Enzymatic post-crosslinking of printed hydrogels of methacrylated gelatin and tyramine-conjugated 8-arm poly(ethylene glycol) to prepare interpenetrating 3D network structures

Methacrylated gelatin (GelMA) has been intensively studied as a 3D printable scaffold material in tissue regeneration fields, which can be attributed to its well-known biological functions. However, the long-term stability of photo-crosslinked GelMA scaffolds is hampered by a combination of its fast degradation in the presence of collagenase and the loss of physical crosslinks at higher temperatures. To increase the longer-term shape stability of printed scaffolds, a mixture of GelMA and tyramine-conjugated 8-arm PEG (8PEGTA) was used to create filaments composed of an interpenetrating network (IPN). Photo-crosslinking during filament deposition of the GelMA and subsequent enzymatic crosslinking of the 8PEGTA were applied to the printed 3D scaffolds. Although both crosslinking mechanisms are radical based, they operate without interference of each other. Rheological data of bulk hydrogels showed that the IPN was an elastic hydrogel, having a storage modulus of 6 kPa, independent of temperature in the range of 10 – 40°C. Tensile and compression moduli were 110 kPa and 80 kPa, respectively. On enzymatic degradation in the presence of collagenase, the gelatin content of the IPN fully degraded in 7 days, leaving a stable secondary crosslinked 8PEGTA network. Using a BioMaker bioprinter, hydrogels without and with human osteosarcoma cells (hMG-63) were printed. On culturing for 21 days, hMG-63 in the GelMA/8PEGTA IPN showed a high cell viability (&gt;90%). Thus, the presence of the photoinitiator, incubation with H2O2, and mechanical forces during printing did not hamper cell viability. This study shows that the GelMA/8PEGTA ink is a good candidate to generate cell-laden bioinks for extrusion-based printing of constructs for tissue engineering applications.</p

Comparative analysis of tissue-specific genes in maize based on machine learning models: CNN performs technically best, LightGBM performs biologically soundest

Introduction: With the advancement of RNA-seq technology and machine learning, training large-scale RNA-seq data from databases with machine learning models can generally identify genes with important regulatory roles that were previously missed by standard linear analytic methodologies. Finding tissue-specific genes could improve our comprehension of the relationship between tissues and genes. However, few machine learning models for transcriptome data have been deployed and compared to identify tissue-specific genes, particularly for plants.Methods: In this study, an expression matrix was processed with linear models (Limma), machine learning models (LightGBM), and deep learning models (CNN) with information gain and the SHAP strategy based on 1,548 maize multi-tissue RNA-seq data obtained from a public database to identify tissue-specific genes. In terms of validation, V-measure values were computed based on k-means clustering of the gene sets to evaluate their technical complementarity. Furthermore, GO analysis and literature retrieval were used to validate the functions and research status of these genes.Results: Based on clustering validation, the convolutional neural network outperformed others with higher V-measure values as 0.647, indicating that its gene set could cover as many specific properties of various tissues as possible, whereas LightGBM discovered key transcription factors. The combination of three gene sets produced 78 core tissue-specific genes that had previously been shown in the literature to be biologically significant.Discussion: Different tissue-specific gene sets were identified due to the distinct interpretation strategy for machine learning models and researchers may use multiple methodologies and strategies for tissue-specific gene sets based on their goals, types of data, and computational resources. This study provided comparative insight for large-scale data mining of transcriptome datasets, shedding light on resolving high dimensions and bias difficulties in bioinformatics data processing

Bifunctional bone substitute materials for bone defect treatment after bone tumor resection

Aggressive benign, malignant and metastatic bone tumors can greatly decrease the quality of patients’ lives and even lead to substantial mortality. Several clinical therapeutic strategies have been developed to treat bone tumors, including preoperative chemotherapy, surgical resection of the tumor tissue, and subsequent systemic chemo- or radiotherapy. However, those strategies are associated with inevitable drawbacks, such as severe side effects, substantial local tumor recurrence, and difficult-to-treat bone defects after tumor resection. To overcome these shortcomings and achieve satisfactory clinical outcomes, advanced bifunctional biomaterials which simultaneously promote bone regeneration and combat bone tumor growth are increasingly advocated. These bifunctional bone substitute materials fill bone defects following bone tumor resection and subsequently exert local anticancer effects. Here we describe various types of the most prevalent bone tumors and provide an overview of common treatment options. Subsequently, we review current progress regarding the development of bifunctional bone substitute materials combining osteogenic and anticancer efficacy. To this end, we categorize these biomaterials based on their anticancer mechanism deriving from i) intrinsic biomaterial properties, ii) local drug release of anticancer agents, and iii) oxidative stress-inducing and iv) hyperthermia-inducing biomaterials. Consequently, this review offers researchers, surgeons and oncologists an up-to-date overview of our current knowledge on bone tumors, their treatment options, and design of advanced bifunctional biomaterials with strong potential for clinical application in oncological orthopedics

Dual-purpose porous Polymethylmethacrylate Cement Loaded with Cisplatin Kills Bone Tumor Cells

Malignant bone tumors are usually treated by resection of tumor tissue followed by filling of the bone defect with bone graft substitutes. Polymethylmethacrylate (PMMA) cement is the most commonly used bone substitute in clinical orthopedics in view of its reliability. However, the dense nature of PMMA renders this biomaterial unsuitable for local delivery of chemotherapeutic drugs to limit the recurrence of bone tumors. Here, we introduce porosity into PMMA cement by adding carboxymethylcellulose (CMC) to facilitate such local delivery of chemotherapeutic drugs, while retaining sufficient mechanical properties for bone reconstruction in load-bearing sites. Our results show that the mechanical strength of PMMA-based cements gradually decreases with increasing CMC content. Upon incorporation of ≥3% CMC, the PMMA-based cements released up to 18% of the loaded cisplatin, in contrast to cements containing lower amounts of CMC which only released less than 2% of the cisplatin over 28 days. This release of cisplatin efficiently killed osteosarcoma cells in vitro and the fraction of dead cells increased to 91.3% at day 7, which confirms the retained chemotherapeutic activity of released cisplatin from these PMMA-based cements. Additionally, tibias filled with PMMA-based cements containing up to 3% of CMC exhibit comparable compressive strengths as compared to intact tibias. In conclusion, we demonstrate that PMMA cements can be rendered therapeutically active by introducing porosity using CMC to allow for release of cisplatin without compromising mechanical properties beyond critical levels. As such, these data suggest that our dual-functional PMMA-based cements represent a viable treatment option for filling bone defects after bone tumor resection in load-bearing sites