerably reduced selection of resistant mutants. In this study, we tested antibiofilm potential of four synthetic chalcone derivatives against A. baumannii. Compound that showed the greatest activity was selected for further evaluation of its antivirulence properties. Real-time PCR was used to evaluate mRNA expression of biofilm-associated virulence factor genes (ompA, bap, abaI) in treated A. baumannii strains. Also, we examined virulence properties related to the expression of these genes, such as fibronectin- and collagen-mediated adhesion, surface motility, and quorum-sensing activity. The results revealed that the expression of all tested genes is downregulated together with the reduction of adhesion and motility. The conclusion is that 2'-hydroxy-2-methoxychalcone exhibits antivirulence activity against A. baumannii by inhibiting the expression of ompA and bap genes, which is reflected in reduced biofilm formation, adhesion, and surface motility.

A comparison of classifiers in biomedical signal processing as a decision support system in disc hernia diagnosis

The aim of this research was to investigate the best methodology for disc hernia diagnosis using foot force measurements from the designed platform. Based on the subjective neurological examination that examines muscle weakness on the nerve endings of the skin area on feet and concludes about origins of nerve roots between spine discs, a platform for objective recordings of the aforementioned muscle weakness has been designed. The dataset included 33 patients with pre-diagnosed L4/L5 and L5/S1 disc hernia on the left or the right side, confirmed with the MRI scanning and neurological exam. We have implemented 5 different classifiers that were found to be the most suitable for smaller dataset and investigated the accuracy of classification depending on the normalization method, linearity/non-linearity of the algorithm, and dataset splitting variation (32-1, 31-2, 30-3, 29-4 patients for training and testing, respectively). The classifier is able to distinguish between four different diagnoses L4/L5 on the left side, L4/L5 on the right side, L5/S1 on the left side and L5/S1 on the right side, as well as to recognize healthy subjects (without disc herniation). The results show that non-linear algorithms achieved better accuracy in comparison to tested linear classifiers, suggesting the expected non-linear connection between the foot force values and the level of disc herniation. Two algorithms with highest accuracy turned out to be Decision Tree and Naïve Bayes, depending on the normalization method. The system is also able to record and recognize improvements in muscle weakness after surgical operation and physical therapy.

Gadolinium-Labelled Cell Scaffolds to Follow-up Cell Transplantation by Magnetic Resonance Imaging

Cell scaffolds are often used in cell transplantation as they provide a solid structural support to implanted cells and can be bioengineered to mimic the native extracellular matrix. Gadolinium fluoride nanoparticles (Gd-NPs) as a contrast agent for Magnetic Resonance Imaging (MRI) were incorporated into poly(lactide-co-glycolide)/chitosan scaffolds to obtain Imaging Labelled Cell Scaffolds (ILCSs), having the shape of hollow spherical/ellipsoidal particles (200-600 μm diameter and 50-80 μm shell thickness). While Gd-NPs incorporated into microparticles do not provide any contrast enhancement in T1-weighted (T1w) MR images, ILCSs can release Gd-NPs in a controlled manner, thus activating MRI contrast. ILCSs seeded with human mesenchymal stromal cells (hMSCs) were xenografted subcutaneously into either immunocompromised and immunocompetent mice without any immunosuppressant treatments, and the transplants were followed-up in vivo by MRI for 18 days. Immunocompromised mice showed a progressive activation of MRI contrast within the implants due to the release of Gd-NPs in the extracellular matrix. Instead, immunocompetent mice showed poor activation of MRI contrast due to the encapsulation of ILCSs within fibrotic capsules and to the scavenging of released Gd-NPs by phagocytic cells. In conclusion, the MRI follow-up of cell xenografts can report the host cell response to the xenograft. However, it does not strictly report on the viability of transplanted hMSCs.

Comparison of Surgisis, Vypro II and TiMesh in contaminated and clean field

PURPOSE

The study aimed to evaluate the histologic properties and infection resistance of three different mesh materials in a rat model.

METHODS

Each mesh, in both infectious (n = 96) and non-infectious groups (n = 270), was positioned both in sublay (preperitoneally) and onlay (subcutaneously) locations. Properties of the biological (Surgisis; Cook Surgical), composite, partially resorbing (Vypro II mesh; Ethicon) and non-resorbing (TiMesh; GFE Medizintechnik GmbH) mesh were evaluated and compared. Animals were killed at 7, 21 and 90 days after implantation. The following parameters were evaluated to assess the host response to the mesh material: inflammation, vascularization, fibrosis, collagen formation, Ki67, and a foreign body reaction by granuloma formation (FBG).

RESULTS

Surgisis mesh produced more pronounced inflammation and cell proliferation, and less intense granuloma formation, as well as fibrosis, compared to the other two groups. When the infected materials were examined, we found signs of local infection to be more often present in Surgisis group of animals.

CONCLUSIONS

In the presence of bacterial contamination, no benefits were observed in the use of the Surgisis prosthesis over the use of TiMesh and Vypro II.

Smart platform for the analysis of cupula deformation caused by otoconia presence within SCCs

In this paper, we first briefly describe the mechanical model of cupula deformation with the appropriate analytical solution. Then, we present the numerical solution of the same problem and compare it with the analytical one. Besides, we provide another numerical solution based on the Finite Element Method procedure, in an effort to encompass a more realistic approach to the problem such as considering the real geometry of the SCCs and the obstruction of the fluid flow during head movement due to the presence of otoconia. As a result, we obtain fifty solutions for a head rotation angle in a range from 0° to 120°, taking into account that such a manoeuvre lasts exactly 3 seconds. In the end, we present a mobile client-server application for visualising the finite element solutions in a way that is convenient for the clinical practice.

