BACKGROUND There is evidence that intrauterine growth restriction, resulting in newborn girls that are small for gestational age (SGA), may be related to the onset of polycystic ovary syndrome ...(PCOS). Thus, we studied whether women born SGA have a higher prevalence of PCOS than women born appropriate for gestational age (AGA). METHODS This was a prospective birth cohort study of 384 women born at term between June 1, 1978, and May 31, 1979, in Ribeirão Preto, Brazil. After exclusion, 165 women effectively participated in this study, of whom 43 were SGA and 122 were AGA. The prevalence of PCOS was analysed. At a mean age of 29 years, the women agreed to follow the study protocol, which included: anamnesis, physical examination, serum tests follicle stimulating hormone, luteinizing hormone, total and free testosterone, dehydroepiandrostenedione sulphate, 17-OH-progesterone, fasting insulin, sex steroid-binding globulin (SHBG) and fasting glucose and pelvic ultrasound. Data regarding gestational age, birthweight, age at menarche and maternal data were obtained from the files of the cohort. The adjusted relative risk (RR) values of the SGA, insulin resistance, body mass index, maternal smoking and parity variables were analysed using Poisson regression with robust adjustment of variance for the prediction of PCOS. RESULTS The prevalence of PCOS was higher in the SGA group than in the AGA group adjusted RR = 2.44, 95% CI (1.39–4.28). Hyperandrogenism was more prevalent in the SGA women than in the AGA women (P = 0.011). Circulating SHBG was lower in the SGA women than in the AGA women (P = 0.041), but fasting insulinemia was similar in both groups. CONCLUSIONS The prevalence of PCOS in SGA women was twice as high as in AGA women in our study population.
The interest in the application of machine learning techniques (MLT) as drug design tools is growing in the last decades. The reason for this is related to the fact that the drug design is very ...complex and requires the use of hybrid techniques. A brief review of some MLT such as self-organizing maps, multilayer perceptron, bayesian neural networks, counter-propagation neural network and support vector machines is described in this paper. A comparison between the performance of the described methods and some classical statistical methods (such as partial least squares and multiple linear regression) shows that MLT have significant advantages. Nowadays, the number of studies in medicinal chemistry that employ these techniques has considerably increased, in particular the use of support vector machines. The state of the art and the future trends of MLT applications encompass the use of these techniques to construct more reliable QSAR models. The models obtained from MLT can be used in virtual screening studies as well as filters to develop/discovery new chemicals. An important challenge in the drug design field is the prediction of pharmacokinetic and toxicity properties, which can avoid failures in the clinical phases. Therefore, this review provides a critical point of view on the main MLT and shows their potential ability as a valuable tool in drug design.
This paper presents a predictive current control method and its application to a voltage source inverter. The method uses a discrete-time model of the system to predict the future value of the load ...current for all possible voltage vectors generated by the inverter. The voltage vector which minimizes a quality function is selected. The quality function used in this work evaluates the current error at the next sampling time. The performance of the proposed predictive control method is compared with hysteresis and pulsewidth modulation control. The results show that the predictive method controls very effectively the load current and performs very well compared with the classical solutions
Background In Brazil, acute Chagas disease (ACD) surveillance involves mandatory notification, which allows for population-based epidemiological studies. We conducted a nationwide population-based ...ecological analysis of the spatiotemporal patterns of ACD notifications in Brazil using secondary surveillance data obtained from the Notifiable Diseases Information System (SINAN) maintained by Brazilian Ministry of Health. Methodology/Principal findings In this nationwide population-based ecological all cases of ACD reported in Brazil between 2001 and 2018 were included. Epidemiological characteristics and time trends were analyzed through joinpoint regression models and spatial distribution using microregions as the unit of analysis. A total of 5,184 cases of ACD were recorded during the period under study. The annual incidence rate in Brazil was 0.16 per 100,000 inhabitants/year. Three statistically significant changes in time trends were identified: a rapid increase prior to 2005 (Period 1), a stable drop from 2005 to 2009 (Period 2), followed by another increasing trend after 2009 (Period 3). Higher frequencies were noted in males and females in the North (all three periods) and in females in Northeast (Periods 1 and 2) macroregions, as well as in individuals aged between 20-64 years in the Northeast, and children, adolescents and the elderly in the North macroregion. Vectorial transmission was the main route reported during Period 1, while oral transmission was found to increase significantly in the North during the other periods. Spatiotemporal distribution was heterogeneous in Brazil over time. Despite regional differences, over time cases of ACD decreased significantly nationwide. An increasing trend was noted in the North (especially after 2007), and significant decreases occurred after 2008 among all microregions other than those in the North, especially those in the Northeast and Central-West macroregions. Conclusions/Significance In light of the newly identified epidemiological profile of CD transmission in Brazil, we emphasize the need for strategically integrated entomological and health surveillance actions.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The minimal geometric deformation (MGD), associated with the 4D Schwarzschild solution of the Einstein equations, is shown to be a solution of the pure 4D Ricci quadratic gravity theory, whose linear ...perturbations are then implemented by the Gregory–Laflamme eigentensors of the Lichnerowicz operator. The stability of MGD black strings is hence studied, through the correspondence between their Lichnerowicz eigenmodes and the ones associated with the 4D MGD solutions. It is shown that there exists a critical mass driving the MGD black strings stability, above which the MGD black string is precluded from any Gregory–Laflamme instability. The general-relativistic limit shows the MGD black string to be unstable, as expected, corresponding to the standard Gregory–Laflamme black string instability.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Abstract
The age of precision medicine demands powerful computational techniques to handle high-dimensional patient data. We present MultiSurv, a multimodal deep learning method for long-term ...pan-cancer survival prediction. MultiSurv uses dedicated submodels to establish feature representations of clinical, imaging, and different high-dimensional omics data modalities. A data fusion layer aggregates the multimodal representations, and a prediction submodel generates conditional survival probabilities for follow-up time intervals spanning several decades. MultiSurv is the first non-linear and non-proportional survival prediction method that leverages multimodal data. In addition, MultiSurv can handle missing data, including single values and complete data modalities. MultiSurv was applied to data from 33 different cancer types and yields accurate pan-cancer patient survival curves. A quantitative comparison with previous methods showed that Multisurv achieves the best results according to different time-dependent metrics. We also generated visualizations of the learned multimodal representation of MultiSurv, which revealed insights on cancer characteristics and heterogeneity.
