A novel delins (c.773_819+47delinsAA) mutation of the PCCA gene associated with neonatal-onset propionic acidemia: a case report.

BACKGROUND: Propionic acidemia (PA)(OMIM#606054) is an inborn error of branched-chain amino acid metabolism, caused by defects in the propionyl-CoA carboxylase (PCC) enzyme which encoded by the PCCA and PCCB genes. CASE PRESENTATION: Here we report a Chinese neonate diagnosed with suspected PA based on the clinical symptoms, gas chromatography-mass spectrometry (GC/MS), and brain imaging tests. Targeted next-generation sequencing (NGS) was performed on the proband. We detected only one heterozygous recurrent nonsense variant (c.937C > T, p.Arg313Ter) in the PCCA gene. When we manually checked the binary alignment map (BAM) diagram of PCCA gene, we found a heterozygous deletion chr13:100915039-100915132delinsAA (c.773_819 + 47delinsAA) (GRCh37.p13) inside the exon 10 in the PCCA gene. The results were validated by Sanger sequencing and qPCR method in the family: the variant (c.937C > T, p.Arg313Ter) was in the maternal allele, and the delins was in the paternal allele. When the mother was pregnant again, prenatal diagnosis was carried out through amniocentesis at 18 weeks gestation, the fetus carried neither of the two mutations. After birth, newborn screening was undertaken, the result was negative. CONCLUSIONS: We identified a recurrent c.937C > T and a novel c.773_819 + 47delinsAA mutations in the PCCA gene, which may be the genetic cause of the phenotype of this patient. Our findings expanded the spectrum of causative genotype-phenotype of the PCCA gene. For the cases, the NGS results revealed only a heterozygous mutation in autosomal recessive disease when the gene is associated with phenotypes, it is necessary to manually check the BAM diagram to improve the detection rate. Targeted NGS is an effective technique to detect the various genetic lesions responsible for the PA in one step. Genetic testing is essential for genetic counselling and prenatal diagnosis in the family to avoid birth defects.

Global relationships among traditional reflectance vegetation indices (NDVI and NDII), evapotranspiration (ET), and soil moisture variability on weekly timescales.

Monitoring the effects of water availability on vegetation globally using satellites is important for applications such as drought early warning, precision agriculture, and food security as well as for more broadly understanding relationships between water and carbon cycles. In this global study, we examine how quickly several satellite-based indicators, assumed to have relationships with water availability, respond, on timescales of days to weeks, in comparison with variations in root-zone soil moisture (RZM) that extends to about 1 m depth. The satellite indicators considered are the normalized difference vegetation and infrared indices (NDVI and NDII, respectively) derived from reflectances obtained with moderately wide (20-40 nm) spectral bands in the visible and near-infrared (NIR) and evapotranspiration (ET) estimated from thermal infrared observations and normalized by a reference ET. NDVI is primarily sensitive to chlorophyll contributions and vegetation structure while NDII may contain additional information on water content in leaves and canopy. ET includes both the loss of root zone soil water through transpiration (modulated by stomatal conductance) as well as evaporation from bare soil. We find that variations of these satellite-based drought indicators on time scales of days to weeks have significant correlations with those of RZM in the same water-limited geographical locations that are dominated by grasslands, shrublands, and savannas whose root systems are generally contained within the 1 m RZM layer. Normalized ET interannual variations show generally a faster response to water deficits and enhancements as compared with those of NDVI and NDII, particularly in sparsely vegetated regions.

AI based colorectal disease detection using real-time screening colonoscopy.

Colonoscopy is an effective tool for early screening of colorectal diseases. However, the application of colonoscopy in distinguishing different intestinal diseases still faces great challenges of efficiency and accuracy. Here we constructed and evaluated a deep convolution neural network (CNN) model based on 117 055 images from 16 004 individuals, which achieved a high accuracy of 0.933 in the validation dataset in identifying patients with polyp, colitis, colorectal cancer (CRC) from normal. The proposed approach was further validated on multi-center real-time colonoscopy videos and images, which achieved accurate diagnostic performance on detecting colorectal diseases with high accuracy and precision to generalize across external validation datasets. The diagnostic performance of the model was further compared to the skilled endoscopists and the novices. In addition, our model has potential in diagnosis of adenomatous polyp and hyperplastic polyp with an area under the receiver operating characteristic curve of 0.975. Our proposed CNN models have potential in assisting clinicians in making clinical decisions with efficiency during application.

[Study the effect of antisense MMP-2 by transfection of ad-aMMP-2 on vascular endothelial cells from human proferating hemangiomas in vitro].

OBJECTIVE: To investigate the effect of antisense MMP-2 by transfecting the Ad-aMMP-2 on the vascular endothelial cells (VEC) derived from human proferating hemangiomas in vitro. METHODS: Three groups. Group M199, Group Ad-GFP and Group Ad-aMMP-2 were tested in this study with the 100 multiplicity of infection (MOI). Reverse transcription PCR (RT-PCR), Western Blotting and Gelatin Zymography analyses were applied to evaluate the level of endogenous MMP-2 expression. RESULTS: The decreased expression level of endogenous MMP-2 mRNA and protein excretion of MMP-2 and Gelatin Zymography analyses of liveness of Group Ad-aMMP-2 were observed by comparison with Group M199 and Group Ad-GFP (P<0.05). CONCLUSION: Ad-aMMP-2 could inhibit the secretion of MMP-2 from VEC in vitro.