MyoFold: Joint myocardial tissue composition and wall motion quantification via a highly folded sequence.

PURPOSE: This study aims to develop and evaluate a novel cardiovascular MR sequence, MyoFold, designed for the simultaneous quantifications of myocardial tissue composition and wall motion. METHODS: MyoFold is designed as a 2D single breathing-holding sequence, integrating joint T1/T2 mapping and cine imaging. The sequence uses a 2-fold accelerated balanced SSFP (bSSFP) for data readout and incorporates electrocardiogram synchronization to align with the cardiac cycle. MyoFold initially acquires six single-shot inversion-recovery images, completed during the diastole of six successive heartbeats. T2 preparation (T2-prep) is applied to introduce T2 weightings for the last three images. Subsequently, over the following six heartbeats, segmented bSSFP is performed for the movie of the entire cardiac cycle, synchronized with an electrocardiogram. A neural network trained using numerical simulations of MyoFold is used for T1 and T2 calculations. MyoFold was validated through phantom and in vivo experiments, with comparisons made against MOLLI, SASHA, T2-prep bSSFP, and the conventional cine. RESULTS: In phantom studies, MyoFold exhibited a 10% overestimation in T1 measurements, whereas T2 measurements demonstrated high accuracy. In vivo experiments revealed that MyoFold T1 had comparable accuracy to SASHA and precision similar to MOLLI. MyoFold demonstrated good agreement with T2-prep bSSFP in myocardial T2 measurements. No significant differences were observed in the quantification of left-ventricle wall thickness and function between MyoFold and the conventional cine. CONCLUSION: MyoFold presents as a rapid, simple, and multitasking approach for quantitative cardiovascular MR examinations, offering simultaneous assessment of tissue composition and wall motion. The sequence's multitasking capabilities make it a promising tool for comprehensive cardiac evaluations in clinical settings.

The LMIT: Light-mediated minimally-invasive theranostics in oncology.

Minimally-invasive diagnosis and therapy have gradually become the trend and research hotspot of current medical applications. The integration of intraoperative diagnosis and treatment is a development important direction for real-time detection, minimally-invasive diagnosis and therapy to reduce mortality and improve the quality of life of patients, so called minimally-invasive theranostics (MIT). Light is an important theranostic tool for the treatment of cancerous tissues. Light-mediated minimally-invasive theranostics (LMIT) is a novel evolutionary technology that integrates diagnosis and therapeutics for the less invasive treatment of diseased tissues. Intelligent theranostics would promote precision surgery based on the optical characterization of cancerous tissues. Furthermore, MIT also requires the assistance of smart medical devices or robots. And, optical multimodality lay a solid foundation for intelligent MIT. In this review, we summarize the important state-of-the-arts of optical MIT or LMIT in oncology. Multimodal optical image-guided intelligent treatment is another focus. Intraoperative imaging and real-time analysis-guided optical treatment are also systemically discussed. Finally, the potential challenges and future perspectives of intelligent optical MIT are discussed.

Designing biomimetic scaffolds for skin tissue engineering.

There is a general increase in the number of patients with non-healing skin wounds, imposing a huge social and economic burden on patients and healthcare systems. Severe skin injury is an important clinical challenge. There is a lack of skin donors, and skin defects and scarring after surgery can lead to impaired skin function and skin integrity. Researchers worldwide have made great efforts to create human skin organs but are limited by the lack of key biological structural features of the skin. Tissue engineering repairs damaged tissue by incorporating cells into biocompatible and biodegradable porous scaffolds. Skin tissue engineered scaffolds not only have appropriate physical and mechanical properties but also exhibit skin-like surface topography and microstructure, which can promote cell adhesion, proliferation, and differentiation. At present, skin tissue engineering scaffolds are being developed into clinical applications that can overcome the limitations of skin transplantation, promote the process of wound healing, and repair skin tissue damage. This provides an effective therapeutic option for the management of patients with skin lesions. This paper reviews the structure and function of skin tissue and the process of wound healing, and summarizes the materials and manufacturing methods used to fabricate skin tissue engineering scaffolds. Next, the design considerations of skin tissue engineering scaffolds are discussed. An extensive review of skin scaffolds and clinically approved scaffold materials is presented. Lastly, some important challenges in the construction of skin tissue engineering scaffolds are presented.

