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The fabrication of cell-laden biomimetic scaffolds represents a pillar of tissue engineering and regenerative medicine (TERM) strategies, and collagen is the gold standard matrix for cells to be. In the recent years, extrusion 3D bioprinting introduced new possibilities to increase collagen scaffold performances thanks to the precision, reproducibility, and spatial control. However, the design of pure collagen bioinks represents a challenge, due to the low storage modulus and the long gelation time, which strongly impede the extrusion of a collagen filament and the retention of the desired shape post-printing. In this study, the tannic acid-mediated crosslinking of the outer layer of collagen is proposed as strategy to enable collagen filament extrusion. For this purpose, a tannic acid solution has been used as supporting bath to act exclusively as external crosslinker during the printing process, while allowing the pH- and temperature-driven formation of collagen fibers within the core. Collagen hydrogels (concentration 2–6 mg/mL) were extruded in tannic acid solutions (concentration 5–20 mg/mL). Results proved that external interaction of collagen with tannic acid during 3D printing enables filament extrusion without affecting the bulk properties of the scaffold. The temporary collagen-tannic acid interaction resulted in the formation of a membrane-like external layer that protected the core, where collagen could freely arrange in fibers. The precision of the printed shapes was affected by both tannic acid concentration and needle diameter and can thus be tuned. Altogether, results shown in this study proved that tannic acid bath enables collagen bioprinting, preserves collagen morphology, and allows the manufacture of a cell-laden pure collagen scaffold.

Optimization of agricultural practices is crucial to ensure high and sustainable food production. In this regard, hyperspectral remote sensing demonstrated its usefulness in the building of models able to predict several vegetation parameters. The effort, in terms of time and costs, of a remote sensing campaign depends on both the instruments to be exploited and the number of samples to be acquired for model calibration. Therefore, the reduction of field activities is important to limit monitoring costs. The purpose of this work is to demonstrate that the combination of appropriate active learning strategies and regression models can dramatically reduce the field work with negligible degradation of model prediction performance. This has been assessed by processing two different datasets. In the first experiment, estimated ryegrass biomass and nitrogen content resulted in average root-mean-square error (RMSE) values of 0.17 t/ha and 3.22 kg/ha against the best literature values, on the same dataset, of 0.20 t/ha and 4.68 kg/ha. In the second test, the estimation of maize biomass and nitrogen content with a reduced calibration dataset provided, respectively, an RMSE of 0.65 t/ha and 11.1 kg/ha against a reference value of 7.1 kg/ha concerning the estimation of nitrogen content. Depending on the settings, the developed methodology provides results comparable with the literature or negligibly degraded, despite a reduction of the calibration dataset up to 80%.

Abstract In recent decades, human mesenchymal stem cells (hMSCs) have gained momentum in the field of cell therapy for treating cartilage and bone injuries. Despite the tri-lineage multipotency, proliferative properties, and potent immunomodulatory effects of hMSCs, their clinical potential is hindered by donor variations, limiting their use in medical settings. To address this challenge, gene delivery technologies have emerged as a promising approach to modulate the phenotype and commitment of hMSCs towards specific cell lineages, thereby enhancing osteochondral repair strategies. This review provides a comprehensive overview of current non-viral gene delivery approaches used to engineer MSCs, highlighting key factors such as the choice of nucleic acid or delivery vector, transfection strategies, and experimental parameters. Additionally, it outlines various protocols and methods for qualitative and quantitative evaluation of their therapeutic potential as a delivery system in osteochondral regenerative applications. In summary, this technical review offers a practical guide for optimizing non-viral systems in osteochondral regenerative approaches. Graphical Abstract hMSCs constitute a key target population for gene therapy techniques. Nevertheless, there is a long way to go for their translation into clinical treatments. In this review, we remind the most relevant transfection conditions to be optimized, such as the type of nucleic acid or delivery vector, the transfection strategy, and the experimental parameters to accurately evaluate a delivery system. This survey provides a practical guide to optimizing non-viral systems for osteochondral regenerative approaches.

