Comparison of the Effects of Neonicotinoids and Pyrethroids Against Oebalus pugnax (Hemiptera: Pentatomidae) in Rice

The rice stink bug, Oebalus pugnax (F.) (Hemiptera: Pentatomidae), is an economically important late-season pest of rice in the southern United States. Stink bug feeding results in yield reduction and discounted purchase price due to broken or discolored ( pecky ) rice grains. The primary tactic for O. pugnax management is the application of insecticides once adults reach an action threshold. Recent surveys show that pyrethroids are preferred by southern U.S. rice farmers over all other insecticides to reduce O. pugnax densities. However, preliminary tests in 2009 suggested resistance to pyrethroids may be developing in an O. pugnax population in Texas, where applications are more frequent than in other rice-growing areas. This study compared the effects of pyrethroids and neonicotinoids on O. pugnax behavior and mortality in the laboratory and in a number of field experiments conducted between 2011 and 2014. Results from these experiments showed that control of O. pugnax given by the neonicotinoid, dinotefuran, was similar to that given by pyrethroids in the laboratory and field. Results from small-plot field studies were influenced by movement of adult rice stink bugs from surrounding untreated plots, and the data from commercial-scale trials and from sampling of nymphs in small plots may provide more useful information on the efficacies of insecticides. Two experiments provided limited evidence for longer residual activity of dinotefuran compared to the pyrethroid-cyhalothrin, and a laboratory study showed that both insecticides reduced feeding activity of rice stink bugs. Tests also confirmed the increased tolerance of a Texas population of rice stink bugs to-cyhalothrin, suggesting the need for insecticides with different modes of action in the O. pugnax management program

Human Endometrial CD98 Is Essential for Blastocyst Adhesion

BACKGROUND: Understanding the molecular basis of embryonic implantation is of great clinical and biological relevance. Little is currently known about the adhesion receptors that determine endometrial receptivity for embryonic implantation in humans. METHODS AND PRINCIPAL FINDINGS: Using two human endometrial cell lines characterized by low and high receptivity, we identified the membrane receptor CD98 as a novel molecule selectively and significantly associated with the receptive phenotype. In human endometrial samples, CD98 was the only molecule studied whose expression was restricted to the implantation window in human endometrial tissue. CD98 expression was restricted to the apical surface and included in tetraspanin-enriched microdomains of primary endometrial epithelial cells, as demonstrated by the biochemical association between CD98 and tetraspanin CD9. CD98 expression was induced in vitro by treatment of primary endometrial epithelial cells with human chorionic gonadotropin, 17-β-estradiol, LIF or EGF. Endometrial overexpression of CD98 or tetraspanin CD9 greatly enhanced mouse blastocyst adhesion, while their siRNA-mediated depletion reduced the blastocyst adhesion rate. CONCLUSIONS: These results indicate that CD98, a component of tetraspanin-enriched microdomains, appears to be an important determinant of human endometrial receptivity during the implantation window

Natural History of Liver Disease in a Large International Cohort of Children with Alagille syndrome:Results from The GALA Study

BACKGROUND: Alagille syndrome (ALGS) is a multisystem disorder, characterized by cholestasis. Existing outcome data are largely derived from tertiary centers and real-world data are lacking. This study aimed to elucidate the natural history of liver disease in a contemporary, international, cohort of children with ALGS.METHODS: Multicenter retrospective study of children with a clinically and/or genetically confirmed ALGS diagnosis, born Jan-1997 - Aug-2019. Native liver survival (NLS) and event-free survival rates were assessed. Cox models were constructed to identify early biochemical predictors of clinically evident portal hypertension (CEPH) and NLS.RESULTS: 1433 children (57% male) from 67 centers in 29 countries were included. 10 and 18-years NLS rates were 54.4% and 40.3%. By 10 and 18-years, 51.5% and 66.0% of ALGS children experienced ≥1 adverse liver-related event (CEPH, transplant or death). Children (&gt;6 and ≤12 months) with median total bilirubin (TB) levels between ≥5.0 and &lt;10.0 mg/dL had a 4.1-fold (95% CI 1.6 - 10.8) and those ≥10.0 mg/dL had an 8.0-fold (95% CI 3.4 - 18.4) increased risk of developing CEPH compared with those &lt;5.0 mg/dL. Median TB levels between ≥5.0 and &lt;10.0 mg/dL and &gt;10.0 mg/dL were associated with a 4.8 (95% CI 2.4 - 9.7) and 15.6 (95% CI 8.7 - 28.2) increased risk of transplantation relative to &lt;5.0 mg/dL. Median TB &lt;5.0 mg/dL were associated with higher NLS rates relative to ≥5.0 mg/dL, with 79% reaching adulthood with native liver (p&lt;0.001).CONCLUSIONS: In this large international cohort of ALGS, only 40.3% of children reach adulthood with their native liver. A TB &lt;5.0 mg/dL between 6-and-12-months of age is associated with better hepatic outcomes. These thresholds provide clinicians with an objective tool to assist with clinical decision-making and in the evaluation of novel therapies.</p

