Remendable Cross-Linked Alginate/Gelatin Hydrogels Incorporating Nanofibers for Wound Repair and Regeneration.

Wound dressings made from natural-derived polymers are highly valued for their biocompatibility, biodegradability, and biofunctionality. However, natural polymer-based hydrogels can come with their own set of limitations, such as low mechanical strength, limited cell affinity, and the potential cytotoxicity of cross-linkers, which delineate the boundaries of their usage and hamper their practical application. To overcome the limitation of natural-derived polymers, this study utilized a mixture of oxidized alginate and gelatin with 5 mg/mL polycaprolactone (PCL):gelatin nanofiber fragments at a ratio of 7:3 (OGN-7) to develop a hydrogel composite wound dressing that can be injected and has the ability to be remended. The in situ formation of the remendable hydrogel is facilitated by dual cross-linking of oxidized alginate chains with gelatin and PCL/gelatin nanofibers through Schiff-base mechanisms, supported by the physical integration of nanofibers, thereby obviating the need for additional cross-linking agents. Furthermore, OGN-7 exhibits increased stiffness (γ = 79.4-316.3%), reduced gelation time (543 ± 5 to 475 ± 5 s), improved remendability of the hydrogel, and excellent biocompatibility. Notably, OGN-7 achieves full fusion within 1 h of incubation and maintains structural integrity under external stress, effectively overcoming the inherent mechanical weaknesses of natural polymer-based dressings and enhancing biofunctionality. The therapeutic efficacy of OGN-7 was validated through a full-thickness in vivo wound healing analysis, which demonstrated that OGN-7 significantly accelerates wound closure compared to alginate-based dressings and control groups. Histological analysis further revealed that re-epithelialization and collagen deposition were markedly enhanced in the regenerating skin of the OGN-7 group, confirming the superior therapeutic performance of OGN-7. In summary, OGN-7 optimized the synergistic effects of natural polymers, which enhances their collective functionality as a wound dressing and expands their utility across diverse biomedical applications.

3D in vitro synovial hyperplasia model on polycaprolactone-micropatterned nanofibrous microwells for screening disease-modifying anti-rheumatic drugs.

Rheumatoid arthritis (RA) is known to be caused by autoimmune disorders and can be partially alleviated through Disease-Modifying Antirheumatic Drugs (DMARDs) therapy. However, due to significant variations in the physical environment and condition of each RA patient, the types and doses of DMARDs prescribed can differ greatly. Consequently, there is a need for a platform based on patient-derived cells to determine the effectiveness of specific DMARDs for individual patient. In this study, we established an RA three-dimensional (3D) spheroid that mimics the human body's 3D environment, enabling high-throughput assays by culturing patient-derived synovial cells on a macroscale-patterned polycaprolactone (PCL) scaffold. Fibroblast-like synoviocytes (FLSs) from patient and human umbilical vein endothelial cells (HUVECs) were co-cultured to simulate vascular delivery. Additionally, RA characteristics were identified at both the genetic and cytokine levels using real-time polymerase chain reaction (RT-qPCR) and dot blot assay. The similarities in junctions and adhesion were demonstrated in both actual RA patient tissues and 3D spheroids. The 3D RA spheroid was treated with representative DMARDs, observing changes in reactive oxygen species (ROS) levels, lactate dehydrogenase (LDH) levels, and inflammatory cytokine responses to confirm the varying cell reactions depending on the DMARDs used. This study underscores the significance of the 3D drug screening platform, which can be applied to diverse inflammatory disease treatments as a personalized drug screening system. We anticipate that this platform will become an indispensable tool for advancing and developing personalized DMARD treatment strategies.

Synergistic Effect of Ferroptosis-Inducing Nanoparticles and X-Ray Irradiation Combination Therapy.