Recent advances have improved our understanding of the renin‐angiotensin system (RAS). These have included the recognition that angiotensin (Ang)‐(1‐7) is a biologically active product of the RAS ...cascade. The identification of the ACE homologue ACE2, which forms Ang‐(1‐7) from Ang II, and the GPCR Mas as an Ang‐(1‐7) receptor have provided the necessary biochemical and molecular background and tools to study the biological significance of Ang‐(1‐7). Most available evidence supports a counter‐regulatory role for Ang‐(1‐7) by opposing many actions of Ang II on AT1 receptors, especially vasoconstriction and proliferation. Many studies have now shown that Ang‐(1‐7) by acting via Mas receptor exerts inhibitory effects on inflammation and on vascular and cellular growth mechanisms. Ang‐(1‐7) has also been shown to reduce key signalling pathways and molecules thought to be relevant for fibrogenesis. Here, we review recent findings related to the function of the ACE2/Ang‐(1‐7)/Mas axis and focus on the role of this axis in modifying processes associated with acute and chronic inflammation, including leukocyte influx, fibrogenesis and proliferation of certain cell types. More attention will be given to the involvement of the ACE2/Ang‐(1‐7)/Mas axis in the context of renal disease because of the known relevance of the RAS for the function of this organ and for the regulation of kidney inflammation and fibrosis. Taken together, this knowledge may help in paving the way for the development of novel treatments for chronic inflammatory and renal diseases.
Molybdates are biocidal materials that can be useful in coating surfaces that are susceptible to contamination and the spread of microorganisms. The aim of this work was to investigate the effects of ...copper doping of hydrated cobalt molybdate, synthesized by the co-precipitation method, on its antibacterial activity and to elucidate the structural and morphological changes caused by the dopant in the material. The synthesized materials were characterized by PXRD, Fourier Transformed Infrared (FTIR), thermogravimetric analysis/differential scanning calorimetry (TG/DSC), and SEM-Energy Dispersive Spectroscopy (SEM-EDS). The antibacterial response of the materials was verified using the Minimum Inhibitory Concentration (MIC) employing the broth microdilution method. The size of the CoMoO
·1.03H
O microparticles gradually increased as the percentage of copper increased, decreasing the energy that is needed to promote the transition from the hydrated to the beta phase and changing the color of material. CoMoO
·1.03H
O obtained better bactericidal performance against the tested strains of
(gram-positive) than
(gram-negative). However, an interesting point was that the use of copper as a doping agent for hydrated cobalt molybdate caused an increase of MIC value in the presence of
and
strains. The study demonstrates the need for caution in the use of copper as a doping material in biocidal matrices, such as cobalt molybdate.
Following a brief introduction to the principle of fluorescent PET (photoinduced electron transfer) sensors and switches, the outputs of laboratories in various countries from the past year or two ...are categorized and critically discussed. Emphasis is placed on the molecular design and the experimental outcomes in terms of target-induced fluorescence enhancements and input/output wavelengths. The handling of single targets takes up a major fraction of the review, but the extension to multiple targets is also illustrated. Conceptually new channels of investigation are opened up by the latter approach,
e.g.
'lab-on-a-molecule' systems and molecular keypad locks. The growing trends of theoretically-fortified design and intracellular application are pointed out.
A fluorophore can be combined with a receptor according to a molecular engineering design in order to yield fluorescent sensing and switching devices.
Among brain tumors, gliomas are the most common and aggressive, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life ...of oncological patients. Magnetic resonance imaging (MRI) is a widely used imaging technique to assess these tumors, but the large amount of data produced by MRI prevents manual segmentation in a reasonable time, limiting the use of precise quantitative measurements in the clinical practice. So, automatic and reliable segmentation methods are required; however, the large spatial and structural variability among brain tumors make automatic segmentation a challenging problem. In this paper, we propose an automatic segmentation method based on Convolutional Neural Networks (CNN), exploring small 3 ×3 kernels. The use of small kernels allows designing a deeper architecture, besides having a positive effect against overfitting, given the fewer number of weights in the network. We also investigated the use of intensity normalization as a pre-processing step, which though not common in CNN-based segmentation methods, proved together with data augmentation to be very effective for brain tumor segmentation in MRI images. Our proposal was validated in the Brain Tumor Segmentation Challenge 2013 database (BRATS 2013), obtaining simultaneously the first position for the complete, core, and enhancing regions in Dice Similarity Coefficient metric (0.88, 0.83, 0.77) for the Challenge data set. Also, it obtained the overall first position by the online evaluation platform. We also participated in the on-site BRATS 2015 Challenge using the same model, obtaining the second place, with Dice Similarity Coefficient metric of 0.78, 0.65, and 0.75 for the complete, core, and enhancing regions, respectively.
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