Gestational diabetes mellitus (GDM) is considered to be a typical condition of glucose intolerance in which a woman previously undiagnosed with diabetes exhibits high levels of blood glucose during the third trimester of pregnancy. It can hence be defined as any degree of intolerance to glucose with its first recognition only during the pregnancy. Approximately 7 % of all cases of pregnancy are found to be variedly complicated with GDM and this result in more than 200,000 cases annually. In US only, GDM has been found to complicate about 7-14 % cases annually, and the trend seems to have increased by 35-100 % in the recent years. A history of GDM can be considered to be one of the sturdiest risk factors concerning the development of type 2 diabetes. Among women who have a history of GDM, the risk of developing classical type 2 diabetes usually ranges from 20 to 50 %. Evidences collected from various efficacy trials suggest that lifestyle interventions like weight management can modulate and prevent type 2 diabetes in at-risk individuals. The cornerstone of GDM management is glycemic control, and hence, it is attributed to be the main focus of attention for the therapy. In this review, we have tried to highlight the various risk factors associated with GDM along with the available therapeutic options in the treatment and management of the disease.

Mutations of SATB2 (OMIM#608148) gene at 2q33.1 have been associated with the autosomal dominant SATB2 -associated syndrome (SAS), which is still short of comprehensive diagnosis technologies for small deletions and low-level mosaicism. In this Chinese Han family, single nucleotide polymorphism array identified a 4.9-kb deletion in the SATB2 gene in two consecutive siblings exhibiting obvious developmental delay and dental abnormalities but failed to find so in their parents. Prenatal diagnosis revealed that their third child carried the same deletion in SATB2 and the pregnancy was terminated. To determine the genetic causes behind the inheritance of SATB2 deletion, gap-PCR was performed on peripheral blood-derived genomic DNA of the family and semen-derived DNA from the father. Gap-PCR that revealed the deletions in the two affected siblings were inherited from the father, while the less intense mutant band indicated the mosaicism of this mutation in the father. The deletion was 3,013 bp in size, spanning from chr2: 200,191,313-200,194,324 (hg19), and covering the entire exon 9 and part of intron 8 and 9 sequences. Droplet digital PCR demonstrated mosaicism percentage of 13.2% and 16.7% in peripheral blood-derived genomic DNA and semen-derived DNA of the father, respectively. Hereby, we describe a family of special AT-rich sequence-binding protein 2-associated syndrome caused by paternal low-level mosaicism and provide effective diagnostic technologies for intragenic deletions.

BackgroundHarlequin ichthyosis (HI) is the most severe form of the keratinizing disorders, and it is characterized by whole-body hard stratum corneum. ABCA12 has been identified as the major disease-causing gene of HI.MethodsA case of HI was prenatally diagnosed by ultrasonography and genetic tests. The fetus had been found with dentofacial deformity and profound thickening of the palm and plantar soft tissues. Chromosomal microarray analysis (CMA) and whole exome sequencing (WES) were then performed on the amniotic fluid to identify germline pathogenic variants for the fetus. Candidate variants were verified by Sanger sequencing.ResultsCompound heterozygous frameshift variants (p.Q719QfsX21; p.F2286LfsX6) of ABCA12 were identified for the fetus, suggesting the former variants were maternally inherited and the latter paternally inherited. The fetus was terminated.ConclusionA prenatal molecular diagnosis is an important approach for the prevention of HI. In the study, we provided a successful case of genetic counseling for a family with an HI baby.

Software vulnerabilities have led to system attacks and data leakage incidents, and software vulnerabilities have gradually attracted attention. Vulnerability detection had become an important research direction. In recent years, Deep Learning (DL)-based methods had been applied to vulnerability detection. The DL-based method does not need to define features manually and achieves low false negatives and false positives. DL-based vulnerability detectors rely on vulnerability datasets. Recent studies found that DL-based vulnerability detectors have different effects on different vulnerability datasets. They also found that the authenticity, imbalance, and repetition rate of vulnerability datasets affect the effectiveness of DL-based vulnerability detectors. However, the existing research only did simple statistics, did not characterize vulnerability datasets, and did not systematically study the impact of vulnerability datasets on DL-based vulnerability detectors. In order to solve the above problems, we propose methods to characterize sample similarity and code features. We use sample granularity, sample similarity, and code features to characterize vulnerability datasets. Then, we analyze the correlation between the characteristics of vulnerability datasets and the results of DL-based vulnerability detectors. Finally, we systematically study the impact of vulnerability datasets on DL-based vulnerability detectors from sample granularity, sample similarity, and code features. We have the following insights for the impact of vulnerability datasets on DL-based vulnerability detectors: (1) Fine-grained samples are conducive to detecting vulnerabilities. (2) Vulnerability datasets with lower inter-class similarity, higher intra-class similarity, and simple structure help detect vulnerabilities in the original test set. (3) Vulnerability datasets with higher inter-class similarity, lower intra-class similarity, and complex structure can better detect vulnerabilities in other datasets.

In this work, based on the channel damage caused by source/drain etching and passivation-layer deposition, the effects of the passivation-layer process on amorphous InGaZnO (a-IGZO) thin-film transistors (TFTs) devices were studied by combining experimental investigation with simulation verification. In terms of experimental exploration, it was found that the back-channel N2O plasma treatment had a significant impact on the performance of the device, which was difficult to control. Hence, to achieve a low cost, the entire back-channel process was directly carried out as two steps of SiO x passivation-layer deposition and final thermal annealing. In the aspect of simulation verification, the influence of the passivation-layer deposition radio-frequency (RF) power and the annealing effect on the internal mechanism of the device was studied based on a high-concentration doped defect density of states (DOS) model (doping level was N D = 1020 cm−3). The experimental results demonstrated that the high-performance of an a-IGZO TFT device can be achieved by adjusting the RF power of SiO x passivation-layer deposition. It was more important that annealing after passivation-layer deposition was a critical step in the manufacture of high-performance TFTs. The device exhibited the ideal performance after annealing under 1000 W RF power, with a threshold voltage of 5.65 V, a saturation mobility of 12.87 cm2 V−1s−1, a subthreshold swing of 0.88 V dec−1, and a current on-off ratio of 2.62 × 10°8. In addition, using the DOS model, it was found that the SiO x passivation-layer process had a significant impact on the DOS distribution and the carrier distribution in the channel, which in turn caused the threshold voltage to drift. At last, the high uniformity and stability of an a-IGZO TFTs array on glass were characterized.