Ferroptosis, characterized by the induction of cell death via lipid peroxidation, has been actively studied over the last few years and has shown the potential to improve the efficacy of cancer nanomedicine in an iron-dependent manner. Radiation therapy, a common treatment method, has limitations as a stand-alone treatment due to radiation resistance and safety as it affects even normal tissues. Although ferroptosis-inducing drugs help alleviate radiation resistance, there are no safe ferroptosis-inducing drugs that can be considered for clinical application and are still in the research stage. Here, the effectiveness of combined treatment with radiotherapy with Fe and hyaluronic acid-based nanoparticles (FHA-NPs) to directly induce ferroptosis, considering the clinical applications is reported. Through the induction of ferroptosis by FHA-NPs and apoptosis by X-ray irradiation, the therapeutic efficiency of cancer is greatly improved both in vitro and in vivo. In addition, Monte Carlo simulations are performed to assess the physical interactions of the X-rays with the iron-oxide nanoparticle. The study provides a deeper understanding of the synergistic effect of ferroptosis and X-ray irradiation combination therapy. Furthermore, the study can serve as a valuable reference for elucidating the role and mechanisms of ferroptosis in radiation therapy.

Hydrogel/Nanofiber Composite Wound Dressing Optimized for Skin Layer Regeneration through the Mechanotransduction-Based Microcellular Environment.

Wound dressings have been designed to provide the optimal environment to fibroblasts, keratinocytes, and macrophages to promote wound healing while inhibiting potential microbial infection. Gelatin methacrylate (GelMA) is a photopolymerizable hydrogel with a gelatin backbone that contains natural cell binding motifs such as arginine-glycine-aspartic acid (RGD) and MMP-sensitive degradation sites, making it an ideal material for wound dressing. However, GelMA alone is unable to stably protect the wound and regulate cellular activities due to its weak mechanical properties and nonmicropatterned surface, limiting its application as a wound dressing. Herein, we report the development of a hydrogel-nanofiber composite wound dressing utilizing GelMA and poly(caprolactone) (PCL)/gelatin nanofiber, which can systematically manage the skin regeneration process with an enhanced mechanical property and micropatterned surface. GelMA sandwiched between electrospun aligned and interlaced nanofibers that mimic epidermis and dermis layers, respectively, increased the stiffness of the resulting hydrogel composite with a comparable swelling rate as GelMA. Fabricated hydrogel composite was determined to be biocompatible and nontoxic. In addition to the beneficial effect of GelMA in accelerating wound healing, subsequent histological analysis revealed upregulated re-epithelialization of granulation tissue and deposition of mature collagen. Hydrogel composite interacted with fibroblasts to regulate their morphology, proliferation, and collagen synthesis, as well as the expression of α-SMA, TGF-ß, and collagen I and III during the wound healing process both in vitro and in vivo. Taken together, we propose hydrogel/nanofiber composite as a wound dressing of the next generation that can induce skin tissue layer regeneration beyond the basic wound closure promotion of present dressings.

Isotropic conductive paste for bioresorbable electronics.

Bioresorbable implantable medical devices can be employed in versatile clinical scenarios that burden patients with complications and surgical removal of conventional devices. However, a shortage of suitable electricalinterconnection materials limits the development of bioresorbable electronic systems. Therefore, this study highlights a highly conductive, naturally resorbable paste exhibiting enhanced electrical conductivity and mechanical stability that can solve the existing problems of bioresorbable interconnections. Multifaceted experiments on electrical and physical properties were used to optimize the composition of pastes containing beeswax, submicron tungstenparticles, and glycofurol. These pastes embody isotropic conductive paths for three-dimensional interconnects and function as antennas, sensors, and contact pads for bioresorbable electronic devices. The degradation behavior in aqueous solutions was used to assess its stability and ability to retain electrical conductance (∼7 â€‹kS/m) and structural form over the requisite dissolution period. In vitro and in vivo biocompatibility tests clarified the safety of the paste as an implantable material.

Multibranched Au-Ag-Pt Nanoparticle as a Nanozyme for the Colorimetric Assay of Hydrogen Peroxide and Glucose.

