Aberrant expression of semaphorin 6B affects cell phenotypes in thyroid carcinoma by activating the Notch signalling pathway

Introduction: Numerous semaphorins have been widely clarified to be involved in the development of multiple cancers. However, semaphorin 6B (SEMA6B) has not yet been extensively reported in cancers, especially in thyroid carcinoma. Material and methods: Thyroid carcinoma RNA-Seq dataset from the TCGA database was used to assess the expression of SEMA6B in tissues, as well as its clinical significance. We adopted qRT-PCR and western blot analyses to measure the mRNA and protein expression of SEMA6B in thyroid carcinoma cells. The biological roles of SEMA6B in thyroid carcinoma cells were examined through cell counting kit 8, clone formation, and Transwell assays. Also, GSEA was used to identify the gene sets modulated by SEMA6B, which is further verified by western blot. Results: According to the public dataset from the TCGA database, we found that the expression of SEMA6B was upregulated in thyroid carcinoma tissues compared to adjacent non-tumour tissues, and a high level of SEMA6B resulted in a poorer prognosis compared to the low-level SEMA6B group. Functional experiments showed that silencing SEMA6B suppressed the B-CPAP cells viability, invasiveness, and motility, whereas up-regulating SEMA6B in FTC-133 cells led to opposite outcomes. Furthermore, knockdown of SEMA6B in B-CPAP cells could significantly elevate the protein expression of NUMB and reduce the expression of NOTCH1, HES1, and Cyclin D1. Conversely, overexpression of SEMA6B in FTC-133 cells presented opposite results on the protein expression of these Notch signalling pathway-related markers. Conclusions: Our findings demonstrated that SEMA6B exerts a tumourigenic effect in thyroid carcinoma partly by activating Notch signalling pathway, which provides a possible biomarker for the therapeutic intervention in thyroid carcinoma

Lower Extremity Peripheral Arterial Disease Is an Independent Predictor of Coronary Heart Disease and Stroke Risks in Patients with Type 2 Diabetes Mellitus in China

We aimed to determine the relationship between lower extremity peripheral arterial disease (PAD), 10-year coronary heart disease (CHD), and stroke risks in patients with type 2 diabetes (T2DM) using the UKPDS risk engine. We enrolled 1178 hospitalized T2DM patients. The patients were divided into a lower extremity PAD group (ankle-brachial index≤0.9 or >1.4; 88 patients, 7.5%) and a non-PAD group (ankle-brachial index>0.9 and ≤1.4; 1090 patients, 92.5%). Age; duration of diabetes; systolic blood pressure; the hypertension rate; the use of hypertension drugs, ACEI /ARB, and statins; CHD risk; fatal CHD risk; stroke risk; and fatal stroke risk were significantly higher in the PAD group than in the non-PAD group (P<0.05 for all). Logistic stepwise regression analysis indicated that ABI was an independent predictor of 10-year CHD and stroke risks in T2DM patients. Compared with those in the T2DM non-PAD group, the odds ratios (ORs) for CHD and stroke risk were 3.6 (95% confidence interval (CI), 2.2–6.0; P<0.001) and 6.9 (95% CI, 4.0–11.8; P<0.001) in those with lower extremity PAD, respectively. In conclusion, lower extremity PAD increased coronary heart disease and stroke risks in T2DM

A novel ATP dependent dimethylsulfoniopropionate lyase in bacteria that releases dimethyl sulfide and acryloyl-CoA

Dimethylsulfoniopropionate (DMSP) is an abundant and ubiquitous organosulfur molecule in marine environments with important roles in global sulfur and nutrient cycling. Diverse DMSP lyases in some algae, bacteria and fungi cleave DMSP to yield gaseous dimethyl sulfide (DMS), an infochemical with important roles in atmospheric chemistry. Here we identified a novel ATP-dependent DMSP lyase, DddX. DddX belongs to the acyl-CoA synthetase superfamily and is distinct from the eight other known DMSP lyases. DddX catalyses the conversion of DMSP to DMS via a two-step reaction: the ligation of DMSP with CoA to form the intermediate DMSP-CoA, which is then cleaved to DMS and acryloyl-CoA. The novel catalytic mechanism was elucidated by structural and biochemical analyses. DddX is found in several Alphaproteobacteria, Gammaproteobacteria and Firmicutes, suggesting that this new DMSP lyase may play an overlooked role in DMSP/DMS cycles

Molecular footprints of domestication and improvement in soybean revealed by whole genome re-sequencing

