Advancing autologous CAR T-cell therapy through real-time patient health data integration: a simulation-based approach.

Autologous chimeric antigen receptor T-cell therapy presents promising treatment outcomes for various cancers. However, its potential is restrained by unique supply chain challenges, including dynamic patient health conditions and extended turnaround time. These challenges often lead to missed optimal treatment windows, impeding the effective delivery of life-saving treatments. This article presents SimPAC (simulation-based decision support for Patient-centric manufacturing of autologous cell therapies). SimPAC is designed to model and incorporate real-time patient health conditions into the supply chain decisions of autologous chimeric antigen receptor T-cell therapy. SimPAC integrates system dynamics and agent-based simulation techniques, facilitating the adaptation of manufacturing processes and production schedules based on real-time patient health conditions. SimPAC can model various patient disease progressions using parametric functions, nonparametric functions, or tabular data. Additionally, SimPAC offers easy configuration options to model various cell therapy supply chains. We provide two case studies to demonstrate the capabilities of SimPAC and highlight the benefits of patient-centric manufacturing, including improved survival rates and potential economic advantages. However, while the benefits are significant, our study also emphasizes the importance of balancing improved patient outcomes, economic viability and ethical considerations in the context of personalized medicine. SimPAC can be used to explore applications of this approach to diverse therapeutic contexts and supply chain configurations.

Functional screening of amplification outlier oncogenes in organoid models of early tumorigenesis.

Somatic copy number gains are pervasive across cancer types, yet their roles in oncogenesis are insufficiently evaluated. This inadequacy is partly due to copy gains spanning large chromosomal regions, obscuring causal loci. Here, we employed organoid modeling to evaluate candidate oncogenic loci identified via integrative computational analysis of extreme copy gains overlapping with extreme expression dysregulation in The Cancer Genome Atlas. Subsets of "outlier" candidates were contextually screened as tissue-specific cDNA lentiviral libraries within cognate esophagus, oral cavity, colon, stomach, pancreas, and lung organoids bearing initial oncogenic mutations. Iterative analysis nominated the kinase DYRK2 at 12q15 as an amplified head and neck squamous carcinoma oncogene in p53-/- oral mucosal organoids. Similarly, FGF3, amplified at 11q13 in 41% of esophageal squamous carcinomas, promoted p53-/- esophageal organoid growth reversible by small molecule and soluble receptor antagonism of FGFRs. Our studies establish organoid-based contextual screening of candidate genomic drivers, enabling functional evaluation during early tumorigenesis.

Active learning-based multistage sequential decision-making model with application on common bile duct stone evaluation.

Multistage sequential decision-making occurs in many real-world applications such as healthcare diagnosis and treatment. One concrete example is when the doctors need to decide to collect which kind of information from subjects so as to make the good medical decision cost-effectively. In this paper, an active learning-based method is developed to model the doctors' decision-making process that actively collects necessary information from each subject in a sequential manner. The effectiveness of the proposed model, especially its two-stage version, is validated on both simulation studies and a case study of common bile duct stone evaluation for pediatric patients.

A disposable impedance-based sensor for in-line cell growth monitoring in CAR-T cell manufacturing.

This paper presents the development of low-cost, disposable impedance-based sensors for real-time, in-line monitoring of suspension cell culture. The sensors consist of electrical discharge machining (EDM) cut aluminum electrodes and polydimethylsiloxane (PDMS) spacers, both of which are low-cost materials that can be safely disposed of. Our research demonstrates the capability of these low-cost sensors for in-line, non-invasive monitoring of suspension cell growth in cell manufacturing. We use a hybrid equivalent circuit model to extract key features/parameters from intertwined impedance signals, which are then fed to a novel physics-inspired (gray-box) model designed for α-relaxation. This model determines viable cell count (VCC), a critical quality attribute (CQA) in cell manufacturing. Predicted VCC trends are then compared with image-based cell count data to verify their accuracy.

