Decellularized kidney extracellular matrix-based hydrogels for renal tissue engineering.

Kidney regeneration is hindered by the limited pool of intrinsic reparative cells. Advanced therapies targeting renal regeneration have the potential to alleviate the clinical and financial burdens associated with kidney disease. Delivery systems for cells, extracellular vesicles, or growth factors aimed at enhancing regeneration can benefit from vehicles enabling targeted delivery and controlled release. Hydrogels, optimized to carry biological cargo while promoting regeneration, have emerged as promising candidates for this purpose. This study aims to develop a hydrogel from decellularized kidney extracellular matrix (DKECM) and explore its biocompatibility as a biomaterial for renal regeneration. The resulting hydrogel crosslinks with temperature and exhibits a high concentration of extracellular matrix. The decellularization process efficiently removes detergent residues, yielding a pathogen-free biomaterial that is non-hemolytic and devoid of α-gal epitope. Upon interaction with macrophages, the hydrogel induces differentiation into both pro-inflammatory and anti-inflammatory phenotypes, suggesting an adequate balance to promote biomaterial functionality in vivo. Renal progenitor cells encapsulated in the DKECM hydrogel demonstrate higher viability and proliferation than in commercial collagen-I hydrogels, while also expressing tubular cells and podocyte markers in long-term culture. Overall, the injectable biomaterial derived from porcine DKECM is anticipated to elicit minimal host reaction while fostering progenitor cell bioactivity, offering a potential avenue for enhancing renal regeneration in clinical settings. STATEMENT OF SIGNIFICANCE: The quest to improve treatments for kidney disease is crucial, given the challenges faced by patients on dialysis or waiting for transplants. Exciting new therapies combining biomaterials with cells can revolutionize kidney repair. In this study, researchers created a hydrogel from pig kidney. This gel could be used to deliver cells and other substances that help in kidney regeneration. Despite coming from pigs, it's safe for use in humans, with no harmful substances and reduced risk of immune reactions. Importantly, it promotes a balanced healing response in the body. This research not only advances our knowledge of kidney repair but also offers hope for more effective treatments for kidney diseases.

FSM-DDTR: End-to-end feedback strategy for multi-objective De Novo drug design using transformers.

The design of compounds that target specific biological functions with relevant selectivity is critical in the context of drug discovery, especially due to the polypharmacological nature of most existing drug molecules. In recent years, in silico-based methods combined with deep learning have shown promising results in the de novo drug design challenge, leading to potential leads for biologically interesting targets. However, several of these methods overlook the importance of certain properties, such as validity rate and target selectivity, or simplify the generative process by neglecting the multi-objective nature of the pharmacological space. In this study, we propose a multi-objective Transformer-based architecture to generate drug candidates with desired molecular properties and increased selectivity toward a specific biological target. The framework consists of a Transformer-Decoder Generator that generates novel and valid compounds in the SMILES format notation, a Transformer-Encoder Predictor that estimates the binding affinity toward the biological target, and a feedback loop combined with a multi-objective optimization strategy to rank the generated molecules and condition the generating distribution around the targeted properties. The results demonstrate that the proposed architecture can generate novel and synthesizable small compounds with desired pharmacological properties toward a biologically relevant target. The unbiased Transformer-based Generator achieved superior performance in the novelty rate (97.38%) and comparable performance in terms of internal diversity, uniqueness, and validity against state-of-the-art baselines. The optimization of the unbiased Transformer-based Generator resulted in the generation of molecules exhibiting high binding affinity toward the Adenosine A2A Receptor (AA2AR) and possessing desirable physicochemical properties, where 99.36% of the generated molecules follow Lipinski's rule of five. Furthermore, the implementation of a feedback strategy, in conjunction with a multi-objective algorithm, effectively shifted the distribution of the generated molecules toward optimal values of molecular weight, molecular lipophilicity, topological polar surface area, synthetic accessibility score, and quantitative estimate of drug-likeness, without the necessity of prior training sets comprising molecules endowed with pharmacological properties of interest. Overall, this research study validates the applicability of a Transformer-based architecture in the context of drug design, capable of exploring the vast chemical representation space to generate novel molecules with improved pharmacological properties and target selectivity. The data and source code used in this study are available at: https://github.com/larngroup/FSM-DDTR.

