Effect of Interlayer on Flatness and Adhesion of Aerosol-Deposited Yttrium Oxide Coating.

In this study, Y2O3 coating is used as an interlayer between Al2O3 substrate and a ceramic coating; this is in order to minimize the morphological distortion produced by a single deposition of the ceramic coating on the Al2O3 substrate, which is performed using the aerosol method. The interlayer coating, which comprises the Y2O3 phase, is deposited on the Al2O3 substrate using an e-beam evaporator. The crystal structure of the powder that was used to process the coating is identified as cubic Y2O3. In contrast, the crystal structure of the top-coating layer and interlayer indicates the presence of two kinds of Y2O3 phases, which possess cubic and monoclinic structures. The single Y2O3 coating without an interlayer exhibits microcracks around the interface between the coating and the substrate, which can be attributed to the stress that occurs during aerosol deposition. In contrast, no cracks are found in the aerosol-deposited Y2O3 coating and interlayer, which show a desirable microstructure. The single Y2O3 coating and the Y2O3 coating with an interlayer exhibit similar hardness and elastic modulus values. Nevertheless, the Y2O3 coating with an interlayer exhibits a higher level of adhesion than the single Y2O3 coating, with a value of 14.8 N compared to 10.2 N.

Continuous blood pressure prediction system using Conv-LSTM network on hybrid latent features of photoplethysmogram (PPG) and electrocardiogram (ECG) signals.

Continuous blood pressure (BP) monitoring is essential for managing cardiovascular disease. However, existing devices often require expert handling, highlighting the need for alternative methods to simplify the process. Researchers have developed various methods using physiological signals to address this issue. Yet, many of these methods either fall short in accuracy according to the BHS, AAMI, and IEEE standards for BP measurement devices or suffer from low computational efficiency due to the complexity of their models. To solve this problem, we developed a BP prediction system that merges extracted features of PPG and ECG from two pulses of both signals using convolutional and LSTM layers, followed by incorporating the R-to-R interval durations as additional features for predicting systolic (SBP) and diastolic (DBP) blood pressure. Our findings indicate that the prediction accuracies for SBP and DBP were 5.306 ± 7.248 mmHg with a 0.877 correlation coefficient and 3.296 ± 4.764 mmHg with a 0.918 correlation coefficient, respectively. We found that our proposed model achieved a robust performance on the MIMIC III dataset with a minimum architectural design and high-level accuracy compared to existing methods. Thus, our method not only meets the passing category for BHS, AAMI, and IEEE guidelines but also stands out as the most rapidly accurate deep-learning-based BP measurement device currently available.

Development of in-silico drug cardiac toxicity evaluation system with consideration of inter-individual variability.

Safety pharmacology examines the potential for new drugs to have unusual, rare side effects such as torsade de pointes (TdP). Recently, as a part of the Comprehensive in vitro Proarrhythmia Assay (CiPA) project, techniques for predicting the development of drug-induced TdP through computer simulations have been proposed and verified. However, CiPA assessment generally does not consider the effect of cardiac cell inter-individual variability, especially related to metabolic status. The study aimed to explore whether rare proarrhythmic effects may be linked to the inter-individual variability of cardiac cells and whether incorporating this variability into computational models could alter the prediction of drugs' TdP risks. This study evaluated the contribution of two biological characteristics to the proarrhythmic effects. The first was spermine concentration, which varies with metabolic status; the second was L-type calcium permeability that could occur due to mutations. Twenty-eight drugs were examined throughout this study, and qNet was analyzed as an essential feature. Even though there were some discrepancies of TdP risk predictions from the baseline model, we found that considering the inter-individual variability might change the TdP risk of drugs. Several drugs in the high-risk drugs group were predicted to affect as intermediate and low-risk drugs in some individuals and vice versa. Also, most intermediate-risk drugs were expected to act as low-risk drugs. When compared, the effects of inter-individual variability of L-type calcium were more significant than spermine in altering the TdP risk of compounds. These results emphasize the importance of considering inter-individual variability to assess drugs.