Application of textile technology in tissue engineering: A review.

One of the key elements in tissue engineering is to design and fabricate scaffolds with tissue-like properties. Among various scaffold fabrication methods, textile technology has shown its unique advantages in mimicking human tissues' properties such as hierarchical, anisotropic, and strain-stiffening properties. As essential components in textile technology, textile patterns affect the porosity, architecture, and mechanical properties of textile-based scaffolds. However, the potential of various textile patterns has not been fully explored when fabricating textile-based scaffolds, and the effect of different textile patterns on scaffold properties has not been thoroughly investigated. This review summarizes textile technology development and highlights its application in tissue engineering to facilitate the broader application of textile technology, especially various textile patterns in tissue engineering. The potential of using different textile methods such as weaving, knitting, and braiding to mimic properties of human tissues is discussed, and the effect of process parameters in these methods on fabric properties is summarized. Finally, perspectives on future directions for explorations are presented. STATEMENT OF SIGNIFICANCE: Recently, biomedical engineers have applied textile technology to fabricate scaffolds for tissue engineering applications. Various textile methods, especially weaving, knitting, and braiding, enables engineers to customize the physical, mechanical, and biological properties of scaffolds. However, most textile-based scaffolds only use simple textile patterns, and the effect of different textile patterns on scaffold properties has not been thoroughly investigated. In this review, we cover for the first time the effect of process parameters in different textile methods on fabric properties, exploring the potential of using different textile methods to mimic properties of human tissues. Previous advances in textile technology are presented, and future directions for explorations are presented, hoping to facilitate new breakthroughs of textile-based tissue engineering.

Textile-based sandwich scaffold using wet electrospun yarns for skin tissue engineering.

One of the key elements in tissue engineering is to design and fabricate scaffolds with tissue-like properties. However, mimicking the strain-stiffening property of human tissues by using synthetic materials is still a challenge in scaffold fabrication since most synthetic materials exhibit strain-softening behavior. To address this challenge, we propose a textile-based sandwich scaffold to mimic strain-stiffening behavior observed in human tissues. For this purpose, we first fabricate polycaprolactone (PCL) yarns by wet electrospinning. Then, we crochet PCL yarns into a textile fabric. Finally, we fabricate the sandwich scaffold by embedding the textile fabric inside two electrospun mats. The wet electrospun PCL yarns induce cellular alignment and elongation. The textile-based sandwich scaffold exhibits strain-stiffening behavior. By changing process parameters during the yarn fabrication and textile process, we can adjust the maximum stress of the scaffold from 3.74 to 11.82 MPa, the maximum strain from 0.16 to 2.37, and the elastic modulus from 2.10 to 18.05 MPa, all within the ranges of that of human skin. The scaffold is also able to support cell proliferation and infiltration after optimizing the thickness of the outer layers of the sandwich scaffold. This study validates the potential of the textile-based sandwich scaffold to mimic the physical, mechanical, and biological properties of human skin and other tissues.

Using Wet Electrospun PCL/Gelatin/CNT Yarns to Fabricate Textile-Based Scaffolds for Vascular Tissue Engineering.

Incorporating conductive materials in scaffolds has shown advantages in regulating adhesion, mitigation, and proliferation of electroactive cells for tissue engineering applications. Among various conductive materials, carbon nanotubes (CNTs) have shown great promises in tissue engineering because of their good mechanical properties. However, the broad application of CNTs in tissue engineering is limited by current methods to incorporate CNTs in polymers that require miscible solvents to dissolve CNTs and polymers or CNT surface modification. These methods either limit polymer selections or adversely affect the properties of polymer/CNT composites. Here, we report a novel method to fabricate polymer/CNT composite yarns by electrospinning polycaprolactone/gelatin into a bath of CNT dispersion and extracting electrospun fibers out of the bath. The concentration of CNTs in the bath affects the thermal and mechanical properties and the yarns' degradation behavior. In vitro biological test results show that within a limited range of CNT concentrations in the bath, the yarns exhibit good biocompatibility and the ability to guide cell elongation and alignment. We also report the design and fabrication of a vascular scaffold by knitting the yarns into a textile fabric and combining the textile fabric with gelatin. The scaffold has similar mechanical properties to native vessels and supports cell proliferation. This work demonstrates that the wet electrospun polymer/CNT yarns are good candidates for constructing vascular scaffolds and provides a novel method to incorporate CNTs or other functional materials into biopolymers for tissue engineering applications.