Abstract Cell microencapsulation has been utilized for years as a means of cell shielding from the external environment while facilitating the transport of gases, general metabolites, and secretory bioactive molecules at once. In this light, hydrogels may support the structural integrity and functionality of encapsulated biologics whereas ensuring cell viability and function and releasing potential therapeutic factors once in situ. In this work, we describe a straightforward strategy to fabricate silk fibroin (SF) microgels (µgels) and encapsulate cells into them. SF µgels (size ≈ 200 µm) were obtained through ultrasonication-induced gelation of SF in a water-oil emulsion phase. A thorough physicochemical (SEM analysis, and FT-IR) and mechanical (microindentation tests) characterization of SF µgels were carried out to assess their nanostructure, porosity, and stiffness. SF µgels were used to encapsulate and culture L929 and primary myoblasts. Interestingly, SF µgels showed a selective release of relatively small proteins (e.g., VEGF, molecular weight, MW = 40 kDa) by the encapsulated primary myoblasts, while bigger (macro)molecules (MW = 160 kDa) were hampered to diffusing through the µgels. This article provided the groundwork to expand the use of SF hydrogels into a versatile platform for encapsulating relevant cells able to release paracrine factors potentially regulating tissue and/or organ functions, thus promoting their regeneration. Graphical Abstract

The spaceborne imaging spectroscopy mission PRecursore IperSpettrale della Missione Applicativa (PRISMA), launched on 22 March 2019 by the Italian Space Agency, opens new opportunities in many scientific domains, including precision farming and sustainable agriculture. This new Earth Observation (EO) data stream requires new-generation approaches for the estimation of important biophysical crop variables (BVs). In this framework, this study evaluated a hybrid approach, combining the radiative transfer model PROSAIL-PRO and several machine learning (ML) regression algorithms, for the retrieval of canopy chlorophyll content (CCC) and canopy nitrogen content (CNC) from synthetic PRISMA data. PRISMA-like data were simulated from two images acquired by the airborne sensor HyPlant, during a campaign performed in Grosseto (Italy) in 2018. CCC and CNC estimations, assessed from the best performing ML algorithms, were used to define two relations with plant nitrogen uptake (PNU). CNC proved to be slightly more correlated to PNU than CCC (R2 = 0.82 and R2 = 0.80, respectively). The CNC-PNU model was then applied to actual PRISMA images acquired in 2020. The results showed that the estimated PNU values are within the expected ranges, and the temporal trends are compatible with plant phenology stages.

Earth observation techniques represent a reliable and faster alternative to in-situ measurements by providing spatio-temporal information on crop status. In this framework, a study was conducted to assess the performance of hybrid approaches, either standard (HYB) or exploiting an active learning optimisation strategy (HYB-AL), to estimate leaf area index (LAI) and canopy nitrogen content (CNC) from Sentinel–2 (S2) data, in rice crops. To achieve this, the PROSAIL-PRO Radiative Transfer Model (RTM) was tested. Results demonstrate that a wide range of rice spectra, simulated according to realistic crop parameters, are reliable when appropriate field background conditions are considered. Simulations were used to train a Gaussian Process Regression (GPR) algorithm. Both cross-validation and validation results showed that HYB-AL approach resulted the best performing retrieval schema. LAI estimation achieved good performance (R2=0.86; RMSE=0.54) and resulted very promising for model application in operational monitoring systems. CNC estimations showed moderate performance (R2=0.63; RMSE=0.89) due to a saturation behaviour limiting the retrieval accuracy for moderate/high CNC values, approximately above 4 [g m−2]. S2 maps of LAI and CNC provided spatio-temporal information in agreement with crop growth, nutritional status and agro-practices applied to the study area, resulting in an important contribution to precision farming applications.