Single heartbeat ECG authentication: a 1D-CNN framework for robust and efficient human identification.

This study proposes a small one-dimensional convolutional neural network (1D-CNN) framework for individual authentication, considering the hypothesis that a single heartbeat as input is sufficient to create a robust system. A short segment between R to R of electrocardiogram (ECG) signals was chosen to generate single heartbeat samples by enforcing a rigid length thresholding procedure combined with an interpolation technique. Additionally, we explored the benefits of the synthetic minority oversampling technique (SMOTE) to tackle the imbalance in sample distribution among individuals. The proposed framework was evaluated individually and in a mixture of four public databases: MIT-BIH Normal Sinus Rhythm (NSRDB), MIT-BIH Arrhythmia (MIT-ARR), ECG-ID, and MIMIC-III which are available in the Physionet repository. The proposed framework demonstrated excellent performance, achieving a perfect score (100%) across all metrics (i.e., accuracy, precision, sensitivity, and F1-score) on individual NSRDB and MIT-ARR databases. Meanwhile, the performance remained high, reaching more than 99.6% on mixed datasets that contain larger populations and more diverse conditions. The impressive performance demonstrated in both small and large subject groups emphasizes the model's scalability and potential for widespread implementation, particularly in security contexts where timely authentication is crucial. For future research, we need to examine the incorporation of multimodal biometric systems and extend the applicability of the framework to real-time environments and larger populations.

Sunflower Pollen-Morphology Mimicked Spiky Zinc Nanomotors as a Photosensitizer for Killing Bacteria and Cancer Cells.

Photosensitizing agents have received increased attention from the medical community, owing to their higher photothermal efficiency, induction of hyperthermia, and sustained delivery of bioactive molecules to their targets. Micro/nanorobots can be used as ideal photosensitizing agents by utilizing various physical stimuli for the targeted killing of pathogens (e.g., bacteria) and cancer cells. Herein, we report sunflower-pollen-inspired spiky zinc oxide (s-ZnO)-based nanorobots that effectively kill bacteria and cancer cells under near-infrared (NIR) light irradiation. The as-fabricated s-ZnO was modified with a catechol-containing photothermal agent, polydopamine (PDA), to improve its NIR-responsive properties, followed by the addition of antimicrobial (e.g., tetracycline/TCN) and anticancer (e.g., doxorubicin/DOX) drugs. The fabricated s-ZnO/PDA@Drug nanobots exhibited unique locomotory behavior with an average speed ranging from 13 to 14 µm/s under 2.0 W/cm2 NIR light irradiation. Moreover, the s-ZnO/PDA@TCN nanobots exhibited superior antibacterial activity against E. coli and S. epidermidis under NIR irradiation. The s-ZnO/PDA@DOX nanobots also displayed sufficient reactive oxygen species (ROS) amplification in B16F10 melanoma cells and induced apoptosis under NIR light, indicating their therapeutic efficacy. We hope the sunflower pollen-inspired s-ZnO nanorobots have tremendous potential in biomedical engineering from the phototherapy perspective, with the hope to reduce pathogen infections.

The Application of Fluorescence In Situ Hybridization in the Prescreening of Veronica Hybrids.

Fluorescence in situ hybridization (FISH), a molecular cytogenetic technique that enables the visualization and identification of specific DNA sequences within chromosomes, has emerged as a pivotal tool in plant breeding programs, particularly in the case of Veronica species. Veronica, a genus with a complex reproductive system, often poses challenges in accurately identifying hybrids because of its tendency to hybridize, which leads to intricate genetic variation. This study focused on the use of FISH as a prescreening method to identify true hybrids in Veronica breeding programs. FISH analysis was first performed on the parents to identify their 45S and 5S rDNA signals, along with their respective chromosome numbers. The signals were then compared with those of the twenty progenies with reference to their supposed parents. Five true hybrids, seven self-pollinated progenies, and eight false hybrids were identified through FISH. The findings highlight the significance of FISH as a screening method that contributes significantly to the efficiency of Veronica breeding programs by ensuring the preservation of desired genetic traits and minimizing the inadvertent inclusion of misidentified hybrids. To conclude, this study underscores the vital role of FISH in enhancing the precision and success of breeding programs and opens new avenues for improved breeding strategies and crop development.