3D Printing, Computational Modeling, and Artificial Intelligence for Structural Heart Disease.

Structural heart disease (SHD) is a new field within cardiovascular medicine. Traditional imaging modalities fall short in supporting the needs of SHD interventions, as they have been constructed around the concept of disease diagnosis. SHD interventions disrupt traditional concepts of imaging in requiring imaging to plan, simulate, and predict intraprocedural outcomes. In transcatheter SHD interventions, the absence of a gold-standard open cavity surgical field deprives physicians of the opportunity for tactile feedback and visual confirmation of cardiac anatomy. Hence, dependency on imaging in periprocedural guidance has led to evolution of a new generation of procedural skillsets, concept of a visual field, and technologies in the periprocedural planning period to accelerate preclinical device development, physician, and patient education. Adaptation of 3-dimensional (3D) printing in clinical care and procedural planning has demonstrated a reduction in early-operator learning curve for transcatheter interventions. Integration of computation modeling to 3D printing has accelerated research and development understanding of fluid mechanics within device testing. Application of 3D printing, computational modeling, and ultimately incorporation of artificial intelligence is changing the landscape of physician training and delivery of patient-centric care. Transcatheter structural heart interventions are requiring in-depth periprocedural understanding of cardiac pathophysiology and device interactions not afforded by traditional imaging metrics.

Active Image Synthesis for Efficient Labeling.

The great success achieved by deep neural networks attracts increasing attention from the manufacturing and healthcare communities. However, the limited availability of data and high costs of data collection are the major challenges for the applications in those fields. We propose in this work AISEL, an active image synthesis method for efficient labeling, to improve the performance of the small-data learning tasks. Specifically, a complementary AISEL dataset is generated, with labels actively acquired via a physics-based method to incorporate underlining physical knowledge at hand. An important component of our AISEL method is the bidirectional generative invertible network (GIN), which can extract interpretable features from the training images and generate physically meaningful virtual images. Our AISEL method then efficiently samples virtual images not only further exploits the uncertain regions but also explores the entire image space. We then discuss the interpretability of GIN both theoretically and experimentally, demonstrating clear visual improvements over the benchmarks. Finally, we demonstrate the effectiveness of our AISEL framework on aortic stenosis application, in which our method lowers the labeling cost by 90 percent while achieving a 15 percent improvement in prediction accuracy.

Quantitative Investigation of the Process Parameters of Electrohydrodynamic Direct-Writing and Their Effects on Fiber Surface Roughness and Cell Adhesion.

Electrohydrodynamic (EHD) direct-writing has been widely used to fabricate micro/nanofibers that can serve as a building block in tissue engineering scaffolds. However, the application of EHD direct-writing in tissue engineering is limited by the lack of fundamental knowledge in the correlations among the process parameters, the fiber surface roughness, and the cell adhesion performance. Without a standardized experimental setting and the quantitative database, inconsistent results have been reported. Here, we quantitatively investigate the process-structure-property relationships as the first step towards a better understanding of the EHD direct-writing technology for tissue engineering. Polycaprolactone (PCL) solution is used as a model ink material, and human mesenchymal stem cells (hMSCs) are used to study cell adhesion on PCL fibers. We investigate the different jetting modes defined by the applied voltage, the feed rate, and the nozzle-collector distance. The quantitative effects of process parameters on the fiber surface roughness and the cell adhesion performance are experimentally determined. The quantitative process-structure-property relationships revealed in this study provide guidelines for controlling the surface roughness and the cell adhesion performance of EHD direct-written fibers. This study will facilitate the application of EHD direct-writing in tissue engineering.