Applied genomics for identification of virulent biothreats and for disease outbreak surveillance.

Fortifying our preparedness to cope with biological threats by identifying and targeting virulence factors may be a preventative strategy for curtailing infectious disease outbreak. Virulence factors evoke successful pathogenic invasion, and the science and technology of genomics offers a way of identifying them, their agents and evolutionary ancestor. Genomics offers the possibility of deciphering if the release of a pathogen was intentional or natural by observing sequence and annotated data of the causative agent, and evidence of genetic engineering such as cloned vectors at restriction sites. However, to leverage and maximise the application of genomics to strengthen global interception system for real-time biothreat diagnostics, a complete genomic library of pathogenic and non-pathogenic agents will create a robust reference assembly that can be used to screen, characterise, track and trace new and existing strains. Encouraging ethical research sequencing pathogens found in animals and the environment, as well as creating a global space for collaboration will lead to effective global regulation and biosurveillance.

DTITR: End-to-end drug-target binding affinity prediction with transformers.

The accurate identification of Drug-Target Interactions (DTIs) remains a critical turning point in drug discovery and understanding of the binding process. Despite recent advances in computational solutions to overcome the challenges of in vitro and in vivo experiments, most of the proposed in silico-based methods still focus on binary classification, overlooking the importance of characterizing DTIs with unbiased binding strength values to properly distinguish primary interactions from those with off-targets. Moreover, several of these methods usually simplify the entire interaction mechanism, neglecting the joint contribution of the individual units of each binding component and the interacting substructures involved, and have yet to focus on more explainable and interpretable architectures. In this study, we propose an end-to-end Transformer-based architecture for predicting drug-target binding affinity (DTA) using 1D raw sequential and structural data to represent the proteins and compounds. This architecture exploits self-attention layers to capture the biological and chemical context of the proteins and compounds, respectively, and cross-attention layers to exchange information and capture the pharmacological context of the DTIs. The results show that the proposed architecture is effective in predicting DTA, achieving superior performance in both correctly predicting the value of interaction strength and being able to correctly discriminate the rank order of binding strength compared to state-of-the-art baselines. The combination of multiple Transformer-Encoders was found to result in robust and discriminative aggregate representations of the proteins and compounds for binding affinity prediction, in which the addition of a Cross-Attention Transformer-Encoder was identified as an important block for improving the discriminative power of these representations. Overall, this research study validates the applicability of an end-to-end Transformer-based architecture in the context of drug discovery, capable of self-providing different levels of potential DTI and prediction understanding due to the nature of the attention blocks. The data and source code used in this study are available at: https://github.com/larngroup/DTITR.

Biomimetic Surface Topography from the Rubus fruticosus Leaf as a Guidance of Angiogenesis in Tissue Engineering Applications.

The promotion of angiogenesis is a fundamental step for efficient organ/tissue reconstitution and replacement. Thus, several strategies to promote vascularization of scaffolds were studied to satisfy this unsolved clinical need. The interface between cells and substrates is a determinant for the success of tissue engineering (TE) strategies. Substrate's topography is reported to play a key role in influencing endothelial cell behavior, namely, on its proliferation, metabolic activity, morphology, migration, and secretion of cytokines and chemokines. Therefore, surface topography of the biomaterial-based grafts is a crucial property that is considered in the development of a new TE approach. Herein, we hypothesize that the surface of Rubus fruticosus leaf plays a crucial role in driving angiogenesis since its architecture resembles the vascular structures at a biologically relevant size scale. For this, we produced biomimetic polycaprolactone (PCL) membranes (BpMs) replicating the surface topography of a R. fruticosus leaf by replica molding and nanoimprint lithography. Our results showed an enhanced performance in terms of proliferation of the human endothelial cell line on top of the BpM. Moreover, an asymmetric cellular spatial distribution among the surface of the BpM was observed. These cells seem to have higher density for longer time periods in the region that replicates the leaf veins. Finally, we assess the angiogenic capacity through a chick chorioallantoic membrane assay, revealing that BpMs are more prone to support angiogenesis than flat PCL membranes. We strongly believe that this strategy can bring new insights into developing TE strategies with an enhanced performance in terms of the vascular integration between the host and the scaffolds implanted.