Evaluation of cardiac pro-arrhythmic risks using the artificial neural network with ToR-ORd in silico model output.

Torsades de pointes (TdP) is a type of ventricular arrhythmia that can lead to sudden cardiac death. Drug-induced TdP has been an important concern for researchers and international regulatory boards. The Comprehensive in vitro Proarrhythmia Assay (CiPA) initiative was proposed that integrates in vitro testing and computational models of cardiac ion channels and human cardiomyocyte cells to evaluate the proarrhythmic risk of drugs. The TdP risk classification performance using only a single TdP metric may require some improvements because of information limitations and the instability of generalizing results. This study evaluates the performance of TdP metrics from the in silico simulations of the Tomek-O'Hara Rudy (ToR-ORd) ventricular cell model for classifying the TdP risk of drugs. We utilized these metrics as an input to an artificial neural network (ANN)-based classifier. The ANN model was optimized through hyperparameter tuning using the grid search (GS) method to find the optimal model. The study outcomes show an area under the curve (AUC) value of 0.979 for the high-risk category, 0.791 for the intermediate-risk category, and 0.937 for the low-risk category. Therefore, this study successfully demonstrates the capability of the ToR-ORd ventricular cell model in classifying the TdP risk into three risk categories, providing new insights into TdP risk prediction methods.

PAI-1 transfected-conditioned media promotes osteogenic differentiation of hBMSCs.

Reconstruction of injured bone remains challenging in the clinic owing to the lack of suitable bone grafts. The utilization of PAI-1 transfected-conditioned media (P-CM) has demonstrated its ability to facilitate the differentiation process of mesenchymal stem cells (MSCs), potentially serving as a crucial mediator in tissue regeneration. This research endeavored to explore the therapeutic potential of P-CM concerning the differentiation of human bone marrow mesenchymal stem cells (hBMSCs). To assess new bone formation, a rat calvaria critical defect model was employed, while in vitro experiments involved the use of the alizarin Red-S mineral induction test. In the rat calvaria critical defect model, P-CM treatment resulted in significan new bone formation. In vitro, P-CM treated hBMSCs displayed robust osteogenesis compared to the control group, as demonstrated by the mineral induction test using alizarin Red-S. P-CM with hydroxyapatite/ß-tricalcium phosphate/fibrin gel treatment significantly exhibited new bone formation, and the expression of osteogenic associated markers was enhanced in the P-CM-treated group. In conclusion, results demonstrate that P-CM treatment significantly enhanced the osteogenic differantiation efficiency and new bone formation, thus could be used as an ideal therapeutic biomolecule for constructing bone-specific implants, especially for orthopedic and dental applications.

Stimuli-Mediated Macrophage Switching, Unraveling the Dynamics at the Nanoplatforms-Macrophage Interface.

Macrophages play an essential role in immunotherapy and tissue regeneration owing to their remarkable plasticity and diverse functions. Recent bioengineering developments have focused on using external physical stimuli such as electric and magnetic fields, temperature, and compressive stress, among others, on micro/nanostructures to induce macrophage polarization, thereby increasing their therapeutic potential. However, it is difficult to find a concise review of the interaction between physical stimuli, advanced micro/nanostructures, and macrophage polarization. This review examines the present research on physical stimuli-induced macrophage polarization on micro/nanoplatforms, emphasizing the synergistic role of fabricated structure and stimulation for advanced immunotherapy and tissue regeneration. A concise overview of the research advancements investigating the impact of physical stimuli, including electric fields, magnetic fields, compressive forces, fluid shear stress, photothermal stimuli, and multiple stimulations on the polarization of macrophages within complex engineered structures, is provided. The prospective implications of these strategies in regenerative medicine and immunotherapeutic approaches are highlighted. This review will aid in creating stimuli-responsive platforms for immunomodulation and tissue regeneration.