A printing-spray-transfer process for attaching biocompatible and antibacterial coatings to the surfaces of patient-specific silicone stents.

An antibacterial coating with stable antibacterial properties and favorable biocompatibility is recognized as an effective method to prevent bacterial adhesion and biofilm formation on biomedical implant surfaces. In this study, a convenient and low-cost printing-spray-transfer process was proposed that enables reliably attaching antibacterial and biocompatible coatings to patient-specific silicone implant surfaces. A desktop three-dimensional printer was used to print the mold of silicone implant molds according to the characteristics of the diseased areas. Multiwalled carbon nanotubes (MWCNTs) uniformly decorated with silver nanoparticles (AgNPs/CNTs) were synthesized as the antibacterial materials for the spray process. The well-distributed AgNPs/CNT coating was anchored to the silicone surface through an in-mold transfer printing process. Stable adhesion of the coatings was assessed via tape testing and UV-vis spectra. Hardly any AgNPs/CNTs peeled off the substrate, and the adhesion was rated at 4B. Antibacterial activity, Ag release, cell viability and morphology were further assessed, revealing high antibacterial activity and great biocompatibility. The process proposed herein has potential applications for fabricating stable antibacterial coatings on silicone implant surfaces, especially for patient-specific silicone implants such as silicone tracheal stents.

Rapid Fabrication of Ready-to-Use Gelatin Scaffolds with Prevascular Networks Using Alginate Hollow Fibers as Sacrificial Templates.

In this study, we developed a facile manufacturing method for interconnected prevascular networks using calcium chloride (CaCl2) cross-linked alginate hollow fibers as sacrificial templates. The resulting network can be used to deliver oxygen and nutrients and remove waste for embedded cells in large-volume gelatin scaffolds during in vitro culturing. The sacrificial templates were printed by customized coaxial nozzles and embedded in scaffolds made of a mixture of gelatin, microbial transglutaminase (mTG), and sodium citrate. During the cross-linking of gelatin and mTG, the sacrificial templates started to dissolve from the scaffold-template interface due to the presence of the sodium citrate in the gelatin. The embedded sacrificial templates were completely dissolved without any postprocessing, and the designed prevascular networks successfully retained their geometries and dimensions. No residue of the template was observed at the scaffold-template interface after dissolution, which promoted cell adhesion. This manufacturing method has a high degree of freedom in templates' geometry, which was demonstrated by fabricating prevascular networks with various designs, including grid, branched, and dendritic networks. The effects of hollow fiber size and sodium citrate concentration on the dissolution time were analyzed. Human umbilical vein endothelial cells were injected into the aforementioned networks and formed a confluent endothelial cell monolayer with high viability during the culture process. The results suggest great promise to rapidly build large-scale ready-to-use gelatin scaffolds with prevascular networks for the applications in tissue engineering.

Direct-write and sacrifice-based techniques for vasculatures.

Fabricating biomimetic vasculatures is considered one of the greatest challenges in tissue regeneration due to their complex structures across various length scales. Many strategies have been investigated on how to fabricate tissue-engineering vasculatures (TEVs), including vascular-like and vascularized structures that can replace their native counterparts. The advancement of additive manufacturing (AM) technologies has enabled a wide range of fabrication techniques that can directly-write TEVs with complex and delicate structures. Meanwhile, sacrifice-based techniques, which rely on the removal of encapsulated sacrificial templates to form desired cavity-like structures, have also been widely studied. This review will specifically focus on the two most promising methods in these recently developed technologies, which are the direct-write method and the sacrifice-based method. The performance, advantages, and shortcomings of each technique are analyzed and compared. In the discussion, we list current challenges in this field and present our vision of next-generation TEVs technologies. Perspectives on future research in this field are given at the end.

