Arsenic Trioxide Enhances the Radiation Sensitivity of Androgen-Dependent and -Independent Human Prostate Cancer Cells

Prostate cancer is the most common malignancy in men. In the present study, LNCaP (androgen-sensitive human prostate cancer cells) and PC-3 cells (androgen-independent human prostate cancer cells) were used to investigate the anti-cancer effects of ionizing radiation (IR) combined with arsenic trioxide (ATO) and to determine the underlying mechanisms in vitro and in vivo. We found that IR combined with ATO increases the therapeutic efficacy compared to individual treatments in LNCaP and PC-3 human prostate cancer cells. In addition, combined treatment showed enhanced reactive oxygen species (ROS) generation compared to treatment with ATO or IR alone in PC-3 cells. Combined treatment induced autophagy and apoptosis in LNCaP cells, and mainly induced autophagy in PC-3 cells. The cell death that was induced by the combined treatment was primarily the result of inhibition of the Akt/mTOR signaling pathways. Furthermore, we found that the combined treatment of cells pre-treated with 3-MA resulted in a significant change in AO-positive cells and cytotoxicity. In an in vivo study, the combination treatment had anti-tumor growth effects. These novel findings suggest that combined treatment is a potential therapeutic strategy not only for androgen-dependent prostate cancer but also for androgen-independent prostate cancer

Proliferative and anti-proliferative effects of dietary levels of phytoestrogens in rat pituitary GH3/B6/F10 cells - the involvement of rapidly activated kinases and caspases

<p>Abstract</p> <p>Background</p> <p>Phytoestogens are a group of lipophillic plant compounds that can have estrogenic effects in animals; both tumorigenic and anti-tumorigenic effects have been reported. Prolactin-secreting adenomas are the most prevalent form of pituitary tumors in humans and have been linked to estrogen exposures. We examined the proliferative effects of phytoestrogens on a rat pituitary tumor cell line, GH₃/B₆/F₁₀, originally subcloned from GH₃cells based on its ability to express high levels of the membrane estrogen receptor-α.</p> <p>Methods</p> <p>We measured the proliferative effects of these phytoestrogens using crystal violet staining, the activation of several mitogen-activated protein kinases (MAPKs) and their downstream targets via a quantitative plate immunoassay, and caspase enzymatic activities.</p> <p>Results</p> <p>Four phytoestrogens (coumestrol, daidzein, genistein, and <it>trans</it>-resveratrol) were studied over wide concentration ranges. Except <it>trans</it>-resveratrol, all phytoestrogens increased GH₃/B₆/F₁₀cell proliferation at some concentration relevant to dietary levels. All four phytoestrogens attenuated the proliferative effects of estradiol when administered simultaneously. All phytoestrogens elicited MAPK and downstream target activations, but with time course patterns that often differed from that of estradiol and each other. Using selective antagonists, we determined that MAPKs play a role in the ability of these phytoestrogens to elicit these responses. In addition, except for <it>trans</it>-resveratrol, a serum removal-induced extrinsic apoptotic pathway was blocked by these phytoestrogens.</p> <p>Conclusion</p> <p>Phytoestrogens can block physiological estrogen-induced tumor cell growth <it>in vitro </it>and can also stimulate growth at high dietary concentrations in the absence of endogenous estrogens; these actions are correlated with slightly different signaling response patterns. Consumption of these compounds should be considered in strategies to control endocrine tumor cell growth, such as in the pituitary.</p

Group B Streptococcus vaccine development: present status and future considerations, with emphasis on perspectives for low and middle income countries.

Globally, group B Streptococcus (GBS) remains the leading cause of sepsis and meningitis in young infants, with its greatest burden in the first 90 days of life. Intrapartum antibiotic prophylaxis (IAP) for women at risk of transmitting GBS to their newborns has been effective in reducing, but not eliminating, the young infant GBS disease burden in many high income countries. However, identification of women at risk and administration of IAP is very difficult in many low and middle income country (LMIC) settings, and is not possible for home deliveries. Immunization of pregnant women with a GBS vaccine represents an alternate pathway to protecting newborns from GBS disease, through the transplacental antibody transfer to the fetus in utero. This approach to prevent GBS disease in young infants is currently under development, and is approaching late stage clinical evaluation. This manuscript includes a review of the natural history of the disease, global disease burden estimates, diagnosis and existing control options in different settings, the biological rationale for a vaccine including previous supportive studies, analysis of current candidates in development, possible correlates of protection and current status of immunogenicity assays. Future potential vaccine development pathways to licensure and use in LMICs, trial design and implementation options are discussed, with the objective to provide a basis for reflection, rather than recommendations

Effects of rapid urbanisation on the urban thermal environment between 1990 and 2011 in Dhaka Megacity, Bangladesh