Recent advances and biomedical application of 3D printed nanocellulose-based adhesive hydrogels: A review.

Nanocellulose-based tissue adhesives show promise for achieving rapid hemostasis and effective wound healing. Conventional methods, such as sutures and staples, have limitations, prompting the exploration of bioadhesives for direct wound adhesion and minimal tissue damage. Nanocellulose, a hydrolysis product of cellulose, exhibits superior biocompatibility and multifunctional properties, gaining interest as a base material for bioadhesive development. This study explores the potential of nanocellulose-based adhesives for hemostasis and wound healing using 3D printing techniques. Nanocellulose enables the creation of biodegradable adhesives with minimal adverse effects and opens avenues for advanced wound healing and complex tissue regeneration, such as skin, blood vessels, lungs, cartilage, and muscle. This study reviews recent trends in various nanocellulose-based 3D printed hydrogel patches for tissue engineering applications. The review also introduces various types of nanocellulose and their synthesis, surface modification, and bioadhesive fabrication techniques via 3D printing for smart wound healing.

Unzipped carbon nanotubes assisted 3D printable functionalized chitosan hydrogels for strain sensing applications.

Developing multifunctional hydrogels for wearable strain sensors has received significant attention due to their diverse applications, including human motion detection, personalized healthcare, soft robotics, and human-machine interfaces. However, integrating the required characteristics into one component remains challenging. To overcome these limitations, we synthesized multifunctional hydrogels using carboxymethyl chitosan (CMCS) and unzipped carbon nanotubes (f-CNTs) as strain sensor via a one-pot strategy. The polar groups in CMCS and f-CNTs enhance the properties of the hydrogels through different interactions. The hydrogels show superior printability with a uniformity factor (U) of 0.996 ± 0.049, close to 1. The f-CNTs-assisted hydrogels showed improved storage modulus (8.8 × 105 Pa) than the pure polymer hydrogel. The hydrogels adequately adhered to different surfaces, including human skin, plastic, plastic/glass interfaces, and printed polymers. The hydrogels demonstrated rapid self-healing and good conductivity. The biocompatibility of the hydrogels was assessed using human fibroblast cells. No adverse effects were observed with hydrogels, showing their biocompatibility. Furthermore, hydrogels exhibited antibacterial potential against Escherichia coli. The developed hydrogel exhibited unidirectional motion and complex letter recognition potential with a strain sensitivity of 2.4 at 210 % strain. The developed hydrogels could explore developing wearable electronic devices for detecting human motion.

Manufacturing 3D Biomimetic Tissue: A Strategy Involving the Integration of Electrospun Nanofibers with a 3D-Printed Framework for Enhanced Tissue Regeneration.

3D printing and electrospinning are versatile techniques employed to produce 3D structures, such as scaffolds and ultrathin fibers, facilitating the creation of a cellular microenvironment in vitro. These two approaches operate on distinct working principles and utilize different polymeric materials to generate the desired structure. This review provides an extensive overview of these techniques and their potential roles in biomedical applications. Despite their potential role in fabricating complex structures, each technique has its own limitations. Electrospun fibers may have ambiguous geometry, while 3D-printed constructs may exhibit poor resolution with limited mechanical complexity. Consequently, the integration of electrospinning and 3D-printing methods may be explored to maximize the benefits and overcome the individual limitations of these techniques. This review highlights recent advancements in combined techniques for generating structures with controlled porosities on the micro-nano scale, leading to improved mechanical structural integrity. Collectively, these techniques also allow the fabrication of nature-inspired structures, contributing to a paradigm shift in research and technology. Finally, the review concludes by examining the advantages, disadvantages, and future outlooks of existing technologies in addressing challenges and exploring potential opportunities.

