Elevated liver enzymes in the first trimester are associated with gestational diabetes mellitus: A prospective cohort study.

AIMS: Previous studies have found that a single liver enzyme may predict gestational diabetes mellitus (GDM), but the results have been inconsistent. This study aimed to explore the associations of liver enzymes in early pregnancy with risk of GDM, as well as to independently rank risk factors. METHODS: This prospective cohort study included 1295 women who underwent liver enzyme measurements during early pregnancy and completed GDM assessment in mid-pregnancy. Logistic regression and restricted cubic spline analyses were conducted to assess the relationship between liver enzymes and risk of GDM. Back-propagation artificial neural network was performed to rank independently risk factors of GDM. RESULTS: Women diagnosed with GDM exhibited significantly higher levels of liver enzymes than those without GDM (all p < 0.05). The highest quartile of liver enzymes was associated with higher risk of GDM compared with the lowest quartile, with adjusted odds ratio (ORs) ranging from 2.76 to 8.11 (all p < 0.05). Moreover, the ORs of GDM increased linearly with liver enzymes level (all P for overall association <0.001). Furthermore, Back-propagation artificial neural network identified γ-gamma-glutamyl transferase (GGT) as accounting for the highest proportion in the ranking of GDM risk prediction weights (up to 20.8%). CONCLUSIONS: Single or total elevations of liver enzymes in early pregnancy could predict the GDM occurrence, in which GGT, alkaline Phosphatase, and aspartate aminotransferase were the three most important independent risk factors.

Mediating Effect of Insulin-Like Growth Factor-I Underlying the Link Between Vitamin D and Gestational Diabetes Mellitus.

Vitamin D was well-known to be associated with gestational diabetes mellitus (GDM). Insulin-like growth factor-I (IGF-I) has been linked to vitamin D and GDM, respectively. We hypothesize that changes in IGF-I metabolism induced by 25(OH)D3 might contribute to GDM. Therefore, we investigated the independent and combined relationships of serum 25(OH)D3 and IGF-I concentrations with GDM risk, and the mediation effect of IGF-I on 25(OH)D3. A total of 278 pregnant women (including 125 cases and 153 controls) were recruited in our current study. Maternal serum 25(OH)D3 and IGF-I were measured in the second trimester. Logistic regression models were used to estimate the associations of 25(OH)D3 and IGF-I concentrations with the risk of GDM. Mediation analyses were used to explore the mediation effect of IGF-I on the association between 25(OH)D3 and the risk of GDM. After adjusted for the confounded factors, both the third and fourth quartile of 25(OH)D3 decreased the risk of GDM (OR = 0.226; 95% CI, 0.103-0.494; OR = 0.109; 95% CI, 0.045-0.265, respectively) compared to the first quartile of 25(OH)D3. However, the third and fourth quartile of serum IGF-I (OR = 5.174; 95% CI, 2.287-11.705; OR = 12.784; 95% CI, 5.292-30.879, respectively) increased the risk of GDM compared to the first quartile of serum IGF-I. Mediation analyses suggested that 19.62% of the associations between 25(OH)D3 and GDM might be mediated by IGF-I. The lower concentration of serum 25(OH)D3 or higher IGF-I in the second trimester was associated with an increased risk of GDM. The serum IGF-I level might be a potential mediator between 25(OH)D3 and GDM.

Forward-Projection Architecture for Fast Iterative Image Reconstruction in X-ray CT.

Iterative image reconstruction can dramatically improve the image quality in X-ray computed tomography (CT), but the computation involves iterative steps of 3D forward- and back-projection, which impedes routine clinical use. To accelerate forward-projection, we analyze the CT geometry to identify the intrinsic parallelism and data access sequence for a highly parallel hardware architecture. To improve the efficiency of this architecture, we propose a water-filling buffer to remove pipeline stalls, and an out-of-order sectored processing to reduce the off-chip memory access by up to three orders of magnitude. We make a floating-point to fixed-point conversion based on numerical simulations and demonstrate comparable image quality at a much lower implementation cost. As a proof of concept, a 5-stage fully pipelined, 55-way parallel separable-footprint forward-projector is prototyped on a Xilinx Virtex-5 FPGA for a throughput of 925.8 million voxel projections/s at 200 MHz clock frequency, 4.6 times higher than an optimized 16-threaded program running on an 8-core 2.8-GHz CPU. A similar architecture can be applied to back-projection for a complete iterative image reconstruction system. The proposed algorithm and architecture can also be applied to hardware platforms such as graphics processing unit and digital signal processor to achieve significant accelerations.

Visualization of micro-agents and surroundings by real-time multicolor fluorescence microscopy.

Optical microscopy techniques are a popular choice for visualizing micro-agents. They generate images with relatively high spatiotemporal resolution but do not reveal encoded information for distinguishing micro-agents and surroundings. This study presents multicolor fluorescence microscopy for rendering color-coded identification of mobile micro-agents and dynamic surroundings by spectral unmixing. We report multicolor microscopy performance by visualizing the attachment of single and cluster micro-agents to cancer spheroids formed with HeLa cells as a proof-of-concept for targeted drug delivery demonstration. A microfluidic chip is developed to immobilize a single spheroid for the attachment, provide a stable environment for multicolor microscopy, and create a 3D tumor model. In order to confirm that multicolor microscopy is able to visualize micro-agents in vascularized environments, in vitro vasculature network formed with endothelial cells and ex ovo chicken chorioallantoic membrane are employed as experimental models. Full visualization of our models is achieved by sequential excitation of the fluorophores in a round-robin manner and synchronous individual image acquisition from three-different spectrum bands. We experimentally demonstrate that multicolor microscopy spectrally decomposes micro-agents, organic bodies (cancer spheroids and vasculatures), and surrounding media utilizing fluorophores with well-separated spectrum characteristics and allows image acquisition with 1280 [Formula: see text] 1024 pixels up to 15 frames per second. Our results display that real-time multicolor microscopy provides increased understanding by color-coded visualization regarding the tracking of micro-agents, morphology of organic bodies, and clear distinction of surrounding media.

Sparse coding with memristor networks.

Sparse representation of information provides a powerful means to perform feature extraction on high-dimensional data and is of broad interest for applications in signal processing, computer vision, object recognition and neurobiology. Sparse coding is also believed to be a key mechanism by which biological neural systems can efficiently process a large amount of complex sensory data while consuming very little power. Here, we report the experimental implementation of sparse coding algorithms in a bio-inspired approach using a 32 × 32 crossbar array of analog memristors. This network enables efficient implementation of pattern matching and lateral neuron inhibition and allows input data to be sparsely encoded using neuron activities and stored dictionary elements. Different dictionary sets can be trained and stored in the same system, depending on the nature of the input signals. Using the sparse coding algorithm, we also perform natural image processing based on a learned dictionary.