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Abstract Bioprinting of cell-laden hydrogels is a rapidly growing field in tissue engineering. The advent of digital light processing (DLP) three-dimensional (3D) bioprinting technique has revolutionized the fabrication of complex 3D structures. By adjusting light exposure, it becomes possible to control the mechanical properties of the structure, a critical factor in modulating cell activities. To better mimic cell densities in real tissues, recent progress has been made in achieving high-cell-density (HCD) printing with high resolution. However, regulating the stiffness in HCD constructs remains challenging. The large volume of cells greatly affects the light-based DLP bioprinting by causing light absorption, reflection, and scattering. Here, we introduce a neural network-based machine learning technique to predict the stiffness of cell-laden hydrogel scaffolds. Using comprehensive mechanical testing data from 3D bioprinted samples, the model was trained to deliver accurate predictions. To address the demand of working with precious and costly cell types, we employed various methods to ensure the generalizability of the model, even with limited datasets. We demonstrated a transfer learning method to achieve good performance for a precious cell type with a reduced amount of data. The chosen method outperformed many other machine learning techniques, offering a reliable and efficient solution for stiffness prediction in cell-laden scaffolds. This breakthrough paves the way for the next generation of precision bioprinting and more customized tissue engineering. 

Abstract Although single-cell and spatial sequencing methods enable simultaneous measurement of more than one biological modality, no technology can capture all modalities within the same cell. For current data integration methods, the feasibility of cross-modal integration relies on the existence of highly correlated, a priori ‘linked’ features. We describe matching X-modality via fuzzy smoothed embedding (MaxFuse), a cross-modal data integration method that, through iterative coembedding, data smoothing and cell matching, uses all information in each modality to obtain high-quality integration even when features are weakly linked. MaxFuse is modality-agnostic and demonstrates high robustness and accuracy in the weak linkage scenario, achieving 20~70% relative improvement over existing methods under key evaluation metrics on benchmarking datasets. A prototypical example of weak linkage is the integration of spatial proteomic data with single-cell sequencing data. On two example analyses of this type, MaxFuse enabled the spatial consolidation of proteomic, transcriptomic and epigenomic information at single-cell resolution on the same tissue section. 

Background:Technology has the potential to prevent patient falls in healthcare settings and to reduce work-related injuries among healthcare providers. However, the usefulness and acceptability of each technology requires careful evaluation. Framed by the Technology Acceptance Model (TAM) and using the Adaptive Robotic Nursing Assistant (ARNA) to assist with patient ambulation, the present study examined the perceived usefulness of robots in patients’ fall prevention with implications for preventing associated work-related injuries among healthcare providers. Methods:Employing an experimental design, subjects were undergraduate nursing students ( N = 38) and one external subject (not a nursing student) who played the role of the patient. Procedures included subjects ambulating a simulated patient in three ways: (a) following the practice of a nurse assisting a patient to walk with the patient wearing a gait belt; (b) an ARNA-assisted process with the gait belt attached to ARNA; (c) an ARNA-assisted process with a subject walking a patient wearing a harness that is attached to ARNA. Block randomization was used with the following experimental scenarios: Gait Belt (human with a gait belt), “ARNA + Gait Belt” (a robot with a gait belt), and “ARNA + Harness” (a robot with a harness). Descriptive statistics and a multiple regression model were used to analyze the data and compare the outcome described as the Perceived Usefulness (PU) of a robot for patient walking versus a human “nurse assistant” without a robot. The independent variables included the experimental conditions of “Gait Belt,” “ARNA + Gait Belt,” and “ARNA + Harness,” the subject’s age, race, and previous videogame playing experience. Findings:Results indicated that PU was significantly higher in the Gait Belt + ARNA and Harness + ARNA conditions than in the Gait Belt condition ( p-value <.01 for both variables). In examining potential influencing factors, the effects of race (White, African American, and Asian), age, and previous video-playing experience were not statistically significant ( p-value >.05). Discussion:Results demonstrated that using robot technology to assist in walking patients was perceived by subjects as more useful in preventing falls than the gait belt. Patient fall prevention also has implications for preventing associated work-related injuries among healthcare providers. Implications:Understanding the effects of a subject’s perceptions can guide further development of assistive robots in patient care. Robotic engineers and interdisciplinary teams can design robots to accommodate worker characteristics and individual differences to improve worker safety and reduce work injuries.