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Measuring individual circadian phase is important to diagnose and treat circadian rhythm sleep-wake disorders and circadian misalignment, inform chronotherapy, and advance circadian science. Initial findings using blood transcriptomics to predict the circadian phase marker dim-light melatonin onset (DLMO) show promise. Alternatively, there are limited attempts using metabolomics to predict DLMO and no known omics-based biomarkers predict dim-light melatonin offset (DLMOff). We analyzed the human plasma metabolome during adequate and insufficient sleep to predict DLMO and DLMOff using one blood sample. Sixteen (8 male/8 female) healthy participants aged 22.4 ± 4.8 years (mean ± SD) completed an in-laboratory study with 3 baseline days (9 h sleep opportunity/night), followed by a randomized cross-over protocol with 9-h adequate sleep and 5-h insufficient sleep conditions, each lasting 5 days. Blood was collected hourly during the final 24 h of each condition to independently determine DLMO and DLMOff. Blood samples collected every 4 h were analyzed by untargeted metabolomics and were randomly split into training (68%) and test (32%) sets for biomarker analyses. DLMO and DLMOff biomarker models were developed using partial least squares regression in the training set followed by performance assessments using the test set. At baseline, the DLMOff model showed the highest performance (0.91 R 2 and 1.1 ± 1.1 h median absolute error ± interquartile range [MdAE ± IQR]), with significantly (p < 0.01) lower prediction error versus the DLMO model. When all conditions (baseline, 9 h, and 5 h) were included in performance analyses, the DLMO (0.60 R 2; 2.2 ± 2.8 h MdAE; 44% of the samples with an error under 2 h) and DLMOff (0.62 R 2; 1.8 ± 2.6 h MdAE; 51% of the samples with an error under 2 h) models were not statistically different. These findings show promise for metabolomics-based biomarkers of circadian phase and highlight the need to test biomarkers that predict multiple circadian phase markers under different physiological conditions. [ABSTRACT FROM AUTHOR]

Organ printing is a variant of the biomedical application of rapid prototyping technology or layer-by-layer additive biofabrication of 3D tissue and organ constructs using self-assembled tissue spheroids as building blocks. Bioengineering of perfusable intraorgan branched vascular trees incorporated into 3D tissue constructs is essential for the survival of bioprinted thick 3D tissues and organs. In order to design the optimal 'blueprint' for digital bioprinting of intraorgan branched vascular trees, the coefficients of tissue retraction associated with post-printing vascular tissue spheroid fusion and remodelling must be determined and incorporated into the original CAD. Using living tissue spheroids assembled into ring-like and tube-like vascular tissue constructs, the coefficient of tissue retraction has been experimentally evaluated. It has been shown that the internal diameter of ring-like and the height of tubular-like tissue constructs are significantly reduced during tissue spheroid fusion. During the tissue fusion process, the individual tissue spheroids also change their shape from ball-like to a conus-like form. A simple formula for the calculation of the necessary number of tissue spheroids for biofabrication of ring-like structures of desirable diameter has been deduced. These data provide sufficient information to design optimal CAD for bioprinted branched vascular trees of desirable final geometry and size. [ABSTRACT FROM AUTHOR]