Designing optimized drug candidates with Generative Adversarial Network.

Drug design is an important area of study for pharmaceutical businesses. However, low efficacy, off-target delivery, time consumption, and high cost are challenges and can create barriers that impact this process. Deep Learning models are emerging as a promising solution to perform de novo drug design, i.e., to generate drug-like molecules tailored to specific needs. However, stereochemistry was not explicitly considered in the generated molecules, which is inevitable in targeted-oriented molecules. This paper proposes a framework based on Feedback Generative Adversarial Network (GAN) that includes optimization strategy by incorporating Encoder-Decoder, GAN, and Predictor deep models interconnected with a feedback loop. The Encoder-Decoder converts the string notations of molecules into latent space vectors, effectively creating a new type of molecular representation. At the same time, the GAN can learn and replicate the training data distribution and, therefore, generate new compounds. The feedback loop is designed to incorporate and evaluate the generated molecules according to the multiobjective desired property at every epoch of training to ensure a steady shift of the generated distribution towards the space of the targeted properties. Moreover, to develop a more precise set of molecules, we also incorporate a multiobjective optimization selection technique based on a non-dominated sorting genetic algorithm. The results demonstrate that the proposed framework can generate realistic, novel molecules that span the chemical space. The proposed Encoder-Decoder model correctly reconstructs 99% of the datasets, including stereochemical information. The model's ability to find uncharted regions of the chemical space was successfully shown by optimizing the unbiased GAN to generate molecules with a high binding affinity to the Kappa Opioid and Adenosine [Formula: see text] receptor. Furthermore, the generated compounds exhibit high internal and external diversity levels 0.88 and 0.94, respectively, and uniqueness.

Explainable deep drug-target representations for binding affinity prediction.

BACKGROUND: Several computational advances have been achieved in the drug discovery field, promoting the identification of novel drug-target interactions and new leads. However, most of these methodologies have been overlooking the importance of providing explanations to the decision-making process of deep learning architectures. In this research study, we explore the reliability of convolutional neural networks (CNNs) at identifying relevant regions for binding, specifically binding sites and motifs, and the significance of the deep representations extracted by providing explanations to the model's decisions based on the identification of the input regions that contributed the most to the prediction. We make use of an end-to-end deep learning architecture to predict binding affinity, where CNNs are exploited in their capacity to automatically identify and extract discriminating deep representations from 1D sequential and structural data. RESULTS: The results demonstrate the effectiveness of the deep representations extracted from CNNs in the prediction of drug-target interactions. CNNs were found to identify and extract features from regions relevant for the interaction, where the weight associated with these spots was in the range of those with the highest positive influence given by the CNNs in the prediction. The end-to-end deep learning model achieved the highest performance both in the prediction of the binding affinity and on the ability to correctly distinguish the interaction strength rank order when compared to baseline approaches. CONCLUSIONS: This research study validates the potential applicability of an end-to-end deep learning architecture in the context of drug discovery beyond the confined space of proteins and ligands with determined 3D structure. Furthermore, it shows the reliability of the deep representations extracted from the CNNs by providing explainability to the decision-making process.

Tlr2/4-Mediated Hyperinflammation Promotes Cherubism-Like Jawbone Expansion in Sh3bp2 (P416R) Knockin Mice.