Abstract Background With the success of recent non-viral gene delivery-based COVID-19 vaccines, nanovectors have gained some public acceptance and come to the forefront of advanced therapies. Unfortunately, the relatively low ability of the vectors to overcome cellular barriers adversely affects their effectiveness. Scientists have thus been striving to develop ever more effective gene delivery vectors, but the results are still far from satisfactory. Therefore, developing novel strategies is probably the only way forward to bring about genuine change. Herein, we devise a brand-new gene delivery strategy to boost dramatically the transfection efficiency of two gold standard nucleic acid (NA)/polymer nanoparticles (polyplexes) in vitro. Results We conceived a device to generate milli-to-nanoscale vibrational cues as a function of the frequency set, and deliver vertical uniaxial displacements to adherent cells in culture. A short-lived high-frequency vibrational load (t = 5 min, f = 1,000 Hz) caused abrupt and extensive plasmalemma outgrowths but was safe for cells as neither cell proliferation rate nor viability was affected. Cells took about 1 hr to revert to quasi-naïve morphology through plasma membrane remodeling. In turn, this eventually triggered the mechano-activated clathrin-mediated endocytic pathway and made cells more apt to internalize polyplexes, resulting in transfection efficiencies increased from 10-to-100-fold. Noteworthy, these results were obtained transfecting three cell lines and hard-to-transfect primary cells. Conclusions In this work, we focus on a new technology to enhance the intracellular delivery of NAs and improve the transfection efficiency of non-viral vectors through priming adherent cells with a short vibrational stimulation. This study paves the way for capitalizing on physical cell stimulation(s) to significantly raise the effectiveness of gene delivery vectors in vitro and ex vivo. Graphical Abstract

At present, it is critical to accurately monitor wheat crops to help decision-making processes in precision agriculture. This research aims to retrieve various wheat crop traits from hyperspectral data using machine learning regression algorithms (MLRAs) and dimensionality reduction (DR) techniques. This experiment was conducted in an agricultural field in Arborea, Oristano-Sardinia, Italy, with different factors such as cultivars, N-treatments, and soil ploughing conditions. Hyperspectral data were acquired on the ground using a full-range Spectral Evolution spectrometer (350–2500 nm). Four DR techniques, including (i) variable influence on projection (VIP), (ii) principal component analysis (PCA), (iii) vegetation indices (VIs), and (iv) spectroscopic feature (SF) calculation, were undertaken to reduce the dimension of the hyperspectral data while maintaining the information content. We used five MLRA models, including (i) partial least squares regression (PLSR), (ii) random forest (RF), (iii) support vector regression (SVR), (iv) Gaussian process regression (GPR), and (v) neural network (NN), to retrieve wheat traits at either leaf and canopy levels. The studied traits were leaf area index (LAI), leaf and canopy water content (LWC and CWC), leaf and canopy chlorophyll content (LCC and CCC), and leaf and canopy nitrogen content (LNC and CNC). MLRA models were able to accurately retrieve wheat traits at the canopy level with PLSR and NN indicating the highest modelling performance. On the contrary, MLRA models indicated less accurate retrievals of the leaf-level traits. DR techniques were found to notably improve the retrieval accuracy of crop traits. Furthermore, the generated models were re-calibrated using soil spectra and then transferred to an airborne dataset collected using a CASI-SASI hyperspectral sensor, allowing the estimation of wheat traits across the entire field. The predicted crop trait maps illustrated consistent patterns while also preserving the real-field characteristics well. Lastly, a statistical paired t-test was undertaken to conduct a proof of concept of wheat phenotyping analysis considering the different agricultural variables across the study site. N-treatment caused significant differences in wheat crop traits in many instances, whereas the observed differences were less pronounced between the cultivars. No particular impact of soil ploughing conditions on wheat crop characteristics was found. Using such combinations of MLRA and DR techniques based on hyperspectral data can help to effectively monitor crop traits throughout the cropping seasons and can also be readily applied to other agricultural settings to help both precision farming applications and the implementation of high-throughput phenotyping solutions.

