Introduction:
Mesenchymal stromal/stem cells (MSCs) possess unique biological properties, including self-renewal, differentiation, and secretory potentials. However, a standard 2D culture does not replicate MSCs' natural microenvironment, compromising their features. Engineering MSC-based constructs that support various 3D cell organizations and analyzing cell behavior under such conditions are crucial for biomedical applications, offering relevant model systems and aiding in the development of therapeutic agents. This study aimed to evaluate the impact of cultivating MSCs in spheroids, alginate microspheres (AMSs), and blood plasma scaffolds on viability and metabolic and functional activity.

Methods:
Human adipose tissue-derived MSCs (obtained with adult donors’ informed consent) were used. Spheroids were formed by the “hanging drop” method. AMSs were generated by electrospraying MSCs dispersed in 2% sodium alginate into 2% calcium chloride. Scaffolds were prepared through cryogelation and being seeded with cells. All constructs were cultured at 37 °C, 5% CO2, and 95% humidity in alpha-MEM supplemented with 10% fetal bovine serum, 50 μg/ml penicillin, and 50 μg/ml streptomycin. Viability (6-CFDA), metabolic activity (resazurin test), actin filaments (Phalloidin-FITC), cell spreading, and induced differentiation were examined.

Results and Discussion:
MSCs exhibited high viability in all constructs but displayed distinct morphologies (spindle-like in scaffolds, round in spheroids, and AMSs). Actin filament development was most pronounced in cells within scaffolds. Metabolic activity was reduced in spheroids and AMSs compared to the scaffolds. All groups demonstrated the ability for induced differentiation.

Conclusions:
The cultivation of MSCs within a macroporous adhesive scaffold promotes fibroblast-like morphology and high metabolic activity. A spheroid and AMS culture results in round-shaped cells with lower metabolic activity, which can reflect a natural-like quiescence state. This study highlights the importance of 3D culture systems in maintaining MSC properties and suggests that constructs' design significantly influences cell functionality, crucial for advancing biomedical applications and therapeutic strategies.

This study was supported by the National Research Foundation of Ukraine (project № 2021.01/0276).

Introduction.

Serotonin is a neurotransmitter that participates in the homeostasis of many physiological functions in humans. The study and understanding of its cellular biology are of special relevance in advancing knowledge of the different actions and biosignal processes of this biological amine. It accumulates in intracellular vesicles and is released by exocytosis. Over 90% of blood serotonin is accumulated in platelets, where the highest content is stored in the so-called delta granules.

We are carrying out a quantitative study of the release, by exocytosis, of serotonin in platelets using amperometry with multi-electrode array (MEA) systems. Each exocytotic event is recorded as an amperometric deflection called a “spike”.

Methods.

We fabricate and optimize novel boron-doped nanocrystalline diamond 16-microelectrode array devices (16-BBD-MEA): opaque devices on silicon substrates (BDD-on-silicon MEA)¹ and transparent devices on quartz substrates (BDD-on-quartz MEA)².

With these 16-BBD-MEA systems, we record the amperometric spikes. The measurements are carried out under two different conditions: basal (without the modification of isolated platelets) and after loading the platelets with10 µM serotonin for 2 h.^1,2

Results.

MEA systems are a solid tool for studying exocytosis in human platelets. This is important as platelets are one of few easily accessible human cells. From each spike, we calculate four kinetic parameters: Imax (maximum oxidation current, in pA), t_1/2 (spike width at half maximum, in ms), Q (spike net charge, in pQ) and the ascending slope of the spike (in pA/ms). These parameters permit us to characterize and classify the secretory spikes.^1,2

Conclusions.

It is possible to quantitatively determine the release of serotonin by exocytosis from human platelets using BDD-MEA devices.

References.

¹ González Brito, R; Montenegro, P; Méndez, A; Carabelli, V; Tomagra, G; Shabgahi, R.E.; Pasquarelli, A.; Borges, R. Biosensors2023, 13, 86.

² González Brito, R; Montenegro, P; Méndez, A; Shabgahi, R.E.; Pasquarelli, A.; Borges, R. Biosensors2024, 14, 75.

Introduction: The contractile dynamics of peripheral muscles are governed by complex recruitment and relaxation strategies optimized by the central nervous system. These dynamics aim to maximize the efficiency of resulting work and are finely regulated by synergies, intermuscular coordination, and sensory feedback mechanisms. When individuals are affected by injuries, trauma, cognitive impairments, or neurodegenerative diseases, among others, such contractile dynamics are altered and often manifested in the musculature through changes in the frequency content of electromyographic (EMG) signals. During rapid contractions, these changes are challenging to study and detect because the time series comprising the EMG exhibit highly non-stationary processes.

Methods: Here, we have proposed an exploratory analysis of the time–frequency characteristics of EMG signals using three different approaches: spectrograms (SPs), Hilbert transform (HT), and empirical mode decomposition. Specifically, for empirical mode decomposition, we employed the noise-assisted multivariate empirical mode decomposition (NA-MEMD). These methodologies were applied to EMG signals obtained from a Parkinson's disease (PD) lesion model to longitudinally study the muscle function alterations.

Results and Discussion: These approaches allowed for determining and characterizing the contraction phases of the biceps femoris muscle in a free movement protocol. The SP of the EMG revealed changes in frequency content in the initial phase of contraction, depending on the progression of the injury. These initial observations were made under certain limitations of time–frequency resolution. The HT revealed subphases at the onset of contraction with significant differences in the frequency content of the EMG signals obtained across different stages of injury progression. Finally, the NA-MEMD of the signals revealed intrinsic mode functions primarily affected by anatomical–functional changes in the animal model over time.

Conclusions: This study allowed for extracting spectral information contained in non-stationary segments of the EMG, thus characterizing changes in contractile dynamics caused by progressive functional alterations in the animal model of PD.

Composites are restorative materials that have evolved in recent years, becoming part of a new group of materials known as bioactive materials. These materials generate an ionic exchange with dental structure, promoting remineralization and preventing bacterial activity. This study aimed to determine the bacterial inhibition of different restorative composites on S. mutans group.

Field strains of S. mutans were used and isolated in our laboratory (LABOFOUNT) and confirmed by reference strains ATCC. Nanohybrid Composite Filtek Z350 (3M ORAL CARE), Nanohybrid Composite Filtek Bulkfill (3M ORAL CARE) (traditional composites) and Alkasite N Cention Composite (Ivoclar Vivadent), and ACTIVA Bioactive Restorative Composite (Pulpdent) (bioactive composites) were evaluated. They were prepared according to the manufacturer's instructions, following standardized biosafety protocols. Test bodies were fabricated using Teflon molds, following laboratory protocols.

Two inhibition studies were performed at different times, in both cases, the samples were studied in triplicate. 50 μl of S. mutans inoculum at 0.5 McFarland scale was seeded in SB20M medium, circular perforations of 4mm in diameter and 2mm in depth were made for the placement of composites and controls (negative: 0.12% chlorhexidine digluconate and positive: sterile distilled water), incubated at 37°C in a candle jar for 48 hours. Subsequently, the inhibition halos were measured with a digital caliper under a stereoscopic magnifying glass.

Inhibition halos of 18,2 mm and 18,4 mm average values corresponding to the two studies were measured for the negative control, while values of 6,9 and 7,1 mm were obtained for Alkasite N. Cention Composite. No inhibition halos could be determined for the other materials.

In this study, only Alkasite N Cention Composite inhibited S. mutans bacterial growth. It is suggested to conduct inhibition studies in relation to the minimum inhibitory concentration (MIC) and colony-forming unit (CFU) count to further evaluate the effect of bioactive composites on S. mutans.

Introduction: The EMG signal is the electrical manifestation of motor unit (MU) recruitment processes underlying the contractile dynamics of muscle fibers. The analysis methodology frequently carried out includes a preprocessing stage based on artifact removal and stationarity testing, as well as a feature extraction and interpretation stage. Generally, stationarity criteria are difficult to meet when EMG signals are evoked by momentary activations (bursting activity). Thus, the study and/or characterization of contractile patterns evoked in free-moving protocols require particular treatments.

Methods: Here, we propose a new approach for quantitatively measuring stationarity using the mean, variance, and autocovariance test (MVA test) and Permutation Entropy for measuring the uncertainty degree. This methodology was applied to EMG signals obtained from a Parkinson's disease (PD) lesion model to longitudinally study the muscle function alterations.

Results and Discussion: The MVA test was compared with the classic Reverse Arrangement test (RA-test). The RA test indicated that EMG signals become more stationary over post-injury time. However, the MVA test revealed that the temporal structure of EMG around the maximum recruitment zone of motor units presents incremental non-stationary characteristics (in variance and autocovariance) over post-injury time. Likewise, it was observed that the initial phase of motor recruitment in the biceps femoris (BF) muscle (around the onset) presents a high non-stationary component, which increases over post-injury time. Permutation entropy measures throughout the contractile dynamics of the BF muscle revealed that the uncertainty degree decreases in the initial phase of contraction as the animal's post-injury time increases.

