Dataset Information

Development of miniaturized walking biological machines.

ABSTRACT: The quest to 'forward-engineer' and fabricate biological machines remains a grand challenge. Towards this end, we have fabricated locomotive "bio-bots" from hydrogels and cardiomyocytes using a 3D printer. The multi-material bio-bot consisted of a 'biological bimorph' cantilever structure as the actuator to power the bio-bot, and a base structure to define the asymmetric shape for locomotion. The cantilever structure was seeded with a sheet of contractile cardiomyocytes. We evaluated the locomotive mechanisms of several designs of bio-bots by changing the cantilever thickness. The bio-bot that demonstrated the most efficient mechanism of locomotion maximized the use of contractile forces for overcoming friction of the supporting leg, while preventing backward movement of the actuating leg upon relaxation. The maximum recorded velocity of the bio-bot was ~236 µm s(-1), with an average displacement per power stroke of ~354 µm and average beating frequency of ~1.5 Hz.

SUBMITTER: Chan V

PROVIDER: S-EPMC3498929 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Development of miniaturized walking biological machines.

Chan Vincent V Park Kidong K Collens Mitchell B MB Kong Hyunjoon H Saif Taher A TA Bashir Rashid R

Scientific reports 20121115

The quest to 'forward-engineer' and fabricate biological machines remains a grand challenge. Towards this end, we have fabricated locomotive "bio-bots" from hydrogels and cardiomyocytes using a 3D printer. The multi-material bio-bot consisted of a 'biological bimorph' cantilever structure as the actuator to power the bio-bot, and a base structure to define the asymmetric shape for locomotion. The cantilever structure was seeded with a sheet of contractile cardiomyocytes. We evaluated the locomot ...[more]

PMID: 23155480

Similar Datasets

Project description:The global coronavirus disease 2019 (COVID-19) pandemic has demonstrated the range of disease severity and pathogen genomic diversity emanating from a singular virus (severe acute respiratory syndrome coronavirus 2, SARS-CoV-2). This diversity in disease manifestations and genomic mutations has challenged healthcare management and resource allocation during the pandemic, especially for countries such as India with a bigger population base. Here, we undertake a combinatorial approach toward scrutinizing the diagnostic and genomic diversity to extract meaningful information from the chaos of COVID-19 in the Indian context. Using methods of statistical correlation, machine learning (ML), and genomic sequencing on a clinically comprehensive patient dataset with corresponding with/without respiratory support samples, we highlight specific significant diagnostic parameters and ML models for assessing the risk of developing severe COVID-19. This information is further contextualized in the backdrop of SARS-CoV-2 genomic features in the cohort for pathogen genomic evolution monitoring. Analysis of the patient demographic features and symptoms revealed that age, breathlessness, and cough were significantly associated with severe disease; at the same time, we found no severe patient reporting absence of physical symptoms. Observing the trends in biochemical/biophysical diagnostic parameters, we noted that the respiratory rate, total leukocyte count (TLC), blood urea levels, and C-reactive protein (CRP) levels were directly correlated with the probability of developing severe disease. Out of five different ML algorithms tested to predict patient severity, the multi-layer perceptron-based model performed the best, with a receiver operating characteristic (ROC) score of 0.96 and an F1 score of 0.791. The SARS-CoV-2 genomic analysis highlighted a set of mutations with global frequency flips and future inculcation into variants of concern (VOCs) and variants of interest (VOIs), which can be further monitored and annotated for functional significance. In summary, our findings highlight the importance of SARS-CoV-2 genomic surveillance and statistical analysis of clinical data to develop a risk assessment ML model.

Dataset Information

Development of miniaturized walking biological machines.

Publications

Development of miniaturized walking biological machines.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets