Cartilage injuries, due to their limited regenerative capacity, often result in chronic pain and functional impairment. These injuries are difficult to manage with conventional surgical repair techniques; therefore, alternative treatments are necessary. Gelatin methacrylate (GelMA) has emerged as a promising biomaterial for cartilage tissue engineering due to its biocompatibility, tunable mechanical properties, and ability to be used in advanced applications like 3D bioprinting. This review examines the synthesis, properties, and limitations of GelMA in cartilage repair, focusing on its applications in 3D bioprinting for the creation of patient-specific cartilage constructs. It also highlights preclinical studies exploring the potential of GelMA-based scaffolds in various animal models. Despite its advantages, challenges remain, such as the mechanical limitations of GelMA and its degradation rate in dynamic environments. Hybrid scaffolds, in situ bioprinting, and personalized bioinks offer solutions to these issues. Ultimately, long-term clinical trials are needed to assess the durability and efficacy of GelMA-based scaffolds in human applications. Future research is aimed at overcoming these challenges, improving the mechanical strength of GelMA scaffolds, and enhancing their clinical translation for cartilage repair.

Acute myeloid leukemia (AML) is a leading blood cancer subtype that can be caused by 27 gene mutations. Previous studies have explored potential vaccine and drug treatments against AML, but many were proven immunologically insignificant. Here, we targeted this issue and applied various clinical filters to improve immune response. KIT is an oncogenic gene that can cause AML when mutated and is predicted to be a promising vaccine target because of its immunogenic responses when activated. We designed a multi-epitope vaccine targeting mutations in the KIT oncogene using CD8+ and CD4+ epitopes. We selected the most viable vaccine epitopes based on thresholds for percentile rank, immunogenicity, antigenicity, half-life, toxicity, IFNγ release, allergenicity, and stability. The efficacy of data was observed through world and regional population coverage of our vaccine design. Then, we obtained epitopes for optimized population coverage from PCOptim-CD, a modified version of our original Java-based program code PCOptim. Using 24 mutations on the KIT gene, 12 CD8+ epitopes and 21 CD4+ epitopes were obtained. The CD8+ dataset had a 98.55% world population coverage, while the CD4+ dataset had a 65.14% world population coverage. There were five CD4+ epitopes that overlapped with the top CD8+ epitopes. Strong binding to murine MHC molecules was found in four CD8+ and six CD4+ epitopes, demonstrating the feasibility of our results in preclinical murine vaccine trials. We then created three-dimensional (3D) models to visualize epitope–MHC complexes and TCR interactions. The final candidate is a non-toxic and non-allergenic multi-epitope vaccine against KIT mutations that cause AML. Further research would involve murine trials of the vaccine candidates on tumor cells causing AML.

Despite improvements in short-term and long-term outcomes of liver transplant patients, the discrepancy between the number of available livers and transplant candidates continues to increase. The use of expanded criteria donors is one strategy that can be used to address donor shortages. In recent years, preservation strategies such as normothermic machine perfusion (NMP) have been explored to improve the preservation of organs and test their viability before transplantation. We reviewed the recent literature and trials assessing the use of NMP in the setting of liver transplantation. Multiple feasibility trials have demonstrated the clinical prospect of NMP and proved its numerous advantages compared to conventional static cold storage. These advantages include preservation and viability assessment of high-risk donor allografts and grafts that would have otherwise been discarded. This review aims to address the topic of liver NMP in the setting of current and future applications in the setting of extended criteria donor grafts.

Personalized cancer vaccines have emerged as a promising avenue for cancer treatment or prevention strategies. This approach targets the specific genetic alterations in individual patient’s tumors, offering a more personalized and effective treatment option. Previous studies have shown that generalized peptide vaccines targeting a limited scope of gene mutations were ineffective, emphasizing the need for personalized approaches. While studies have explored personalized mRNA vaccines, personalized peptide vaccines have not yet been studied in this context. Pancreatic ductal adenocarcinoma (PDAC) remains challenging in oncology, necessitating innovative therapeutic strategies. In this study, we developed a personalized peptide vaccine design methodology, employing RNA sequencing (RNAseq) to identify prevalent gene mutations underlying PDAC development in a patient solid tumor tissue. We performed RNAseq analysis for trimming adapters, read alignment, and somatic variant calling. We also developed a Python program called SCGeneID, which validates the alignment of the RNAseq analysis. The Python program is freely available to download. Using chromosome number and locus data, SCGeneID identifies the target gene along the UCSC hg38 reference set. Based on the gene mutation data, we developed a personalized PDAC cancer vaccine that targeted 100 highly prevalent gene mutations in two patients. We predicted peptide-MHC binding affinity, immunogenicity, antigenicity, allergenicity, and toxicity for each epitope. Then, we selected the top 50 and 100 epitopes based on our previously published vaccine design methodology. Finally, we generated pMHC-TCR 3D molecular model complex structures, which are freely available to download. The designed personalized cancer vaccine contains epitopes commonly found in PDAC solid tumor tissue. Our personalized vaccine was composed of neoantigens, allowing for a more precise and targeted immune response against cancer cells. Additionally, we identified mutated genes, which were also found in the reference study, where we obtained the sequencing data, thus validating our vaccine design methodology. This is the first study designing a personalized peptide cancer vaccine targeting neoantigens using human patient data to identify gene mutations associated with the specific tumor of interest.

Previous epitope-based cancer vaccines have focused on analyzing a limited number of mutated epitopes and clinical variables preliminarily to experimental trials. As a result, relatively few positive clinical outcomes have been observed in epitope-based cancer vaccines. Further efforts are required to diversify the selection of mutated epitopes tailored to cancers with different genetic signatures. To address this, we developed the first version of AutoEpiCollect, a user-friendly GUI software, capable of generating safe and immunogenic epitopes from missense mutations in any oncogene of interest. This software incorporates a novel, machine learning-driven epitope ranking method, leveraging a probabilistic logistic regression model that is trained on experimental T-cell assay data. Users can freely download AutoEpiCollectGUI with its user guide for installing and running the software on GitHub. We used AutoEpiCollect to design a pan-cancer vaccine targeting missense mutations found in the proto-oncogene PIK3CA, which encodes the p110ɑ catalytic subunit of the PI3K kinase protein. We selected PIK3CA as our gene target due to its widespread prevalence as an oncokinase across various cancer types and its lack of presence as a gene target in clinical trials. After entering 49 distinct point mutations into AutoEpiCollect, we acquired 361 MHC Class I epitope/HLA pairs and 219 MHC Class II epitope/HLA pairs. From the 49 input point mutations, we identified MHC Class I epitopes targeting 34 of these mutations and MHC Class II epitopes targeting 11 mutations. Furthermore, to assess the potential impact of our pan-cancer vaccine, we employed PCOptim and PCOptim-CD to streamline our epitope list and attain optimized vaccine population coverage. We achieved a world population coverage of 98.09% for MHC Class I data and 81.81% for MHC Class II data. We used three of our predicted immunogenic epitopes to further construct 3D models of peptide-HLA and peptide-HLA-TCR complexes to analyze the epitope binding potential and TCR interactions. Future studies could aim to validate AutoEpiCollect’s vaccine design in murine models affected by PIK3CA-mutated or other mutated tumor cells located in various tissue types. AutoEpiCollect streamlines the preclinical vaccine development process, saving time for thorough testing of vaccinations in experimental trials.

Normothermic regional perfusion (NRP) has emerged as a pivotal strategy in controlled donation after circulatory death (cDCD) liver transplantation, mitigating ischemia‐reperfusion injury and improving graft outcomes. Compared to super rapid recovery (SRR), NRP significantly reduces rates of early allograft dysfunction, primary nonfunction, and biliary non‐anastomotic stricture (NAS), with biliary complications reported in 5%–16% and NAS as low as 0%–2%. Outcomes from cDCD‐NRP grafts are increasingly comparable to those of donation after brain death (DBD). Viability assessment during NRP remains variably defined across centers. Nonetheless, stable pump flow, stable or declining lactate levels, controlled transaminase levels, and favorable macroscopic appearance are commonly used parameters. Histological thresholds may guide graft acceptance but are not universally applied. Sequential use of ex situ machine perfusion following NRP offers additional benefits in marginal or prolonged ischemia settings. NRP implementation has improved liver utilization rates from 34% to 63% in the United Kingdom and from 39% to 71% in the United States. This review highlights NRP as a transformative platform that reshapes viability standards, expanding transplant access, and supports sustained growth in liver transplantation.