The new generation of available (i.e., PRISMA, ENMAP, DESIS) and future (i.e., ESA-CHIME, NASA-SBG) spaceborne hyperspectral missions provide unprecedented data for environmental and agricultural monitoring, such as crop trait assessment. This paper focuses on retrieving two crop traits, specifically Chlorophyll and Nitrogen content at the canopy level (CCC and CNC), starting from hyperspectral images acquired during the CHIME-RCS project, exploiting a self-supervised learning (SSL) technique. SSL is a machine learning paradigm that leverages unlabeled data to generate valuable representations for downstream tasks, bridging the gap between unsupervised and supervised learning. The proposed method comprises pre-training and fine-tuning procedures: in the first stage, a de-noising Convolutional Autoencoder is trained using pairs of noisy and clean CHIME-like images; the pre-trained Encoder network is utilized as-is or fine-tuned in the second stage. The paper demonstrates the applicability of this technique in hybrid approach methods that combine Radiative Transfer Modelling (RTM) and Machine Learning Regression Algorithm (MLRA) to set up a retrieval schema able to estimate crop traits from new generation space-born hyperspectral data. The results showcase excellent prediction accuracy for estimating CCC (R2 = 0.8318; RMSE = 0.2490) and CNC (R2 = 0.9186; RMSE = 0.7908) for maize crops from CHIME-like images without requiring further ground data calibration.

IntroductionThe use of spinal implants for the treatment of back disorders is largely affected by the insurgence of infections at the implantation site. Antibacterial coatings have been proposed as a viable solution to limit such infections. However, despite being effective at short-term, conventional coatings lack the ability to prevent infections at medium and long-term. Hydrogel-based drug delivery systems may represent a solution controlling the release of the loaded antibacterial agents while improving cell integration. Agarose, in particular, is a biocompatible natural polysaccharide known to improve cell growth and already used in drug delivery system formulations. In this study, an agarose hydrogel-based coating has been developed for the controlled release of gentamicin (GS).MethodsSand blasted Ti6Al4V discs were grafted with dopamine (DOPA) solution. After, GS loaded agarose hydrogels have been produced and additioned with tannic acid (TA) and calcium chloride (CaCl2) as crosslinkers. The different GS-loaded hydrogel formulations were deposited on Ti6Al4V-DOPA surfaces, and allowed to react under UV irradiation. Surface topography, wettability and composition have been analyzed with profilometry, static contact angle measurement, XPS and FTIR spectroscopy analyses. GS release was performed under pseudo-physiological conditions up to 28 days and the released GS was quantified using a specific ELISA test. The cytotoxicity of the produced coatings against human cells have been tested, along with their antibacterial activity against S. aureus bacteria.ResultsA homogeneous coating was obtained with all the hydrogel formulations. Moreover, the coatings presented a hydrophilic behavior and micro-scale surface roughness. The addition of TA in the hydrogel formulations showed an increase in the release time compared to the normal GS-agarose hydrogels. Moreover, the GS released from these gels was able to significantly inhibit S. aureus growth compared to the GS-agarose hydrogels. The addition of CaCl2 to the gel formulation was able to significantly decrease cytotoxicity of the TA-modified hydrogels.ConclusionsDue to their surface properties, low cytotoxicity and high antibacterial effects, the hereby proposed gentamicin-loaded agarose-hydrogels provide new insight, and represent a promising approach for the surface modification of spinal implants, greatly impacting their application in the orthopedic surgical scenario.

Plasma electrolytic oxidation (PEO) is proven as a scalable method for surface treatment of titanium (Ti) providing a thick oxide layer with porous micro-nano morphology. Despite the lack of antibacterial performance, this modification has potential to improve the osseointegration properties of Ti-based implant. To address this limitation, we demonstrated a new concept, showing that partial incorporation of graphene oxide (GO) to porous-PEO Ti-surface can significantly improve its antibacterial performance. Our idea for partial coating compared with a full surface coverage of GO was motivated to create a mixed surface with porous PEO and GO to improve antibacterial ability, while maintaining the osseointegration properties. To achieve these goals, we combined PEO and electrophoretic deposition process (EPD) to deposit GO sheets over the titanium PEO-treated substrate. The SEM, EDS, optical profilometry, XRD and Raman spectroscopy confirmed the growth of unique multi-structured porous PEO structures decorated with GO patches. The bio-mineralization test provided the evidence of hydroxyapatite formation over the PEO-GO surface, indicating its good bioactivity. Finally, PEO-GO samples demonstrated a superior antibacterial rate of ~80% against E.coli and ~100% against S. aureus. These results indicate that PEO-GO modified titanium substrates are very promising for the development of advanced biomedical implants.