Extracellular Matrix-Bioinspired Anisotropic Topographical Cues of Electrospun Nanofibers: A Strategy of Wound Healing through Macrophage Polarization.

The skin serves as the body's outermost barrier and is the largest organ, providing protection not only to the body but also to various internal organs. Owing to continuous exposure to various external factors, it is susceptible to damage that can range from simple to severe, including serious types of wounds such as burns or chronic wounds. Macrophages play a crucial role in the entire wound-healing process and contribute significantly to skin regeneration. Initially, M1 macrophages infiltrate to phagocytose bacteria, debris, and dead cells in fresh wounds. As tissue repair is activated, M2 macrophages are promoted, reducing inflammation and facilitating restoration of the dermis and epidermis to regenerate the tissue. This suggests that extracellular matrix (ECM) promotes cell adhesion, proliferation, migrationand macrophage polarization. Among the numerous strategies, electrospinning is a versatile technique for obtaining ECM-mimicking structures with anisotropic and isotropic topologies of micro/nanofibers. Various electrospun biomaterials influence macrophage polarization based on their isotropic or anisotropic topologies. Moreover, these fibers possess a high surface-area-to-volume ratio, promoting the effective exchange of vital nutrients and oxygen, which are crucial for cell viability and tissue regeneration. Micro/nanofibers with diverse physical and chemical properties can be tailored to polarize macrophages toward skin regeneration and wound healing, depending on specific requirements. This review describes the significance of micro/nanostructures for activating macrophages and promoting wound healing.

Stimuli-Responsive 3D Printable Conductive Hydrogel: A Step toward Regulating Macrophage Polarization and Wound Healing.

Conductive hydrogels (CHs) are promising alternatives for electrical stimulation of cells and tissues in biomedical engineering. Wound healing and immunomodulation are complex processes that involve multiple cell types and signaling pathways. 3D printable conductive hydrogels have emerged as an innovative approach to promote wound healing and modulate immune responses. CHs can facilitate electrical and mechanical stimuli, which can be beneficial for altering cellular metabolism and enhancing the efficiency of the delivery of therapeutic molecules. This review summarizes the recent advances in 3D printable conductive hydrogels for wound healing and their effect on macrophage polarization. This report also discusses the properties of various conductive materials that can be used to fabricate hydrogels to stimulate immune responses. Furthermore, this review highlights the challenges and limitations of using 3D printable CHs for future material discovery. Overall, 3D printable conductive hydrogels hold excellent potential for accelerating wound healing and immune responses, which can lead to the development of new therapeutic strategies for skin and immune-related diseases.

Mind In Vitro Platforms: Versatile, Scalable, Robust, and Open Solutions to Interfacing with Living Neurons.

Motivated by the unexplored potential of in vitro neural systems for computing and by the corresponding need of versatile, scalable interfaces for multimodal interaction, an accurate, modular, fully customizable, and portable recording/stimulation solution that can be easily fabricated, robustly operated, and broadly disseminated is presented. This approach entails a reconfigurable platform that works across multiple industry standards and that enables a complete signal chain, from neural substrates sampled through micro-electrode arrays (MEAs) to data acquisition, downstream analysis, and cloud storage. Built-in modularity supports the seamless integration of electrical/optical stimulation and fluidic interfaces. Custom MEA fabrication leverages maskless photolithography, favoring the rapid prototyping of a variety of configurations, spatial topologies, and constitutive materials. Through a dedicated analysis and management software suite, the utility and robustness of this system are demonstrated across neural cultures and applications, including embryonic stem cell-derived and primary neurons, organotypic brain slices, 3D engineered tissue mimics, concurrent calcium imaging, and long-term recording. Overall, this technology, termed "mind in vitro" to underscore the computing inspiration, provides an end-to-end solution that can be widely deployed due to its affordable (>10× cost reduction) and open-source nature, catering to the expanding needs of both conventional and unconventional electrophysiology.