BACKGROUND: Artificial selection played an important role in the origin of modern Glycine max cultivars from the wild soybean Glycine soja. To elucidate the consequences of artificial selection accompanying the domestication and modern improvement of soybean, 25 new and 30 published whole-genome re-sequencing accessions, which represent wild, domesticated landrace, and Chinese elite soybean populations were analyzed. RESULTS: A total of 5,102,244 single nucleotide polymorphisms (SNPs) and 707,969 insertion/deletions were identified. Among the SNPs detected, 25.5% were not described previously. We found that artificial selection during domestication led to more pronounced reduction in the genetic diversity of soybean than the switch from landraces to elite cultivars. Only a small proportion (2.99%) of the whole genomic regions appear to be affected by artificial selection for preferred agricultural traits. The selection regions were not distributed randomly or uniformly throughout the genome. Instead, clusters of selection hotspots in certain genomic regions were observed. Moreover, a set of candidate genes (4.38% of the total annotated genes) significantly affected by selection underlying soybean domestication and genetic improvement were identified. CONCLUSIONS: Given the uniqueness of the soybean germplasm sequenced, this study drew a clear picture of human-mediated evolution of the soybean genomes. The genomic resources and information provided by this study would also facilitate the discovery of genes/loci underlying agronomically important traits

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Obstructive Sleep Apnea Syndrome is Associated with Metabolic Syndrome among Adolescents and Youth in Beijing: Data from Beijing Child and Adolescent Metabolic Syndrome Study

Background: Obstructive sleep apnea (OSA) syndrome has a negative impact on the health of millions of adolescents and youth. The aim of this study was to evaluate the associations of OSA syndrome with obesity and cardiometabolic risk factors among adolescents and youth at risk for metabolic syndrome (MS). Methods: A total of 558 subjects aged 14–28 years were recruited from the Beijing Child and Adolescent Metabolic Syndrome Study. Each underwent a 2-h oral glucose tolerance test (OGTT), echocardiography, and liver ultrasonography. Anthropometric measures, blood levels of glucose, lipids, and liver enzymes were assessed. Subjects with high or low risk for OSA were identified by Berlin Questionnaire (BQ). Results: Among the subjects in obesity, 33.7% of whom were likely to have OSA by BQ. Subjects with high risk for OSA had higher neck and waist circumference and fat mass percentage compared to those with low risk for OSA (P < 0.001). Moreover, significant differences in levels of lipids, glucose after OGTT, and liver enzymes, as well as echocardiographic parameters were found between the two groups with high or low risk for OSA (P < 0.05). The rates of nonalcoholic fatty liver disease (71.0% vs. 24.2%), MS (38.9% vs. 7.0%), and its components in high-risk group were significantly higher than in low-risk group. Conclusions: The prevalence of OSA by BQ was high in obese adolescents and youth. A high risk for OSA indicates a high cardiometabolic risk. Mechanisms mediating the observed associations require further investigation

Polydatin Attenuates Hypoxic Pulmonary Hypertension and Reverses Remodeling through Protein Kinase C Mechanisms

These authors contributed equally to this work. * Authors to whom correspondence should be addressed

Machine Learning Assisted Doppler Features for Enhancing Thyroid Cancer Diagnosis: A Multi-cohort Study

Background: This pilot study aims at exploiting machine learning techniques to extract colour Doppler Ultrasound (CDUS) features and to build an artificial neural network (ANN) model based on these CDUS features for improving the diagnostic performance of thyroid cancer classification. Methods: A total of 674 patients with 712 thyroid nodules (TNs) (512 from in-ternal dataset and 200 from external dataset) were randomly selected in this retrospective study. We used ANN to build a model (TDUS-Net) for classifying malignant and benign TNs using both the automatically extracted quantitative CDUS features (whole ratio, intranodular ratio, peripheral ratio, and number of vessels) and grey-scale Ultrasound (US) features defined by the ACR Thyroid Imaging Reporting and Data System (TI-RADS). Then, we compared the diagnostic performance of the model, the performance of another ANN model based on the grey-scale US features alone (TUS-Net), and that of radiologists. Results: The TDUS-Net (0.898, 95%CI: 0.868-0.922) achieved a higher area under the curve (AUC) than that of TUS-Net (0.881, 95%CI: 0.850-0.908) in the internal tests. Compared with radiologists, TDUS-Net (AUC: 0.925, 95% CI: 0.880-0.958) performed better than radiologists (AUC: 0.810, 95% CI: 0.749-0.862) in the external tests. Conclusions: Applying a machine learning model by combining both gray-scale US features and CDUS features can achieve comparable or even higher performance than radiologists in classifying thyroid nodules