Cherubism (CBM), characterized by expansile jawbones with multilocular fibrocystic lesions, is caused by gain-of-function mutations in SH3 domain-binding protein 2 (SH3BP2; mouse orthologue Sh3bp2). Loss of jawbone and dental integrity significantly decrease the quality of life for affected children. Treatment for CBM is limited to multiple surgeries to correct facial deformities. Despite significant advances made with CBM knockin (KI) mouse models (Sh3bp2 KI/KI ), the activation mechanisms of CBM lesions remain unknown because mutant mice do not spontaneously develop expansile jawbones. We hypothesize that bony inflammation of an unknown cause triggers jawbone expansion in CBM. To introduce jawbone inflammation in a spatiotemporally controlled manner, we exposed pulp of the first right mandibular molar of 6-week-old Sh3bp2 +/+ , Sh3bp2 KI/+ , and Sh3bp2 KI/KI mice. Bacterial invasion from the exposed pulp into root canals led to apical periodontitis in wild-type and mutant mice. The pathogen-associated molecular patterns (PAMPs)-induced inflammation of alveolar bone resulted in jawbone expansion in Sh3bp2 KI/+ and Sh3bp2 KI/KI mice. CBM-like lesions developed exacerbated inflammation with increased neutrophil, macrophage, and osteoclast numbers. These lesions displayed excessive neutrophil extracellular traps (NETs) compared to Sh3bp2 +/+ mice. Expression levels of IL-1ß, IL-6, and TNF-α were increased in periapical lesions of Sh3bp2 +/+ , Sh3bp2 KI/+ , and Sh3bp2 KI/KI mice and also in plasma and the left untreated mandibles (with no pulp exposure) of Sh3bp2 KI/KI mice, suggesting a systemic upregulation. Ablation of Tlr2/4 signaling or depletion of neutrophils by Ly6G antibodies ameliorated jawbone expansion induced by PAMPs in Sh3bp2 KI/KI mice. In summary, successful induction of CBM-like lesions in jaws of CBM mice is important for studying initiating mechanisms of CBM and for testing potential therapies. Our findings further emphasize a critical role of host immunity in the development of apical periodontitis and the importance of maintaining oral health in CBM patients. © 2021 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

Drug-Target Interaction Prediction: End-to-End Deep Learning Approach.

The discovery of potential Drug-Target Interactions (DTIs) is a determining step in the drug discovery and repositioning process, as the effectiveness of the currently available antibiotic treatment is declining. Although putting efforts on the traditional in vivo or in vitro methods, pharmaceutical financial investment has been reduced over the years. Therefore, establishing effective computational methods is decisive to find new leads in a reasonable amount of time. Successful approaches have been presented to solve this problem but seldom protein sequences and structured data are used together. In this paper, we present a deep learning architecture model, which exploits the particular ability of Convolutional Neural Networks (CNNs) to obtain 1D representations from protein sequences (amino acid sequence) and compounds SMILES (Simplified Molecular Input Line Entry System) strings. These representations can be interpreted as features that express local dependencies or patterns that can then be used in a Fully Connected Neural Network (FCNN), acting as a binary classifier. The results achieved demonstrate that using CNNs to obtain representations of the data, instead of the traditional descriptors, lead to improved performance. The proposed end-to-end deep learning method outperformed traditional machine learning approaches in the correct classification of both positive and negative interactions.

Engineering pericyte-supported microvascular capillaries in cell-laden hydrogels using stem cells from the bone marrow, dental pulp and dental apical papilla.

Engineered tissue constructs require the fabrication of highly perfusable and mature vascular networks for effective repair and regeneration. In tissue engineering, stem cells are widely employed to create mature vascularized tissues in vitro. Pericytes are key to the maturity of these vascular networks, and therefore the ability of stem cells to differentiate into pericyte-like lineages should be understood. To date, there is limited information regarding the ability of stem cells from the different tissue sources to differentiate into pericytes and form microvascular capillaries in vitro. Therefore, here we tested the ability of the stem cells derived from bone marrow (BMSC), dental pulp (DPSC) and dental apical papilla (SCAP) to engineer pericyte-supported vascular capillaries when encapsulated along with human umbilical vein endothelial cells (HUVECs) in gelatin methacrylate (GelMA) hydrogel. Our results show that the pericyte differentiation capacity of BMSC was greater with high expression of α-SMA and NG2 positive cells. DPSC had α-SMA positive cells but showed very few NG2 positive cells. Further, SCAP cells were positive for α-SMA while they completely lacked NG2 positive cells. We found the pericyte differentiation ability of these stem cells to be different, and this significantly affected the vasculogenic ability and quality of the vessel networks. In summary, we conclude that, among stem cells from different craniofacial regions, BMSCs appear more suitable for engineering of mature vascularized networks than DPSCs or SCAPs.

A dentin-derived hydrogel bioink for 3D bioprinting of cell laden scaffolds for regenerative dentistry.