This study investigates the influence of land-use/land-cover (LULC) change on land surface temperature (LST) in Dhaka Megacity, Bangladesh during a period of rapid urbanisation. LST was derived from Landsat 5 TM scenes captured in 1990, 2000 and 2011 and compared to contemporaneous LULC maps. We compared index-based and linear spectral mixture analysis (LSMA) techniques for modelling LST. LSMA derived biophysical parameters corresponded more strongly to LST than those produced using index-based parameters. Results indicated that vegetation and water surfaces had relatively stable LST but it increased by around 2 °C when these surfaces were converted to built-up areas with extensive impervious surfaces. Knowledge of the expected change in LST when one land-cover is converted to another can inform land planners of the potential impact of future changes and urges the development of better management strategies

Guidelines for Designing Social Robots as Second Language Tutors

In recent years, it has been suggested that social robots have potential as tutors and educators for both children and adults. While robots have been shown to be effective in teaching knowledge and skill-based topics, we wish to explore how social robots can be used to tutor a second language to young children. As language learning relies on situated, grounded and social learning, in which interaction and repeated practice are central, social robots hold promise as educational tools for supporting second language learning. This paper surveys the developmental psychology of second language learning and suggests an agenda to study how core concepts of second language learning can be taught by a social robot. It suggests guidelines for designing robot tutors based on observations of second language learning in human–human scenarios, various technical aspects and early studies regarding the effectiveness of social robots as second language tutors

Evaluation of appendicitis risk prediction models in adults with suspected appendicitis

Background Appendicitis is the most common general surgical emergency worldwide, but its diagnosis remains challenging. The aim of this study was to determine whether existing risk prediction models can reliably identify patients presenting to hospital in the UK with acute right iliac fossa (RIF) pain who are at low risk of appendicitis. Methods A systematic search was completed to identify all existing appendicitis risk prediction models. Models were validated using UK data from an international prospective cohort study that captured consecutive patients aged 16–45 years presenting to hospital with acute RIF in March to June 2017. The main outcome was best achievable model specificity (proportion of patients who did not have appendicitis correctly classified as low risk) whilst maintaining a failure rate below 5 per cent (proportion of patients identified as low risk who actually had appendicitis). Results Some 5345 patients across 154 UK hospitals were identified, of which two‐thirds (3613 of 5345, 67·6 per cent) were women. Women were more than twice as likely to undergo surgery with removal of a histologically normal appendix (272 of 964, 28·2 per cent) than men (120 of 993, 12·1 per cent) (relative risk 2·33, 95 per cent c.i. 1·92 to 2·84; P < 0·001). Of 15 validated risk prediction models, the Adult Appendicitis Score performed best (cut‐off score 8 or less, specificity 63·1 per cent, failure rate 3·7 per cent). The Appendicitis Inflammatory Response Score performed best for men (cut‐off score 2 or less, specificity 24·7 per cent, failure rate 2·4 per cent). Conclusion Women in the UK had a disproportionate risk of admission without surgical intervention and had high rates of normal appendicectomy. Risk prediction models to support shared decision‐making by identifying adults in the UK at low risk of appendicitis were identified

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

A study of multilayer perceptron networks applied to classification of ceramic insulators using ultrasound

Interruptions in the supply of electricity cause numerous losses to consumers, whether residential or industrial and may result in fines being imposed on the regulatory agency’s concessionaire. In Brazil, the electrical transmission and distribution systems cover a large territorial area, and because they are usually outdoors, they are exposed to environmental variations. In this context, periodic inspections are carried out on the electrical networks, and ultrasound equipment is widely used, due to non-destructive analysis characteristics. Ultrasonic inspection allows the identification of defective insulators based on the signal interpreted by an operator. This task fundamentally depends on the operator’s experience in this interpretation. In this way, it is intended to test machine learning applications to interpret ultrasound signals obtained from electrical grid insulators, distribution, class 25 kV. Currently, research in the area uses several models of artificial intelligence for various types of evaluation. This paper studies Multilayer Perceptron networks’ application to the classification of the different conditions of ceramic insulators based on a restricted database of ultrasonic signals recorded in the laboratory

Echo state network applied for classification of medium voltage insulators

Insulators are components of electrical power grid that have the function of mechanically supporting cables and isolating electrical potential. The proper functioning of the insulators is essential for the continuity in the supply of electrical energy. When an insulator has its properties damaged, disruptive discharges may shut down the system and impairs the network's reliability. For this reason, classifying adverse conditions is a critical task to keep the system running. In this paper, the echo state network is used for the classification of the insulators based on the ultrasound signal. Hypertuning is applied to automate the evaluation of the parameters and optimize the network to have a general application. The insulators are evaluated in the laboratory under controlled conditions from the application of 7.95 kV (phase-to-ground), under the same conditions that are found in the field. The assessment is made on perforated and contaminated insulators, which were removed from service due to defects. The echo state network achieves 87.36 % accuracy for the multiclassification and 99.99 % for the specific classification of drilling. For comparative analysis, the multilayer perceptron and support-vector machines are evaluated based on the fast fourier transform. The results show that echo state network is promising to classify the evaluated conditions, being more accurate than the multilayer perceptron and support-vector machines based on fast fourier transform