Exopolysaccharide Produced by Probiotic Strain Lactobacillus paraplantarum BGCG11 Reduces Inflammatory Hyperalgesia in Rats

The aim of this study was to test the potential of high molecular weight exopolysaccharide (EPS) produced by the putative probiotic strain Lactobacillus paraplantarum BGCG11 (EPS CG11) to alleviate inflammatory pain in Wistar rats. The EPS CG11 was isolated from bacterial surface and was subjected to Fourier-transform infrared spectroscopy (FTIR) and thermal analysis. FTIR spectra confirmed the polysaccharide structure of isolated sample, while the thermal methods revealed good thermal properties of the polymer. The antihyperalgesic and antiedematous effects of the EPS CG11 were examined in the rat model of inflammation induced by carrageenan injection in hind paw. The results showed that the intraperitoneal administration of EPS CG11 produced a significant decrease in pain sensations (mechanical hyperalgesia) and a paw swelling in a dose-dependent manner as it was measured using Von Frey anesthesiometer and plethysmometer, respectively. These effects were followed by a decreased expression of IL-1β and iNOS mRNAs in rat's paw tissue suggesting that the antihyperalgesic and antiedematous effects of the EPS CG11 are related to the suppression of inflammatory response. Additionally, we demonstrated that EPS CG11 exhibits immunosuppressive properties in the peritonitis model induced by carrageenan. Expression levels of pro-inflammatory mediators IL-1β, TNF-α and iNOS were decreased, together with the enhanced secretion of anti-inflammatory IL-10 and IL-6 cytokines, while neutrophil infiltration was not changed. To the best of our knowledge, this is the first study which reports an antihyperalgesic effect as the novel property of bacterial EPSs. Given the high demands of pharmaceutical industry for the replacement of commonly used analgesics due to numerous side effects, this study describes a promising natural compound for the future pharmacological testing in the area.

The influence of sex and gonadectomy on hepatic and brain fatty acid composition, lipogenesis and β-oxidation

The aim of this study was to investigate the influence of sex and castration of rats on liver and brain fatty acid profile and liver mRNA expression of genes involved in lipogenesis and β-oxidation. Castration significantly increased body weight and liver index and decreased serum triglyceride content in the female rats. The fatty acid composition of the liver tissue was influenced by sex and castration. Male rats had higher content of C16:0, C18:1n7, C18:2n6 and C22:5n3, while female rats had higher content of C18:0, C20:4n6 and C22:6n3. Castration of male rats decreased differences caused by sex for C18:2n6, C20:4n6 and C22:6n3. Values for C16:1n7 were higher in the castrated male rats in comparison with all other groups. Liver phospholipids showed a distribution of fatty acids similar to the total lipids. Brain total lipids and phospholipids were not influenced by sex or castration. Castration increased ∆6D gene expression in both the sexes, while ∆5D and ∆9D increased in females and males respectively. Gonadectomy increased expression of the FASN gene in the females and decreased CPT1 and ACOX1 gene expression in the liver tissue of male rats. The observed results of lipid peroxidation, measured by TBARS, were the lowest in the intact females in comparison with all other groups. In conclusion, sex strongly influences both SFA and PUFA in liver tissue, and castration decreases these differences only for PUFA. Castration also influences the expression of the genes involved in lipid metabolism differently in male and female rats, with an increase in lipogenic genes in female rats and a decrease in key genes for mitochondrial and peroxisomal β-oxidation in male rats.

45S5Bioglass®-based scaffolds coated with selenium nanoparticles or with poly(lactide-co-glycolide)/selenium particles: Processing, evaluation and antibacterial activity

In the bone tissue engineering field, there is a growing interest in the application of bioactive glass scaffolds (45S5Bioglass(®)) due to their bone bonding ability, osteoconductivity and osteoinductivity. However, such scaffolds still lack some of the required functionalities to enable the successful formation of new bone, e.g. effective antibacterial properties. A large number of studies suggest that selenium (Se) has significant role in antioxidant protection, enhanced immune surveillance and modulation of cell proliferation. Selenium nanoparticles (SeNp) have also been reported to possess antibacterial as well as antiviral activities. In this investigation, uniform, stable, amorphous SeNp have been synthesized and additionally immobilized within spherical PLGA particles (PLGA/SeNp). These particles were used to coat bioactive glass-based scaffolds synthesized by the foam replica method. Samples were characterized by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS) and transmission electron microscopy (TEM). SeNp, 45S5Bioglass(®)/SeNp and 45S5Bioglass(®)/PLGA/SeNp showed a considerable antibacterial activity against Gram positive bacteria, Staphylococcus aureus and Staphylococcus epidermidis, one of the main causative agents of orthopedic infections. The functionalized Se-coated bioactive glass scaffolds represent a new family of bioactive, antibacterial scaffolds for bone tissue engineering applications.

Prediction of coronary plaque location on arteries having myocardial bridge, using finite element models

This study was performed to evaluate the influences of the myocardial bridges on the plaque initializations and progression in the coronary arteries. The wall structure is changed due to the plaque presence, which could be the reason for multiple heart malfunctions. Using simplified parametric finite element model (FE model) of the coronary artery having myocardial bridge and analyzing different mechanical parameters from blood circulation through the artery (wall shear stress, oscillatory shear index, residence time), we investigated the prediction of "the best" position for plaque progression. We chose six patients from the angiography records and used data from DICOM images to generate FE models with our software tools for FE preprocessing, solving and post-processing. We found a good correlation between real positions of the plaque and the ones that we predicted to develop at the proximal part of the myocardial bridges with wall shear stress, oscillatory shear index and residence time. This computer model could be additional predictive tool for everyday clinical examination of the patient with myocardial bridge.