Recent studies in tissue engineering have adopted extracellular matrix (ECM) derived scaffolds as natural and cytocompatible microenvironments for tissue regeneration. The dentin matrix, specifically, has been shown to be associated with a host of soluble and insoluble signaling molecules that can promote odontogenesis. Here, we have developed a novel bioink, blending printable alginate (3% w/v) hydrogels with the soluble and insoluble fractions of the dentin matrix. We have optimized the printing parameters and the concentrations of the individual components of the bioink for print accuracy, cell viability and odontogenic potential. We find that, while viscosity, and hence printability of the bioinks, was greater in the formulations containing higher concentrations of alginate, a higher proportion of insoluble dentin matrix proteins significantly improved cell viability; where a 1:1 ratio of alginate and dentin (1:1 Alg-Dent) was most suitable. We further demonstrate high retention of the soluble dentin molecules within the 1:1 Alg-Dent hydrogel blends, evidencing renewed interactions between these molecules and the dentin matrix post crosslinking. Moreover, at concentrations of 100 µg ml-1, these soluble dentin molecules significantly enhanced odontogenic differentiation of stem cells from the apical papilla encapsulated in bioprinted hydrogels. In summary, the proposed novel bioinks have demonstrable cytocompatibility and natural odontogenic capacity, which can be a used to reproducibly fabricate scaffolds with complex three-dimensional microarchitectures for regenerative dentistry in the future.

Engineering Microvascular Networks in LED Light-Cured Cell-Laden Hydrogels.

The success of tissue engineering inevitably depends on the fabrication of tissue constructs that can be vascularized and that anastomose with the host vasculature. In this report, we studied the effects of light-emitting diode (LED) photopolymerized gelatin methacryloyl hydrogels (GelMA), encapsulated with stem cells from the apical papilla (SCAP) and human umbilical vein endothelial cells (HUVECs), in promoting vasculature network formation as a function of hydrogel physical and mechanical properties, as well as total cell density. Lithium acylphosphinate (LAP) was used as the photoinitiator in concentrations of 0.05, 0.075, 0.1% (w/v). GelMA hydrogel precursors of 5% (w/v) were encapsulated with cocultures of SCAPs and HUVECs at different cell densities (1×, 5×, and 10 × 106 cells/mL) and photo-cross-linked for 5 s. Results suggested that the compressive modulus of GelMA hydrogels increased as a function of LAP concentration, and had a maximum stiffness of 3.2 kPa. GelMA hydrogels photopolymerized using 0.05 or 0.075% LAP, which had an average of 1.5 and 1.6 kPa of elastic modulus respectively, had the most efficient vasculature formation after 5 days, and these results were further enhanced when the highest cell density (10 × 106 cells/mL) was used. Immunofluorescence images showed that SCAP cells spread in close contact with endothelial networks and expressed alpha smooth muscle actin (αSMA), which is suggestive of their differentiation into pericyte-like cells. αSMA expression was also apparently higher in hydrogels polymerized with 0.05% LAP and 10 × 106 cells/mLl. In conclusion, photopolymerization of GelMA hydrogels using an LED-light source can be an effective method for potential chair-side/in situ procedures for engineering of vascularized tissue constructs in regenerative medicine.

Photopolymerization of cell-laden gelatin methacryloyl hydrogels using a dental curing light for regenerative dentistry.

Photopolymerized hydrogels, such as gelatin methacryloyl (GelMA), have desirable biological and mechanical characteristics for a range of tissue engineering applications. OBJECTIVE: This study aimed to optimize a new method to photopolymerize GelMA using a dental curing light (DL). METHODS: Lithium acylphosphinate photo-initiator (LAP, 0.05, 0.067, 0.1% w/v) was evaluated for its ability to polymerize GelMA hydrogel precursors (10% w/v) encapsulated with odontoblast-like cells (OD21). Different irradiances (1650, 2300 and 3700mW/cm2) and photo-curing times (5-20s) were tested, and compared against the parameters typically used in UV light photopolymerization (45mW/cm2, 0.1% w/v Irgacure 2959 as photoinitiator). Physical and mechanical properties of the photopolymerized GelMA hydrogels were determined. Cell viability was assessed using a live and dead assay kit. RESULTS: Comparing DL and UV polymerization methods, the DL method photopolymerized GelMA precursor faster and presented larger pore size than the UV polymerization method. The live and dead assay showed more than 80% of cells were viable when hydrogels were photopolymerized with the different DL irradiances. However, the cell viability decreased when the exposure time was increased to 20s using the 1650mW/cm2 intensity, and when the LAP concentration was increased from 0.05 to 0.1%. Both DL and UV photocrosslinked hydrogels supported a high percentage of cell viability and enabled fabrication of micropatterns using a photolithography microfabrication technique. SIGNIFICANCE: The proposed method to photopolymerize GelMA cell-laden hydrogels using a dental curing light is effective and represents an important step towards the establishment of chair-side procedures in regenerative dentistry.