Quantitative Prediction of Paravalvular Leak in Transcatheter Aortic Valve Replacement Based on Tissue-Mimicking 3D Printing.

OBJECTIVES: This study aimed to develop a procedure simulation platform for in vitro transcatheter aortic valve replacement (TAVR) using patient-specific 3-dimensional (3D) printed tissue-mimicking phantoms. We investigated the feasibility of using these 3D printed phantoms to quantitatively predict the occurrence, severity, and location of any degree of post-TAVR paravalvular leaks (PVL). BACKGROUND: We have previously shown that metamaterial 3D printing technique can be used to create patient-specific phantoms that mimic the mechanical properties of biological tissue. This may have applications in procedural planning for cardiovascular interventions. METHODS: This retrospective study looked at 18 patients who underwent TAVR. Patient-specific aortic root phantoms were created using the tissue-mimicking 3D printing technique using pre-TAVR computed tomography. The CoreValve (self-expanding valve) prostheses were deployed in the phantoms to simulate the TAVR procedure, from which post-TAVR aortic root strain was quantified in vitro. A novel index, the annular bulge index, was measured to assess the post-TAVR annular strain unevenness in the phantoms. We tested the comparative predictive value of the bulge index and other known predictors of post-TAVR PVL. RESULTS: The maximum annular bulge index was significantly different among patient subgroups that had no PVL, trace-to-mild PVL, and moderate-to-severe PVL (p = 0.001). Compared with other known PVL predictors, bulge index was the only significant predictor of moderate-severe PVL (area under the curve = 95%; p < 0.0001). Also, in 12 patients with post-TAVR PVL, the annular bulge index predicted the major PVL location in 9 patients (accuracy = 75%). CONCLUSIONS: In this proof-of-concept study, we have demonstrated the feasibility of using 3D printed tissue-mimicking phantoms to quantitatively assess the post-TAVR aortic root strain in vitro. A novel indicator of the post-TAVR annular strain unevenness, the annular bulge index, outperformed the other established variables and achieved a high level of accuracy in predicting post-TAVR PVL, in terms of its occurrence, severity, and location.

CNT Enabled Co-braided Smart Fabrics: A New Route for Non-invasive, Highly Sensitive &Large-area Monitoring of Composites.

The next-generation of hierarchical composites needs to have built-in functionality to continually monitor and diagnose their own health states. This paper includes a novel strategy for in-situ monitoring the processing stages of composites by co-braiding CNT-enabled fiber sensors into the reinforcing fiber fabrics. This would present a tremendous improvement over the present methods that excessively focus on detecting mechanical deformations and cracks. The CNT enabled smart fabrics, fabricated by a cost-effective and scalable method, are highly sensitive to monitor and quantify various events of composite processing including resin infusion, onset of crosslinking, gel time, degree and rate of curing. By varying curing temperature and resin formulation, the clear trends derived from the systematic study confirm the reliability and accuracy of the method, which is further verified by rheological and DSC tests. More importantly, upon wisely configuring the smart fabrics with a scalable sensor network, localized processing information of composites can be achieved in real time. In addition, the smart fabrics that are readily and non-invasively integrated into composites can provide life-long structural health monitoring of the composites, including detection of deformations and cracks.

Bioprinting: an assessment based on manufacturing readiness levels.

