3D Printable Hydrogel with Tunable Degradability and Mechanical Properties as a Tissue Scaffold for Pelvic Organ Prolapse Treatment

Abstract

Pelvic organ prolapse (POP) is a dysfunction that affects a large proportion of women. Current support scaffolds’ lack of biocompatibility, biodegradability, and mechanical compliance are associated with surgical complications including erosion and pain, indicating the urgent need for new tissue scaffolds with customizable functions. A new material that uses polyvinyl alcohol (PVA) as the main ingredient and is chemically tuned to possess suitable mechanical properties and degradation rates for the surgical treatment of POP is developed. Specifically, the thiol‐norbornene “click” chemistry enables the sol‐gel transition of the biomaterial under UV‐light without side‐products. Meanwhile, NaOH treatment further toughens the hydrogel with a higher crosslink density. The PVA‐based biocompatible ink can be printed with UV‐facilitated direct ink writing due to the rapidly UV‐initiated chemical crosslink; in situ image analysis and machine learning methods are applied during this procedure to quantify and improve the printing quality. The cell viability results demonstrate the biocompatibility of the POP scaffolds, suggesting the potential for future animal studies and the possibility of clinical research. This study bridges polymer chemistry and manufacturing engineering with a specific tissue engineering application to solve the common disorder of POP, shedding light on individualized medicine and intelligent systems for biomedical engineering.

Enhancing quality control in bioprinting through machine learning

“…The use of sophisticated ML algorithms has also been reported for in-process QC in EBB, as reported in table 2. A typical solution is to train and test these algorithms with data coming from cameras mounted directly inside the bioprinter [98]. For example, Jin et al used a commercial pneumatic extrusion bioprinter to print two-layers constructs with different infill patterns, including grid, rectilinear, gyroid, and honeycomb (figure 5(a)).…”

Application of machine learning in polymer additive manufacturing: A review

“…In contrast to parametric models, non-parametric models do not assume a predefined mapping or distribution for a given problem, which makes them more flexible, adapting to the characteristics of the data. Common non-parametric techniques used in polymer AM research include decision trees (DT), 135 K-nearest neighbors (KNN), 136 K-means clustering (KMC), 137 Gaussian process (GP), 138 principal component analysis (PCA), 139 and t-distributed stochastic neighbor embedding (t-SNE). 125 KMC, PCA, and t-SNE are unsupervised ML techniques, while DT and KNN are supervised ML techniques.…”

“…ML-based techniques have been used to optimize printing parameters for reducing defects in printed parts. 139 The modeling approach taken in this study used data from in-situ imaging. A camera was attached to the printing nozzle to record the printing process in realtime.…”

Ink-Based Additive Manufacturing of a Polymer/Coal Composite: A Non-Traditional Reinforcement

“…This capability makes it particularly suitable for generating specialized structures such as lattices, interwoven designs, or meta-structures, especially in biomedical applications. 32 − 35 …”

“…DIW excels in constructing structures with intricate geometries, showcasing proficiency in creating overhangs and internal voids. This capability makes it particularly suitable for generating specialized structures such as lattices, interwoven designs, or meta-structures, especially in biomedical applications. − …”