Conclusions: The analysis proposed allowed for a longitudinal characterization of muscle function alterations in an animal model of PD in terms of the stationarity properties of EMG signals. Furthermore, it was observed that permutation entropy could serve as a robust biomarker for quantifying neuromuscular remodeling caused by PD progression.

Scoliosis is a three-dimensional deformity of the trunk and spine that can develop significantly during growth stages. Recently, computer-aided design has played a crucial role in various physical rehabilitation and orthotic applications. This study focuses on designing medical orthotics using 3D modelling and force simulation techniques, aiming to improve the accuracy and effectiveness of scoliosis treatments.

The approach starts by reconstructing a 3D model of the scoliosis case using computed tomography (CT) data and then employs Solidworks software to analyse and simulate the mechanical properties. The numerical simulation of applying different pressure points with varying values generates a comprehensive dataset of curves that express the spine's deformity, simulating correction using different braces with distinct pressure points in Solidworks. By selecting the optimal pressure points in various planes, we were able to manufacture a precise 3D model that addresses scoliosis based on pre-examined pressure points.

This study demonstrates that combining engineering and medical technologies can significantly enhance the quality of treatment and effectively meet patients' needs. Furthermore, advancements in 3D printing technology enable the production of highly accurate and customized orthotic devices, ensuring a perfect fit for each patient's unique anatomical structure. The ability to rapidly prototype and adjust designs in real time significantly reduces the time and cost associated with traditional orthotic manufacturing methods. Moreover, the integration of deep artificial neural networks could further refine the design process, enabling the reconstruction of appropriate scoliosis braces based on extensive data analysis and predictive modeling.

Biopolymer-based scaffolds have emerged as therapeutic solutions, supporting tissue regeneration and accelerating wound healing. The clinical translation of scaffolds remains challenging due to the complex nature of wound healing. Understanding intricate cell--material interactions is crucial for designing tailored scaffolds. The ideal scaffold requires a strategic balance of physico-chemical properties that influence the scaffold’s biological performance. Machine learning (ML) and deep learning (DL) have transformed tissue engineering by enabling the prediction of tissue outcomes in complex biological settings. This study applies ML and DL to model the relationship between scaffold properties and cell response, outlining the necessary scaffold requirements for different cell lines to provide design insights and predict scaffold performance.

Classification models were developed to predict the miscibility of polymer blends used to engineer scaffolds. Physico-chemical features of polymer blends were used as input data. Regression models were developed to predict cell--material interactions during the inflammation and proliferation phases of the wound healing process, using scaffold physico-chemical characteristics and in vitro cell culture data. Macrophage cell features were extracted from scanning electron microscope (SEM) images and classified by phenotypes. Pre-trained DL convolution neural network (CNN) models were applied and fine-tuned for cell-image classification.

Key physico-chemical parameters influencing the miscibility of polymer blends and cell responses were identified using random forest models, achieving validation accuracies between 63% and 96%. Fiber diameter and pore diameter were the most relevant parameters impacting cell responses on scaffolds. Two pre-trained DL models indicated that CNNs effectively classify macrophage cells from SEM images based on phenotypes, independently of other physico-chemical features (validation accuracies between 83% and 91%).

Polymer blends influence scaffold properties, which dictate cell--material interactions. Predictive modelling highlighted fibre diameter and pore diameter as crucial for directing cell growth and penetration in scaffolds. For proper wound healing, inflammation and proliferation phases are critical. Thus, predicting specific cell--scaffold interactions can facilitate appropriate therapy.

Introduction: SARS-CoV-2, a virus responsible for the emergence of the life-threatening disease known as COVID-19, exhibits a diverse range of clinical manifestations. The spectrum of symptoms varies widely, encompassing mild to severe presentations, while a considerable portion of the population remains asymptomatic. COVID-19, primarily a respiratory virus, has been linked to cardiovascular complications in some patients. Notably, cardiac issues can also arise after recovery, contributing to post-acute COVID-19 syndrome, a significant concern for patient health. The present study intends to evaluate the post-acute COVID-19 syndrome cardiovascular effect through ECG by comparing patients affected with cardiac diseases without COVID-19 diagnosis report (class 1) and patients with cardiac pathologies who present post-acute COVID-19 syndrome (class 2).