In the next few years, the new Copernicus Hyperspectral Imaging Mission (CHIME) is foreseen to be launched by the European Space Agency (ESA). This mission will provide an unprecedented amount of hyperspectral data, enabling new research possibilities within several fields of natural resources, including the “agriculture and food security” domain. In order to efficiently exploit this upcoming hyperspectral data stream, new processing methods and techniques need to be studied and implemented. In this work, the hybrid approach (HYB) and its variant, featuring sampling dimensionality reduction through active learning heuristics (HAL), were applied to CHIME-like data to evaluate the retrieval of crop traits, such as chlorophyll and nitrogen content at both leaf (LCC and LNC) and canopy level (CCC and CNC). The results showed that HYB was able to provide reliable estimations at canopy level (R2 = 0.79, RMSE = 0.38 g m−2 for CCC and R2 = 0.84, RMSE = 1.10 g m−2 for CNC) but failed at leaf level. The HAL approach improved retrieval accuracy at canopy level (best metric: R2 = 0.88 and RMSE = 0.21 g m−2 for CCC; R2 = 0.93 and RMSE = 0.71 g m−2 for CNC), providing good results also at leaf level (best metrics: R2 = 0.72 and RMSE = 3.31 μg cm−2 for LCC; R2 = 0.56 and RMSE = 0.02 mg cm−2 for LNC). The promising results obtained through the hybrid approach support the feasibility of an operational retrieval of chlorophyll and nitrogen content, e.g., in the framework of the future CHIME mission. However, further efforts are required to investigate the approach across different years, sites and crop types in order to improve its transferability to other contexts.

Background: Great interest has recently been focused on tooth and tooth derivatives as suitable substrates for the treatment of alveolar bone defects. Here, we propose the use of demineralized baby teeth (BT) as potential grafting materials for bone augmentation procedures. Methods: Particles of human BT (Ø < 1 mm) were demineralized by means of a chemical/thermal treatment. Demineralized BT particles were thoroughly characterized by scanning electron microscopy/energy dispersive X-ray analyses to evaluate the effects of the demineralization on BT topography and mineral phase composition, and by enzyme-linked immunosorbent assays (ELISA) to quantify collagen and bone morphogenetic protein-2 (BMP-2) protein contents. The response of SAOS-2 cells to exogenous BMP-2 stimulation was evaluated to identify the minimum BMP-2 concentration able to induce osteodifferentiation in vitro (alkaline phosphatase (ALP) activity). Results: The demineralization treatment led to a dramatic decrease in relative Ca and P content (%) of ≈75% with respect to the native BT particles, while preserving native protein conformation and activity. Interestingly, the demineralization process led to a rise in the bioavailability of BMP-2 in BT particles, as compared to the untreated counterparts. The BMP-2 content found in demineralized BT was also proved to be very effective in enhancing ALP activity, thus in the osteodifferentiation of SAOS-2 cells in vitro, as confirmed by cell experiments performed upon exogenously added BMP-2. Conclusions: In this study we demonstrate that the BMP-2 content found in demineralized BT is very effective in inducing cell osteodifferentiation, and strengthens the idea that BTs are very attractive bioactive materials for bone-grafting procedures.

Transfection by means of non-viral gene delivery vectors is the cornerstone of modern gene delivery. Despite the resources poured into the development of ever more effective transfectants, improvement is still slow and limited. Of note, the performance of any gene delivery vector in vitro is strictly dependent on several experimental conditions specific to each laboratory. The lack of standard tests has thus largely contributed to the flood of inconsistent data underpinning the reproducibility crisis. A way researchers seek to address this issue is by gauging the effectiveness of newly synthesized gene delivery vectors with respect to benchmarks of seemingly well-known behavior. However, the performance of such reference molecules is also affected by the testing conditions. This survey points to non-standardized transfection settings and limited information on variables deemed relevant in this context as the major cause of such misalignments. This review provides a catalog of conditions optimized for the gold standard and internal reference, 25 kDa polyethyleneimine, that can be profitably replicated across studies for the sake of comparison. Overall, we wish to pave the way for the implementation of standardized protocols in order to make the evaluation of the effectiveness of transfectants as unbiased as possible.