Many studies have recently produced artificial enzymes with metal nanoparticles (NPs) to overcome the limitations of natural enzymes, such as low stability, high cost, and storage problems. In particular, gold NPs exhibit peroxidase-like activity and are strongly influenced by external parameters, such as pH, temperature, size, shape, and functional layer, which change the enzyme activity. Here, chitosan-capped multibranched Au-Ag-Pt NPs (CCNPs) that mimic peroxidase were synthesized using various peroxidase-mimicking strategies. The results demonstrated that enzyme activity sequentially increased because of the multibranched Au-Ag NPs coated with Pt and chitosan. The enzyme activity of the particle was evaluated through the oxidation of 3,3',5,5'-tetramethylbenzidine (TMB), which causes a color change into blue. This change was observable with the naked eye and could be used practically. The color change depended on the concentration of hydrogen peroxide (H2O2), and it was shown that the CCNPs could be applied to measure H2O2 with a limit of detection (LOD) of 0.054 mM. Furthermore, with glucose oxidase, the CCNPs can be used for glucose detection with an LOD of 0.289 mM. Also, the potential of the CCNP application in human serum was shown through the serum test. Thus, this study suggested the utilization of the multibranched Au-Ag-Pt NPs that mimic the peroxidase activity of natural enzymes and the possibility of application in various biological analyses.

Machine Learning Improves the Prediction Rate of Non-Curative Resection of Endoscopic Submucosal Dissection in Patients with Early Gastric Cancer.

Non-curative resection (NCR) of early gastric cancer (EGC) after endoscopic submucosal dissection (ESD) can increase the burden of additional treatment and medical expenses. We aimed to develop a machine-learning (ML)-based NCR prediction model for EGC prior to ESD. We obtained data from 4927 patients with EGC who underwent ESD between January 2006 and February 2020. Ten clinicopathological characteristics were selected using extreme gradient boosting (XGBoost) and were used to develop a ML-based model. Dataset was divided into the training and internal validation sets and verified using an external validation set. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) were evaluated. The performance of each model was compared by using the Delong test. A total of 1100 (22.1%) patients were identified as being treated non-curatively with ESD. Seven ML-based NCR prediction models were developed. The performance of NCR prediction was highest in the XGBoost model (AUROC, 0.851; 95% confidence interval, 0.837-0.864). When we compared the prediction performance by the Delong test, XGBoost (p = 0.02) and support vector machine (p = 0.02) models showed a significantly higher performance among the NCR prediction models. We developed an ML model capable of accurately predicting the NCR of EGC before ESD. This ML model can provide useful information for decision-making regarding the appropriate treatment of EGC before ESD.

Erythropoiesis stimulating agent recommendation model using recurrent neural networks for patient with kidney failure with replacement therapy.

In patients with kidney failure with replacement therapy (KFRT), optimizing anemia management in these patients is a challenging problem because of the complexities of the underlying diseases and heterogeneous responses to erythropoiesis-stimulating agents (ESAs). Therefore, we propose a ESA dose recommendation model based on sequential awareness neural networks. Data from 466 KFRT patients (12,907 dialysis sessions) in seven tertiary-care general hospitals were included in the experiment. First, a Hb prediction model was developed to simulate longitudinal heterogeneous ESA and Hb interactions. Based on the prediction model as a prospective study simulator, we built an ESA dose recommendation model to predict the required amount of ESA dose to reach a target hemoglobin level after 30 days. Each model's performance was evaluated in the mean absolute error (MAE). The MAEs presenting the best results of the prediction and recommendation model were 0.59 (95% confidence interval: 0.56-0.62) g/dL and 43.2 µg (ESAs dose), respectively. Compared to the results in the real-world clinical data, the recommendation model achieved a reduction of ESA dose (Algorithm: 140 vs. Human: 150 µg/month, P < 0.001), a more stable monthly Hb difference (Algorithm: 0.6 vs. Human: 0.8 g/dL, P < 0.001), and an improved target Hb success rate (Algorithm: 79.5% vs. Human: 62.9% for previous month's Hb < 10.0 g/dL; Algorithm: 95.7% vs. Human:73.0% for previous month's Hb 10.0-12.0 g/dL). We developed an ESA dose recommendation model for optimizing anemia management in patients with KFRT and showed its potential effectiveness in a simulated prospective study.