Methods: From 2 body positions, a total of 10 non-linear features, extracted every 1 second under a multi-band analysis performed by Discrete Wavelet Transform (DWT), have been compressed by 6 statistical metrics to serve as inputs for an individual feature analysis by the means of Mann-Whitney U-test and XROC classification.

Results and Discussion: 480 Mann-Whitney U-test statistical analyses and XROC discrimination approaches have been done. The percentage of statistical analysis with significant differences (p<0.05) was 30.42% (146 out of 480). The best overall results were obtained by approximating the feature Energy, with the data compressor Kurtosis in the body position Down. Those results were 83.33% of Accuracy, 83.33% of Sensitivity, 83.33% of Specificity and 87.50% of AUC.

Conclusions: The results show that the applied methodology can be a way to show changes in cardiac behaviour provoked by post-acute COVID-19 syndrome.

Introduction

There is a worldwide need to develop novel and sustainable ways to tackle food safety and disease resistance in agriculture. Plants are conventionally treated with a large quantity of pesticides, with delivery being inefficient. The use of nanomaterials for disease control can be a more efficient and target-specific strategy. Copper, one of the eight essential plant micronutrients, provides protection against diseases such as ergot and bacterial wilt. By using nanoscale carriers or nanoscale variants of copper-based particles, one could potentially decrease disease proliferation, and reduce the environmental and public health impact of pesticides.

Methods

Water-dispersible copper-based nanoparticles were synthesized using a simple one-pot chemical precipitation method with various ligands used to coat and stabilize the nanomaterials. All particles were characterized using standard techniques such as TEM, DLS, and FTIR. An extensive greenhouse trial was performed on tomato plants with half of the plants inoculated with Ralstonia solanacearum and the rest used as control. The application of nanoparticles was carried out via both foliar spray (1 mg/plant) and soil (200 mg NP/kg soil). Parameters such as plant height, leaf count, and number of flowers/fruits as well as signs of wilting were recorded regularly. Root and shoot biomass and metal contents were determined to evaluate the effectiveness of the NPs.

Results and Discussion

The nanoparticle sizes ranged from 40 to 500 nm, depending on stabilizing ligand and reaction protocol. Copper content varied between 20 and 60% (EDX elemental analysis). In the greenhouse trials, it was observed that there was a size dependence on resistance to bacterial wilt, with smaller nanoparticles leading to higher resistance.

Conclusions

Overall, our data showed that copper-based nanoparticles can be useful in providing resistance to bacterial wilt in tomato plants. In vitro testing on mammalian cell lines (L929, A549, and PC12) is under way to establish nanoparticle toxicity.

Introduction: The World Health Organization has raised the alarm concerning the burden of cutaneous leishmaniasis (CL), which mainly affects poor marginalized communities globally, causing non-healing ulcerated wounds that leave stigmatizing scars. Secondary bacterial infections highly hinder the healing process, making patients more vulnerable to other diseases. This study aims to develop wound dressings that accelerate healing while preventing secondary infections using sugarcane bagasse lignocellulosic biomass, which is abundant in Mauritius. This approach shifts the focus from merely eradicating Leishmania parasites to improving wound healing in CL patients.

Methods: Cellulose and lignin were extracted using a single cost-effective protocol, blended in varying ratios and chemically crosslinked to form soft hydrogels (Cel-lig). Their physicochemical properties, biocompatibility with murine cells and antibacterial activity were evaluated. The optimal hydrogel was then loaded with eugenol (EUG) and berberine (BER), respectively, to enhance its biological activities. The in vitro biocompatibility of the loaded scaffolds was tested with mouse fibroblasts (L929) and macrophages (RAW 264.7), and the production of a pro-inflammatory cytokine (TNF-α) was determined. Additionally, the antibacterial activity of the loaded hydrogels was tested against a representative of the common bacteria isolated from CL wounds.

Results and Discussion: The Cel-lig 70:30 hydrogel possessed adequate surface stiffness for the adhesion and migration of the L929 cells in vitro and antibacterial activity owing to lignin’s intrinsic antimicrobial properties. Loading of EUG and BER into Cel-lig 70:30 did not alter the hydrogel’s physicochemical characteristics. The hydrogels did not trigger high production levels of TNF-α from the RAW 264.7 macrophages and showcased significant antibacterial activity against Gram-positive and Gram-negative bacteria, with the 24-hour growth reduction percentages ranging from 61 to 79%.

Conclusion: Among all the hydrogels tested, the Cel-lig 70:30 hydrogels loaded with eugenol and berberine, respectively, demonstrated the best attributes for advanced testing and for conversion into a wound patch for direct use on CL wounds.