Electrospun Nanofibrous Meshes Cultured With Wharton's Jelly Stem Cell: An Alternative for Cartilage Regeneration, Without the Need of Growth Factors.

Many efforts are being directed worldwide to the treatment of OA-focal lesions. The majority of those efforts comprise either the refinement of surgical techniques or combinations of biomaterials with various autologous cells. Herein, we tested electrospun polycaprolactone (PCL) nanofibrous meshes for cartilage tissue engineering. For that, articular chondrocytes (hACs) isolated from human osteoarthritic joints and Wharton's Jelly Stem Cells (hWJSCs) are cultured on electrospun nanofiber meshes, without adding external growth factors. We observed higher glycosaminoglycans production and higher over-expression of cartilage-related genes from hWJSCs cultured with basal medium, when compared to hACs isolated from osteoarthritic joints. Moreover, the presence of sulfated proteoglycans and collagen type II is observed on both types of cell cultures. We believe that this effect is due to either the electrospun nanofibers topography or the intrinsic chondrogenic differentiation potential of hWJSCs. Therefore, we propose the electrospun nanofibrous scaffolds in combination with hWJSCs as a viable alternative to the commercial membranes used in autologous chondrogenic regeneration approaches.

Advances and perspectives in tooth tissue engineering.

Bio-engineered teeth that can grow and remodel in a manner similar to that of natural teeth have the potential to serve as permanent replacements to the currently used prosthetic teeth, such as dental implants. A major challenge in designing functional bio-engineered teeth is to mimic both the structural and anisotropic mechanical characteristics of the native tooth. Therefore, the field of dental and whole tooth regeneration has advanced towards the molecular and nanoscale design of bio-active, biomimetic systems, using biomaterials, drug delivery systems and stem cells. The focus of this review is to discuss recent advances in tooth tissue engineering, using biomimetic scaffolds that provide proper architectural cues, exhibit the capacity to support dental stem cell proliferation and differentiation and sequester and release bio-active agents, such as growth factors and nucleic acids, in a spatiotemporal controlled manner. Although many in vitro and in vivo studies on tooth regeneration appear promising, before tooth tissue engineering becomes a reality for humans, additional research is needed to perfect methods that use adult human dental stem cells, as opposed to embryonic dental stem cells, and to devise the means to generate bio-engineered teeth of predetermined size and shape. Copyright © 2016 John Wiley & Sons, Ltd.

Dental cell sheet biomimetic tooth bud model.

Tissue engineering and regenerative medicine technologies offer promising therapies for both medicine and dentistry. Our long-term goal is to create functional biomimetic tooth buds for eventual tooth replacement in humans. Here, our objective was to create a biomimetic 3D tooth bud model consisting of dental epithelial (DE) - dental mesenchymal (DM) cell sheets (CSs) combined with biomimetic enamel organ and pulp organ layers created using GelMA hydrogels. Pig DE or DM cells seeded on temperature-responsive plates at various cell densities (0.02, 0.114 and 0.228 cells 10(6)/cm(2)) and cultured for 7, 14 and 21 days were used to generate DE and DM cell sheets, respectively. Dental CSs were combined with GelMA encapsulated DE and DM cell layers to form bioengineered 3D tooth buds. Biomimetic 3D tooth bud constructs were cultured in vitro, or implanted in vivo for 3 weeks. Analyses were performed using micro-CT, H&E staining, polarized light (Pol) microscopy, immunofluorescent (IF) and immunohistochemical (IHC) analyses. H&E, IHC and IF analyses showed that in vitro cultured multilayered DE-DM CSs expressed appropriate tooth marker expression patterns including SHH, BMP2, RUNX2, tenascin and syndecan, which normally direct DE-DM interactions, DM cell condensation, and dental cell differentiation. In vivo implanted 3D tooth bud constructs exhibited mineralized tissue formation of specified size and shape, and SHH, BMP2 and RUNX2and dental cell differentiation marker expression. We propose our biomimetic 3D tooth buds as models to study optimized DE-DM cell interactions leading to functional biomimetic replacement tooth formation.