The aims of this study were: (i) the mapping of asbestos cement roofs in an urban area; and (ii) the development of a spectral index related to the roof weathering status. Aerial images were collected through the Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) sensor, which acquires data in 102 channels from the visible to the thermal infrared spectral range. An image based supervised classification was performed using the Spectral Angle Mapper (SAM) algorithm. The SAM was trained through a set of pixels selected on roofs of different materials. The map showed an average producer’s accuracy (PA) of 86% and a user’s accuracy (UA) of 89% for the asbestos cement class. A novel spectral index, the “Index of Surface Deterioration” (ISD), was defined based on measurements collected with a portable spectroradiometer on asbestos cement roofs that were characterized by different weathering statuses. The ISD was then calculated on the MIVIS images, allowing the distinction of two weathering classes (i.e., high and low). The asbestos cement map was handled in a Geographic Information System (GIS) in order to supply the municipalities with the cadastral references of each property having an asbestos cement roof. This tool can be purposed for municipalities as an aid to prioritize asbestos removal, based on roof weathering status.

High-spatial-resolution satellites usually have the constraint of a low temporal frequency, which leads to long periods without information in cloudy areas. Furthermore, low-spatial-resolution satellites have higher revisit cycles. Combining information from high- and low- spatial-resolution satellites is thought a key factor for studies that require dense time series of high-resolution images, e.g., crop monitoring. There are several fusion methods in the bibliography, but they are time-consuming and complicated to implement. Moreover, the local evaluation of the fused images is rarely analyzed. In this paper, we present a simple and fast fusion method based on a weighted average of two input images (H and L), which are weighted by their temporal validity to the image to be fused. The method was applied to two years (2009–2010) of Landsat and MODIS (MODerate Imaging Spectroradiometer) images that were acquired over a cropped area in Brazil. The fusion method was evaluated at global and local scales. The results show that the fused images reproduced reliable crop temporal profiles and correctly delineated the boundaries between two neighboring fields. The greatest advantages of the proposed method are the execution time and ease of use, which allow us to obtain a fused image in less than five minutes.

The World Health Organization estimates that 100 thousand people in the world die every year from asbestos-related cancers and more than 300 thousand European citizens are expected to die from asbestos-related mesothelioma by 2030. Both the European and the Italian legislations have banned the manufacture, importation, processing and distribution in commerce of asbestos-containing products and have recommended action plans for the safe removal of asbestos from public and private buildings. This paper describes the quantitative mapping of asbestos-cement covers over a large mountainous region of Italian Western Alps using the Multispectral Infrared and Visible Imaging Spectrometer sensor. A very large data set made up of 61 airborne transect strips covering 3263 km2 were processed to support the identification of buildings with asbestos-cement roofing, promoted by the Valle d’Aosta Autonomous Region with the support of the Regional Environmental Protection Agency. Results showed an overall mapping accuracy of 80%, in terms of asbestos-cement surface detected. The influence of topography on the classification’s accuracy suggested that even in high relief landscapes, the spatial resolution of data is the major source of errors and the smaller asbestos-cement covers were not detected or misclassified.

Rangeland monitoring services require the capability to investigate vegetation condition and to assess biomass production, especially in areas where local livelihood depends on rangeland status. Remote sensing solutions are strongly recommended, where the systematic acquisition of field data is not feasible and does not guarantee properly describing the spatio-temporal dynamics of wide areas. Recent research on semi-arid rangelands has focused its attention on the evaporative fraction (EF), a key factor to estimate evapotranspiration (ET) in the energy balance (EB) algorithm. EF is strongly linked to the vegetation water status, and works conducted on eddy covariance towers used this parameter to increase the performances of satellite-based biomass estimation. In this work, a method to estimate EF from MODIS products, originally developed for evapotranspiration estimation, is tested and evaluated. Results show that the EF estimation from low spatial resolution over wide semi-arid area is feasible. Estimated EF resulted in being well correlated to field ET measurements, and the spatial patterns of EF maps are in agreement with the well-known climatic and landscape Sahelian features. The preliminary test on rangeland biomass production shows that satellite-retrieved EF as a water availability factor significantly increased the capacity of a remote sensing operational product to detect the variability of the field biomass measurements.