Intracerebral Nanoparticle Transport Facilitated by Alzheimer Pathology and Age.

Nanoparticles have emerged as potential transporters of drugs targeting Alzheimer's disease (AD), but their design should consider the blood-brain barrier (BBB) integrity and neuroinflammation of the AD brain. This study presents that aging is a significant factor for the brain localization and retention of nanoparticles, which we engineered to bind with reactive astrocytes and activated microglia. We assembled 200 nm-diameter particles using a block copolymer of poly(lactic-co-glycolic acid) (PLGA) and CD44-binding hyaluronic acid (HA). The resulting PLGA-b-HA nanoparticles displayed increased binding to CD44-expressing reactive astrocytes and activated microglia. Upon intravascular injection, nanoparticles were localized to the hippocampi of both APP/PS1 AD model mice and their control littermates at 13-16 months of age due to enhanced transvascular transport through the leaky BBB. No particles were found in the hippocampi of young adult mice. These findings demonstrate the brain localization of nanoparticles due to aging-induced BBB breakdown regardless of AD pathology.

A Review on Electroactive Polymer-Metal Composites: Development and Applications for Tissue Regeneration.

Electroactive polymer-metal composites (EAPMCs) have gained significant attention in tissue engineering owing to their exceptional mechanical and electrical properties. EAPMCs develop by combining an electroactive polymer matrix and a conductive metal. The design considerations include choosing an appropriate metal that provides mechanical strength and electrical conductivity and selecting an electroactive polymer that displays biocompatibility and electrical responsiveness. Interface engineering and surface modification techniques are also crucial for enhancing the adhesion and biocompatibility of composites. The potential of EAPMC-based tissue engineering revolves around its ability to promote cellular responses, such as cell adhesion, proliferation, and differentiation, through electrical stimulation. The electrical properties of these composites can be used to mimic natural electrical signals within tissues and organs, thereby aiding tissue regeneration. Furthermore, the mechanical characteristics of the metallic components provide structural reinforcement and can be modified to align with the distinct demands of various tissues. EAPMCs have extraordinary potential as regenerative biomaterials owing to their ability to promote beneficial effects in numerous electrically responsive cells. This study emphasizes the characteristics and applications of EAPMCs in tissue engineering.

Machine learning approach to evaluate TdP risk of drugs using cardiac electrophysiological model including inter-individual variability.

Introduction: Predicting ventricular arrhythmia Torsade de Pointes (TdP) caused by drug-induced cardiotoxicity is essential in drug development. Several studies used single biomarkers such as qNet and Repolarization Abnormality (RA) in a single cardiac cell model to evaluate TdP risk. However, a single biomarker may not encompass the full range of factors contributing to TdP risk, leading to divergent TdP risk prediction outcomes, mainly when evaluated using unseen data. We addressed this issue by utilizing multi-in silico features from a population of human ventricular cell models that could capture a representation of the underlying mechanisms contributing to TdP risk to provide a more reliable assessment of drug-induced cardiotoxicity. Method: We generated a virtual population of human ventricular cell models using a modified O'Hara-Rudy model, allowing inter-individual variation. IC50 and Hill coefficients from 67 drugs were used as input to simulate drug effects on cardiac cells. Fourteen features (dVmdtrepol, dVmdtmax, Vmpeak, Vmresting, APDtri, APD90, APD50, Capeak, Cadiastole, Catri, CaD90, CaD50, qNet, qInward) could be generated from the simulation and used as input to several machine learning models, including k-nearest neighbor (KNN), Random Forest (RF), XGBoost, and Artificial Neural Networks (ANN). Optimization of the machine learning model was performed using a grid search to select the best parameter of the proposed model. We applied five-fold cross-validation while training the model with 42 drugs and evaluated the model's performance with test data from 25 drugs. Result: The proposed ANN model showed the highest performance in predicting the TdP risk of drugs by providing an accuracy of 0.923 (0.908-0.937), sensitivity of 0.926 (0.909-0.942), specificity of 0.921 (0.906-0.935), and AUC score of 0.964 (0.954-0.975). Discussion and conclusion: According to the performance results, combining the electrophysiological model including inter-individual variation and optimization of machine learning showed good generalization ability when evaluated using the unseen dataset and produced a reliable drug-induced TdP risk prediction system.