The IDENTIFY study: the investigation and detection of urological neoplasia in patients referred with suspected urinary tract cancer - a multicentre observational study

Objective To evaluate the contemporary prevalence of urinary tract cancer (bladder cancer, upper tract urothelial cancer [UTUC] and renal cancer) in patients referred to secondary care with haematuria, adjusted for established patient risk markers and geographical variation. Patients and Methods This was an international multicentre prospective observational study. We included patients aged ≥16 years, referred to secondary care with suspected urinary tract cancer. Patients with a known or previous urological malignancy were excluded. We estimated the prevalence of bladder cancer, UTUC, renal cancer and prostate cancer; stratified by age, type of haematuria, sex, and smoking. We used a multivariable mixed-effects logistic regression to adjust cancer prevalence for age, type of haematuria, sex, smoking, hospitals, and countries. Results Of the 11 059 patients assessed for eligibility, 10 896 were included from 110 hospitals across 26 countries. The overall adjusted cancer prevalence (n = 2257) was 28.2% (95% confidence interval [CI] 22.3–34.1), bladder cancer (n = 1951) 24.7% (95% CI 19.1–30.2), UTUC (n = 128) 1.14% (95% CI 0.77–1.52), renal cancer (n = 107) 1.05% (95% CI 0.80–1.29), and prostate cancer (n = 124) 1.75% (95% CI 1.32–2.18). The odds ratios for patient risk markers in the model for all cancers were: age 1.04 (95% CI 1.03–1.05; P < 0.001), visible haematuria 3.47 (95% CI 2.90–4.15; P < 0.001), male sex 1.30 (95% CI 1.14–1.50; P < 0.001), and smoking 2.70 (95% CI 2.30–3.18; P < 0.001). Conclusions A better understanding of cancer prevalence across an international population is required to inform clinical guidelines. We are the first to report urinary tract cancer prevalence across an international population in patients referred to secondary care, adjusted for patient risk markers and geographical variation. Bladder cancer was the most prevalent disease. Visible haematuria was the strongest predictor for urinary tract cancer

Deep learning enables automated scoring of liver fibrosis stages

Current liver fibrosis scoring by computer-assisted image analytics is not fully automated as it requires manual preprocessing (segmentation and feature extraction) typically based on domain knowledge in liver pathology. Deep learning-based algorithms can potentially classify these images without the need for preprocessing through learning from a large dataset of images. We investigated the performance of classification models built using a deep learning-based algorithm pre-trained using multiple sources of images to score liver fibrosis and compared them against conventional non-deep learning-based algorithms - artificial neural networks (ANN), multinomial logistic regression (MLR), support vector machines (SVM) and random forests (RF). Automated feature classification and fibrosis scoring were achieved by using a transfer learning-based deep learning network, AlexNet-Convolutional Neural Networks (CNN), with balanced area under receiver operating characteristic (AUROC) values of up to 0.85–0.95 versus ANN (AUROC of up to 0.87–1.00), MLR (AUROC of up to 0.73–1.00), SVM (AUROC of up to 0.69–0.99) and RF (AUROC of up to 0.94–0.99). Results indicate that a deep learning-based algorithm with transfer learning enables the construction of a fully automated and accurate prediction model for scoring liver fibrosis stages that is comparable to other conventional non-deep learning-based algorithms that are not fully automated