Over the last decade, bioprinting has emerged as a promising technology in the fields of tissue engineering and regenerative medicine. With recent advances in additive manufacturing, bioprinting is poised to provide patient-specific therapies and new approaches for tissue and organ studies, drug discoveries and even food manufacturing. Manufacturing Readiness Level (MRL) is a method that has been applied to assess manufacturing maturity and to identify risks and gaps in technology-manufacturing transitions. Technology Readiness Level (TRL) is used to evaluate the maturity of a technology. This paper reviews recent advances in bioprinting following the MRL scheme and addresses corresponding MRL levels of engineering challenges and gaps associated with the translation of bioprinting from lab-bench experiments to ultimate full-scale manufacturing of tissues and organs. According to our step-by-step TRL and MRL assessment, after years of rigorous investigation by the biotechnology community, bioprinting is on the cusp of entering the translational phase where laboratory research practices can be scaled up into manufacturing products specifically designed for individual patients.

A strong integrated strength and toughness artificial nacre based on dopamine cross-linked graphene oxide.

Demands of the strong integrated materials have substantially increased across various industries. Inspired by the relationship of excellent integration of mechanical properties and hierarchical nano/microscale structure of the natural nacre, we have developed a strategy for fabricating the strong integrated artificial nacre based on graphene oxide (GO) sheets by dopamine cross-linking via evaporation-induced assembly process. The tensile strength and toughness simultaneously show 1.5 and 2 times higher than that of natural nacre. Meanwhile, the artificial nacre shows high electrical conductivity. This type of strong integrated artificial nacre has great potential applications in aerospace, flexible supercapacitor electrodes, artificial muscle, and tissue engineering.

Vanadium oxide-carbon nanotube composite electrodes for energy storage by supercritical fluid deposition: experiment design and device performance.

Vanadium pentoxide (V2O5) deposited on porous multiwalled carbon nanotube (MWCNT) buckypaper using supercritical fluid CO2(scCO2) deposition shows excellent performance for electrochemical capacitors. However, the low weight loading of V2O5 is one of the main problems. In this paper, design of experiments and response surface methods were employed to explore strategies for improving the active material loading by increasing the organo-vanadium precursor adsorption. A second-order response surface model was fitted to the designed experiments to predict the loading of the vanadium precursors onto carbon nanotube buckypaper as a function of time, temperature and pressure of CO2, buckypaper functionalization, precursor type, initial precursor mass and stir speed. Operation conditions were identified by employing a model that led to a precursor loading of 19.33%, an increase of 72.28% over the initial screening design. CNTs-V2O5 composite electrodes fabricated from deposited samples using the optimized conditions demonstrated outstanding electrochemical performance (947.1 F g(-1) of V2O5 at a high scan rate 100 mV s(-1)). The model also predicted operation conditions under which light precursor aggregation took place. The V2O5 from aggregated precursor still possessed considerable specific capacitance (311 F g(-1) of V2O5 at a scan rate 100 mV s(-1)), and the significantly higher V2O5 loading (∼81%) contributed to an increase in overall electrode capacitance.

Nanoscale infiltration behaviour and through-thickness permeability of carbon nanotube buckypapers.

Carbon nanotube thin films or 'buckypapers' show potential for various applications including electrodes for energy devices, nanoscale filtration devices and composite materials. This paper reports on the study of through-thickness permeability of different buckypaper materials. The infiltration behaviours of different liquids into four types of buckypaper were investigated. Infiltration of the liquids into buckypaper was found to follow Darcy's law, except in the case of epoxy resin solution permeation into SWNT buckypaper. The results revealed that the permeability of SWNT buckypaper was of the order of 10(-19) m(2), which is about two orders of magnitude lower than the 10(-17) m(2) permeability for the MWNT buckypaper. The factors of wider pores, higher porosity and less surface area appear to contribute to a higher permeability, which is consistent with Darcy's law and the Kozeny-Carman model. The Kozeny constants of buckypapers correlated well with the tortuosity of their flow paths and nanoscale pore size. The polarity of working fluids did not show an impact on the permeability. Solutions with molecular size near the size of the nanopores in the buckypaper led to lower permeability due to the occurrence of pore blockage. In addition, a threshold pressure existed for liquid to infiltrate into nanoscale pores in buckypapers, which does not exist in fibre reinforcement preforms.