Polyphenolic Carbon Quantum Dots with Intrinsic Reactive Oxygen Species Amplification for Two-Photon Bioimaging and In Vivo Tumor Therapy.

Recent studies indicate that mitochondrial dysfunctions and DNA damage have a critical influence on cell survival, which is considered one of the therapeutic targets for cancer therapy. In this study, we demonstrated a comparative study of the effect of polyphenolic carbon quantum dots (CQDs) on in vitro and in vivo antitumor efficacy. Dual emissive (green and yellow) shape specific polyphenolic CQDs (G-CQDs and Y-CQDs) were synthesized from easily available nontoxic precursors (phloroglucinol), and the antitumor property of the as-synthesized probe was investigated as compared to round-shaped blue emissive CQDs (B-CQDs) derived from well-reported precursor citric acid and urea. The B-CQDs had a nuclei-targeting property, and G-CQDs and Y-CQDs had mitochondria-targeting properties. We have found that the polyphenol containing CQDs (at a dose of 100 µg mL-1) specifically attack mitochondria by excess accumulation, altering the metabolism, inhibiting branching pattern, imbalanced Bax/Bcl-2 homeostasis, and ultimately generating oxidative stress levels, leading to oxidative stress-induced cell death in cancer cells in vitro. We show that G-CQDs are the main cause of oxidative stress in cancer cells because of their ability to produce sufficient •OH- and 1O2 radicals, evidenced by electron paramagnetic resonance spectroscopy and a terephthalic acid test. Moreover, the near-infrared absorption properties of the CQDs were exhibited in two-photon (TP) emission, which was utilized for TP cellular imaging of cancer cells without photobleaching. The in vivo antitumor test further discloses that intratumoral injection of G-CQDs can significantly augment the treatment efficacy of subcutaneous tumors without any adverse effects on BalB/c nude mice. We believe that shape-specific polyphenolic CQD-based nanotheranostic agents have a potential role in tumor therapy, thus proving an insight on treatment of malignant cancers.

Trackable and highly fluorescent nanocellulose-based printable bio-resins for image-guided tissue regeneration.

Dynamic tracking of cell migration during tissue regeneration remains challenging owing to imaging techniques that require sophisticated devices, are often lethal to healthy tissues. Herein, we developed a 3D printable non-invasive polymeric hydrogel based on 2,2,6,6-(tetramethylpiperidin-1-yl) oxyl (TEMPO)-oxidized nanocellulose (T-CNCs) and carbon dots (CDs) for the dynamic tracking of cells. The as-prepared T-CNC@CDs were used to fabricate a liquid bio-resin containing gelatin methacryloyl (GelMA) and polyethylene glycol diacrylate (GPCD) for digital light processing (DLP) bioprinting. The shear-thinning properties of the GPCD bio-resin were further improved by the addition of T-CNC@CDs, allowing high-resolution 3D printing and bioprinting of human cells with higher cytocompatibility (viability ∼95 %). The elastic modulus of the printed GPCD hydrogel was found to be ∼13 ± 4.2 kPa, which is ideal for soft tissue engineering. The as-fabricated hydrogel scaffold exhibited tunable structural color property owing to the addition of T-CNC@CDs. Owing to the unique fluorescent property of T-CNC@CDs, the human skin cells could be tracked within the GPCD hydrogel up to 30 days post-printing. Therefore, we anticipate that GPCD bio-resin can be used for 3D bioprinting with high structural stability, dynamic tractability, and tunable mechanical stiffness for image-guided tissue regeneration.