Hydrogel-Based Stimuli-Responsive Micromotors for Biomedicine.

The rapid development of medical micromotors draws a beautiful blueprint for the noninvasive or minimally invasive diagnosis and therapy. By combining stimuli-sensitive hydrogel materials, micromotors are bestowed with new characteristics such as stimuli-responsive shape transformation/morphing, excellent biocompatibility and biodegradability, and drug loading ability. Actuated by chemical fuels or external fields (e.g., magnetic field, ultrasound, light, and electric field), hydrogel-based stimuli-responsive (HBSR) micromotors can be utilized to load therapeutic agents into the hydrogel networks or directly grip the target cargos (e.g., drug-loaded particles, cells, and thrombus), transport them to sites of interest (e.g., tumor area and diseased tissues), and unload the cargos or execute a specific task (e.g., cell capture, targeted sampling, and removal of blood clots) in response to a stimulus (e.g., change of temperature, pH, ion strength, and chemicals) in the physiological environment. The high flexibility, adaptive capacity, and shape morphing property enable the HBSR micromotors to complete specific medical tasks in complex physiological scenarios, especially in confined, hard-to-reach tissues, and vessels of the body. Herein, this review summarizes the current progress in hydrogel-based medical micromotors with stimuli responsiveness. The thermo-responsive, photothermal-responsive, magnetocaloric-responsive, pH-responsive, ionic-strength-responsive, and chemoresponsive micromotors are discussed in detail. Finally, current challenges and future perspectives for the development of HBSR micromotors in the biomedical field are discussed.

Dynamic network connectivity predicts subjective cognitive decline: the Sino-Longitudinal Cognitive impairment and dementia study.

Subjective cognitive decline (SCD) is the preclinical stage of Alzheimer's disease (AD), the most common neurodegenerative disease in the elderly. We collected resting-state functional MRI data and applied novel graph-theoretical analyses to investigate the dynamic spatiotemporal cerebral connectivities in 63 individuals with SCD and 67 normal controls (NC). Temporal flexibility and spatiotemporal diversity were mapped to reflect dynamic time-varying functional interactions among the brain regions within and outside communities. Temporal flexibility indicates how frequently a brain region interacts with regions of other communities across time; spatiotemporal diversity describes how evenly a brain region interacts with regions belonging to other communities. SCD and NC differed in large-scale brain dynamics characterized by the two measures, which, with support vector machine, demonstrated higher classification accuracies than conventional static parameters and structural metrics. The findings characterize dynamic network dysfunction that may serve as a biomarker of the preclinical stage of AD.

Dynamic network dysfunction in cocaine dependence: Graph theoretical metrics and stop signal reaction time.

Graphic theoretical metrics have become increasingly popular in characterizing functional connectivity of neural networks and how network connectivity is compromised in neuropsychiatric illnesses. Here, we add to this literature by describing dynamic network connectivities of 78 cocaine dependent (CD) and 85 non-drug using healthy control (HC) participants who underwent fMRI during performance of a stop signal task (SST). Compared to HC, CD showed prolonged stop signal reaction time (SSRT), consistent with deficits in response inhibition. In graph theoretical analysis of dynamic functional connectivity, we examined temporal flexibility and spatiotemporal diversity of 14 networks covering the whole brain. Temporal flexibility quantifies how frequently a brain region interacts with regions of other communities across time, with high temporal flexibility indicating that a region interacts predominantly with regions outside its own community. Spatiotemporal diversity quantifies how uniformly a brain region interacts with regions in other communities over time, with high spatiotemporal diversity indicating that the interactions are more evenly distributed across communities. Compared to HC, CD exhibited decreased temporal flexibility and increased spatiotemporal diversity in the great majority of neural networks. The graph metric measures of the default mode network negatively correlated with SSRT in CD but not HC. The findings are consistent with diminished temporal flexibility and a compensatory increase in spatiotemporal diversity, in association with impairment of a critical executive function, in cocaine addiction. More broadly, the findings suggest that graph theoretical metrics provide new insights for connectivity analyses to elucidate network dysfunction that may elude conventional measures.