Antibacterial activity of chitosan nanofiber meshes with liposomes immobilized releasing gentamicin.

Chitsan (Ch) nanofiber mesh (NFM) is a material with natural characteristics favoring its use in human wound dressing. The present work proposes a gentamicin-loaded liposome immobilized at the surface of Ch NFMs to promote its antibacterial activity. To achieve this purpose, Ch NFMs were functionalized with thiol groups, and gentamicin-loaded liposomes were covalently immobilized by the reaction of the SH groups with maleimide. The maximum concentration of SH groups (55.52±11.19nmolcm(-2)) was obtained at pH 7. A fluorescent dye was covalently bound to the SH groups present at the surface of electrospun Ch NFMs. Their spatial distribution was uniform throughout the NFMs when analyzed by fluorescence microscopy. Gentamicin was successfully encapsulated into the liposomes with an efficiency of 17%. Gentamicin-loaded liposomes were uniformly distributed at the surface of the Ch NFMs and the drug release kinetic showed a sustained release of gentamicin during 16h, achieving a steady state at 24h. The in vitro susceptibility tests confirmed that the gentamicin released from the liposomes immobilized at the surface of electrospun Ch NFM has bactericidal activity against Escherichia coli, Pseudomonas aeruginosa and Staphylococcus aureus. The results show that the developed system has promising performance for wound dressing applications, avoiding infections caused by these common pathogens.

Nanoparticle-based bioactive agent release systems for bone and cartilage tissue engineering.

The inability to deliver bioactive agents locally in a transient but sustained manner is one of the challenges on the development of bio-functionalized scaffolds for tissue engineering (TE) and regenerative medicine. The mode of release is especially relevant when the bioactive agent is a growth factor (GF), because the dose and the spatiotemporal release of such agents at the site of injury are crucial to achieve a successful outcome. Strategies that combine scaffolds and drug delivery systems have the potential to provide more effective tissue regeneration relative to current therapies. Nanoparticles (NPs) can protect the bioactive agents, control its profile, decrease the occurrence and severity of side effects and deliver the bioactive agent to the target cells maximizing its effect. Scaffolds containing NPs loaded with bioactive agents can be used for their local delivery, enabling site-specific pharmacological effects such as the induction of cell proliferation and differentiation, and, consequently, neo-tissue formation. This review aims to describe the concept of combining NPs with scaffolds, and the current efforts aiming to develop highly multi-functional bioactive agent release systems, with the emphasis on their application in TE of connective tissues.

On the use of dexamethasone-loaded liposomes to induce the osteogenic differentiation of human mesenchymal stem cells.

Stem cells have received considerable attention by the scientific community because of their potential for tissue engineering and regenerative medicine. The most frequently used method to promote their differentiation is supplementation of the in vitro culture medium with growth/differentiation factors (GDFs). The limitations of that strategy caused by the short half-life of GDFs limit its efficacy in vivo and consequently its clinical use. Thus, the development of new concepts that enable the bioactivity and bioavailability of GDFs to be protected, both in vitro and in vivo, is very relevant. Nanoparticle-based drug delivery systems can be injected, protect the GDFs and enable spatiotemporal release kinetics to be controlled. Liposomes are well-established nanodelivery devices presenting significant advantages, viz. a high load-carrying capacity, relative safety and easy production, and a versatile nature in terms of possible formulations and surface functionalization. The main objective of the present study was to optimize the formulation of liposomes to encapsulate dexamethasone (Dex). Our results showed that the optimized Dex-loaded liposomes do not have any cytotoxic effect on human bone marrow-derived mesenchymal stem cells (hBMSCs). More importantly, they were able to promote an earlier induction of differentiation of hBMSCs into the osteogenic lineage, as demonstrated by the expression of osteoblastic markers, both phenotypically and genotypically. We concluded that Dex-loaded liposomes represent a viable nanoparticle strategy with enhanced safety and efficacy for tissue engineering and regenerative medicine.