Because synthetic vascular prostheses perform poorly in small-diameter revascularization, biological vascular substitutes are being developed as an alternative. Although their in vivo results are promising, their production involves long, complex, and expensive tissue engineering methods. To overcome these limitations, we propose an innovative approach that combines the human amniotic membrane (HAM), which is a widely available and cost-effective biological raw material, with a rapid and robust textile-inspired assembly strategy. Fetal membranes were collected after cesarean deliveries at term. Once isolated by dissection, HAM sheets were cut into ribbons that could be further processed by twisting into threads. Characterization of the HAM yarns (both ribbons and threads) showed that their physical and mechanical properties could be easily tuned. Since our clinical strategy will be to provide an off-the-shelf allogeneic implant, we studied the effects of decellularization and/or gamma sterilization on the histological, mechanical, and biological properties of HAM ribbons. Gamma irradiation of hydrated HAMs, with or without decellularization, did not interfere with the ability of the matrix to support endothelium formation in vitro. Finally, our HAM-based, woven tissue-engineered vascular grafts (TEVGs) exhibited clinically relevant mechanical properties. Thus, this study demonstrates that human, completely biological, allogeneic, small-diameter TEVGs can be produced from HAM, thereby avoiding costly cell culture and bioreactors.

Sleep is one of the most important human physiological activities, and plays an essential role in human health. Polysomnography (PSG) is the gold standard for measuring sleep quality and disorders, but it is time-consuming, labor-intensive, and prone to errors. Current research has confirmed the correlations between sleep and the respiratory/circulatory system. Electrocardiography (ECG) is convenient to perform, and ECG data are rich in breathing information. Therefore, sleep research based on ECG data has become popular. Currently, deep learning (DL) methods have achieved promising results on predictive health care tasks using ECG signals. Therefore, in this review, we systematically identify recent research studies and analyze them from the perspectives of data, model, and task. We discuss the shortcomings, summarize the findings, and highlight the potential opportunities. For sleep-related tasks, many ECG-based DL methods produce more accurate results than traditional approaches by combining multiple signal features and model structures. Methods that are more interpretable, scalable, and transferable will become ubiquitous in the daily practice of medicine and ambient-assisted-living applications. This paper is the first systematic review of ECG-based DL methods for sleep tasks.

The photoplethysmogram (PPG) signal is widely used in pulse oximeters and smartwatches. A fundamental step in analysing the PPG is the detection of heartbeats. Several PPG beat detection algorithms have been proposed, although it is not clear which performs best. Objective: This study aimed to: (i) develop a framework with which to design and test PPG beat detectors; (ii) assess the performance of PPG beat detectors in different use cases; and (iii) investigate how their performance is affected by patient demographics and physiology. Approach: Fifteen beat detectors were assessed against electrocardiogram-derived heartbeats using data from eight datasets. Performance was assessed using the F1 score, which combines sensitivity and positive predictive value. Main results: Eight beat detectors performed well in the absence of movement with F1 scores of ?90% on hospital data and wearable data collected at rest. Their performance was poorer during exercise with F1 scores of 55%�V91%; poorer in neonates than adults with F1 scores of 84%�V96% in neonates compared to 98%�V99% in adults; and poorer in atrial fibrillation (AF) with F1 scores of 92%�V97% in AF compared to 99%�V100% in normal sinus rhythm. Significance: Two PPG beat detectors denoted 'MSPTD' and 'qppg' performed best, with complementary performance characteristics. This evidence can be used to inform the choice of PPG beat detector algorithm. The algorithms, datasets, and assessment framework are freely available.

Glioma is one of the most malignant types of cancer and most gliomas remain incurable. One of the hallmarks of glioma is its invasiveness. Furthermore, glioma cells tend to readily detach from the primary tumor and travel through the brain tissue, making complete tumor resection impossible in many cases. To expand the knowledge regarding the invasive behavior of glioma, evaluate drug resistance, and recapitulate the tumor microenvironment, various modeling strategies were proposed in the last decade, including three-dimensional (3D) biomimetic scaffold-free cultures, organ-on-chip microfluidics chips, and 3D bioprinting platforms, which allow for the investigation on patient-specific treatments. The emerging method of 3D bioprinting technology has introduced a time- and cost-efficient approach to create in vitro models that possess the structural and functional characteristics of human organs and tissues by spatially positioning cells and bioink. Here, we review emerging 3D bioprinted models developed for recapitulating the brain environment and glioma tumors, with the purpose of probing glioma cell invasion and gliomagenesis and discuss the potential use of 4D printing and machine learning applications in glioma modelling.