BACKGROUND: Successful non-viral gene delivery currently requires compromises to achieve useful transfection levels while minimizing toxicity. Despite high molecular weight (MW) branched polyethylenimine (bPEI) is considered the gold standard polymeric transfectant, it suffers from high cytotoxicity. Inversely, its low MW counterpart is less toxic and effective in transfection. Moreover, chitosan is a highly biocompatible and biodegradable polymer but characterized by very low transfection efficiency. In this scenario, a straightforward approach widely exploited to develop effective transfectants relies on the synthesis of chitosan-graft-low MW bPEIs (Chi-g-bPEI(x)) but, despite the vast amount of work that has been done in developing promising polymeric assemblies, the possible influence of the degree of grafting on the overall behavior of copolymers for gene delivery has been largely overlooked. METHODOLOGY/PRINCIPAL FINDINGS: With the aim of providing a comprehensive evaluation of the pivotal role of the degree of grafting in modulating the overall transfection effectiveness of copolymeric vectors, we have synthesized seven Chi-g-bPEI(x) derivatives with a variable amount of bPEI grafts (minimum: 0.6%; maximum: 8.8%). Along the Chi-g-bPEI(x) series, the higher the degree of grafting, the greater the ζ-potential and the cytotoxicity of the resulting polyplexes. Most important, in all cell lines tested the intermediate degree of grafting of 2.7% conferred low cytotoxicity and higher transfection efficiency compared to other Chi-g-bPEI(x) copolymers. We emphasize that, in transfection experiments carried out in primary articular chondrocytes, Chi-g-bPEI(2.7%) was as effective as and less cytotoxic than the gold standard 25 kDa bPEI. CONCLUSIONS/SIGNIFICANCE: This work underlines for the first time the pivotal role of the degree of grafting in modulating the overall transfection effectiveness of Chi-g-bPEI(x) copolymers. Crucially, we have demonstrated that, along the copolymer series, the fine tuning of the degree of grafting directly affected the overall charge of polyplexes and, altogether, had a direct effect on cytotoxicity.

BackgroundA promising strategy to create stimuli-responsive gene delivery systems is to exploit the redox gradient between the oxidizing extracellular milieu and the reducing cytoplasm in order to disassemble DNA/cationic lipid complexes (lipoplexes). On these premises, we previously described the synthesis of SS14 redox-sensitive gemini surfactant for gene delivery. Although others have attributed the beneficial effects of intracellular reducing environment to reduced glutathione (GSH), these observations cannot rule out the possible implication of the redox milieu in its whole on transfection efficiency of bioreducible transfectants leaving the determinants of DNA release largely undefined.Methodology/principal findingsWith the aim of addressing this issue, SS14 was here formulated into binary and ternary 100 nm-extruded liposomes and the effects of the helper lipid composition and of the SS14/helper lipids molar ratio on chemical-physical and structural parameters defining transfection effectiveness were investigated. Among all formulations tested, DOPC/DOPE/SS14 at 25:50:25 molar ratio was the most effective in transfection studies owing to the presence of dioleoyl chains and phosphatidylethanolamine head groups in co-lipids. The increase in SS14 content up to 50% along DOPC/DOPE/SS14 liposome series yielded enhanced transfection, up to 2.7-fold higher than that of the benchmark Lipofectamine 2000, without altering cytotoxicity of the corresponding lipoplexes at charge ratio 5. Secondly, we specifically investigated the redox-dependent mechanisms of gene delivery into cells through tailored protocols of transfection in GSH-depleted and repleted vs. increased oxidative stress conditions. Importantly, GSH specifically induced DNA release in batch and in vitro.Conclusions/significanceThe presence of helper lipids carrying unsaturated dioleoyl chains and phosphatidylethanolamine head groups significantly improved transfection efficiencies of DOPC/DOPE/SS14 lipoplexes. Most importantly, this study shows that intracellular GSH levels linearly correlated with transfection efficiency while oxidative stress levels did not, highlighting for the first time the pivotal role of GSH rather than oxidative stress in its whole in transfection of bioreducible vectors.