The development of physiologically relevant in vitro colorectal cancer (CRC) models is vital for advancing understanding of tumor biology. Although CRC patient-derived xenografts (PDXs) recapitulate key patient tumor characteristics and demonstrate high concordance with clinical outcomes, the use of this in vivo model is costly and low-throughput. Here we report the establishment and in-depth characterization of an in vitro tissue-engineered CRC model using PDX cells. To form the 3D engineered CRC-PDX (3D-eCRC-PDX) tissues, CRC PDX tumors were expanded in vivo, dissociated, and the isolated cells encapsulated within PEG-fibrinogen hydrogels. Following PEG-fibrinogen encapsulation, cells remain viable and proliferate within 3D-eCRC-PDX tissues. Tumor cell subpopulations, including human cancer and mouse stromal cells, are maintained in long-term culture (29 days); cellular subpopulations increase ratiometrically over time. The 3D-eCRC-PDX tissues mimic the mechanical stiffness of originating tumors. Extracellular matrix protein production by cells in the 3D-eCRC-PDX tissues resulted in approximately 57% of proteins observed in the CRC-PDX tumors also being present in the 3D-eCRC-PDX tissues on day 22. Furthermore, we show congruence in enriched gene ontology molecular functions and Hallmark gene sets in 3D-eCRC-PDX tissues and CRC-PDX tumors compared to normal colon tissue, while prognostic Kaplan-Meier plots for overall and relapse free survival did not reveal significant differences between CRC-PDX tumors and 3D-eCRC-PDX tissues. Our results demonstrate high batch-to-batch consistency and strong correlation between our in vitro tissue-engineered PDX-CRC model and the originating in vivo PDX tumors, providing a foundation for future studies of disease progression and tumorigenic mechanisms.

Articulated legs enable the selection of robot gaits, including walking in different directions such as forward or sideways. For longer distances, the best gaits might maximize velocity or minimize the cost of transport (COT). While animals often have morphology suited to walking either forward (like insects) or sideways (like crabs), hexapod robots often default to forward walking. In this paper, we compare forward walking with crab-like sideways walking. To do this, a simple gait design method is introduced for determining forward and sideways gaits with equivalent body heights and step heights. Specifically, the frequency and stride lengths are tuned within reasonable constraints to find gaits that represent a robot's performance potential in terms of speed and energy cost. Experiments are performed in both dynamic simulation in Webots and a laboratory environment with our 18 degree-of-freedom hexapod robot, Sebastian. With the common three joint leg design, the results show that sideways walking is overall better (75% greater walking speed and 40% lower COT). The performance of sideways walking was better on both hard floors and granular media (dry play sand). This supports development of future crab-like walking robots for future applications. In future work, this approach may be used to develop nominal gaits without extensive optimization, and to explore whether the advantages of sideways walking persist for other hexapod designs.

This paper discusses the foundations of a bio-based material paradigm for architecture. It argues that moving from a current reliance on the non-renewable materials of the geosphere, to the renewable and fundamentally cyclical materials of the biosphere can establish alternate foundations for thinking alternative sustainable building practices. By positioning architecture and the built environment as a particular case for bio-based materials, where the longer life spans of buildings support better carbon storage, this paper identifies the bottlenecks that limit their adaptation into the way architecture is thought, designed and built. If architectural ideation and design is traditionally understood through the durable and the permanent, our aim here is to challenge this foundation and bring forth the fundamental differences that bio-based materials engender. With focus on the embedded lifespans of living materials, the fundamental circularity and degradability of biomass and resulting transformative life cycles of the artefacts that they embody, this paper asks how a new representational framework for bio-based material paradigm can be conceptualised, instrumentalised and in turn materialised. The paper supports this positioning through a presentation of a series of methodological probes. The probes outline strategies for new methodologies by which we can capture, predict and steer the transformations of living materials and functionalise them as part of an architectural performance.

Proteins are some of the most versatile and studied macromolecules with extensive biomedical applications. The natural and biological origin of proteins offer such materials several advantages over their synthetic counterparts, such as innate bioactivity, recognition by cells and reduced immunogenic potential. Furthermore, proteins can be easily functionalized by altering their primary amino acid sequence and can often be further self-assembled into higher order structures either spontaneously or under specific environmental conditions. This review will feature the recent advances in protein-based biomaterials in the delivery of therapeutic cargo such as small molecules, genetic material, proteins, and cells. First, we will discuss the ways in which secondary structural motifs, the building blocks of more complex proteins, have unique properties that enable them to be useful for therapeutic delivery. Next, supramolecular assemblies, such as fibers, nanoparticles, and hydrogels, made from these building blocks that are engineered to behave in a cohesive manner, are discussed. Finally, we will cover additional modifications to protein materials that impart environmental responsiveness to materials. This includes the emerging field of protein molecular robots, and relatedly, protein-based theranostic materials that combine therapeutic potential with modern imaging modalities, including near-infrared fluorescence spectroscopy, single-photon emission computed tomography/computed tomography, positron emission tomography, magnetic resonance imaging, and ultrasound/photoacoustic imaging.

Photoplethysmography is now widely utilised by clinical devices such as pulse oximeters, and wearable devices such as smartwatches. It holds great promise for health monitoring in daily life. This editorial considers whether it would be possible and beneficial to establish best practices for photoplethysmography signal acquisition and processing. It reports progress made towards this, balanced with the challenges of working with a diverse range of photoplethysmography device designs and intended applications, each of which could benefit from different approaches to signal acquisition and processing. It concludes that there are several potential benefits to establishing best practices. However, it is not yet clear whether it is possible to establish best practices which hold across the range of photoplethysmography device designs and applications.