Data mining in HIV-AIDS surveillance system: application to portuguese data

The Human Immunodeficiency Virus (HIV) is an infectious agent that attacks the immune system cells. Without a strong immune system, the body becomes very susceptible to serious life threatening opportunistic diseases. In spite of the great progresses on medication and prevention over the last years, HIV infection continues to be a major global public health issue, having claimed more than 36 million lives over the last 35 years since the recognition of the disease. Monitoring, through registries, of HIV-AIDS cases is vital to assess general health care needs and to support long-term health-policy control planning. Surveillance systems are therefore established in almost all developed countries. Typically, this is a complex system depending on several stakeholders, such as health care providers, the general population and laboratories, which challenges an efficient and effective reporting of diagnosed cases. One issue that often arises is the administrative delay in reports of diagnosed cases. This paper aims to identify the main factors influencing reporting delays of HIV-AIDS cases within the portuguese surveillance system. The used methodologies included multilayer artificial neural networks (MLP), naive bayesian classifiers (NB), support vector machines (SVM) and the k-nearest neighbor algorithm (KNN). The highest classification accuracy, precision and recall were obtained for MLP and the results suggested homogeneous administrative and clinical practices within the reporting process. Guidelines for reductions of the delays should therefore be developed nationwise and transversally to all stakeholders.- A. Rita Gaio was partially supported by CMUP (UID/MAT/00144/2013), which is funded by FCT (Portugal) with national (MEC) and European structural funds (FEDER), under the partnership agreement PT2020. Luis Paulo Reis was partially by the European Regional Development Fund through the programme COMPETE by FCT (Portugal) in the scope of the project PEst - UID/ CEC/00027/2015 Luis Paulo Reis and Brigida Monica Faria were partially funded by QVida+: Estimacao Continua de Qualidade de Vida para Auxilio Eficaz a Decisao Clinica, NORTE-01-0247-FEDER-003446, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement

Classifications for Cesarean Section: A Systematic Review

Background: Rising cesarean section (CS) rates are a major public health concern and cause worldwide debates. To propose and implement effective measures to reduce or increase CS rates where necessary requires an appropriate classification. Despite several existing CS classifications, there has not yet been a systematic review of these. This study aimed to 1) identify the main CS classifications used worldwide, 2) analyze advantages and deficiencies of each system.Methods and Findings: Three electronic databases were searched for classifications published 1968-2008. Two reviewers independently assessed classifications using a form created based on items rated as important by international experts. Seven domains (ease, clarity, mutually exclusive categories, totally inclusive classification, prospective identification of categories, reproducibility, implementability) were assessed and graded. Classifications were tested in 12 hypothetical clinical case-scenarios. From a total of 2948 citations, 60 were selected for full-text evaluation and 27 classifications identified. Indications classifications present important limitations and their overall score ranged from 2-9 (maximum grade = 14). Degree of urgency classifications also had several drawbacks (overall scores 6-9). Woman-based classifications performed best (scores 5-14). Other types of classifications require data not routinely collected and may not be relevant in all settings (scores 3-8).Conclusions: This review and critical appraisal of CS classifications is a methodologically sound contribution to establish the basis for the appropriate monitoring and rational use of CS. Results suggest that women-based classifications in general, and Robson's classification, in particular, would be in the best position to fulfill current international and local needs and that efforts to develop an internationally applicable CS classification would be most appropriately placed in building upon this classification. the use of a single CS classification will facilitate auditing, analyzing and comparing CS rates across different settings and help to create and implement effective strategies specifically targeted to optimize CS rates where necessary.Universidade Federal de São Paulo, Dept Obstet, São Paulo, BrazilBrazilian Cochrane Ctr, São Paulo, BrazilWorld Hlth Org, Dept Reprod Hlth & Res, Geneva, SwitzerlandWorld Hlth Org, Dept Knowledge Management & Sharing, Geneva, SwitzerlandUniversidade Federal de São Paulo, Dept Obstet, São Paulo, BrazilWeb of Scienc

PatternLab for proteomics: a tool for differential shotgun proteomics

<p>Abstract</p> <p>Background</p> <p>A goal of proteomics is to distinguish between states of a biological system by identifying protein expression differences. Liu <it>et al</it>. demonstrated a method to perform semi-relative protein quantitation in shotgun proteomics data by correlating the number of tandem mass spectra obtained for each protein, or "spectral count", with its abundance in a mixture; however, two issues have remained open: how to normalize spectral counting data and how to efficiently pinpoint differences between profiles. Moreover, Chen <it>et al</it>. recently showed how to increase the number of identified proteins in shotgun proteomics by analyzing samples with different MS-compatible detergents while performing proteolytic digestion. The latter introduced new challenges as seen from the data analysis perspective, since replicate readings are not acquired.</p> <p>Results</p> <p>To address the open issues above, we present a program termed PatternLab for proteomics. This program implements existing strategies and adds two new methods to pinpoint differences in protein profiles. The first method, ACFold, addresses experiments with less than three replicates from each state or having assays acquired by different protocols as described by Chen <it>et al</it>. ACFold uses a combined criterion based on expression fold changes, the AC test, and the false-discovery rate, and can supply a "bird's-eye view" of differentially expressed proteins. The other method addresses experimental designs having multiple readings from each state and is referred to as nSVM (natural support vector machine) because of its roots in evolutionary computing and in statistical learning theory. Our observations suggest that nSVM's niche comprises projects that select a minimum set of proteins for classification purposes; for example, the development of an early detection kit for a given pathology. We demonstrate the effectiveness of each method on experimental data and confront them with existing strategies.</p> <p>Conclusion</p> <p>PatternLab offers an easy and unified access to a variety of feature selection and normalization strategies, each having its own niche. Additionally, graphing tools are available to aid in the analysis of high throughput experimental data. PatternLab is available at <url>http://pcarvalho.com/patternlab</url>.</p

In vitro phosphorylation as tool for modification of silk and keratin fibrous materials

An overview is given of the recent work on in vitro enzymatic phosphorylation of silk fibroin and human hair keratin. Opposing to many chemical "conventional" approaches, enzymatic phosphorylation is in fact a mild reaction and the treatment falls within "green chemistry" approach. Silk and keratin are not phosphorylated in vivo, but in vitro. This enzyme-driven modification is a major technological breakthrough. Harsh chemical chemicals are avoided, and mild conditions make enzymatic phosphorylation a real "green chemistry" approach. The current communication presents a novel approach stating that enzyme phosphorylation may be used as a tool to modify the surface charge of biocompatible materials such as keratin and silk

Mixed model approaches for the identification of QTLs within a maize hybrid breeding program

Two outlines for mixed model based approaches to quantitative trait locus (QTL) mapping in existing maize hybrid selection programs are presented: a restricted maximum likelihood (REML) and a Bayesian Markov Chain Monte Carlo (MCMC) approach. The methods use the in-silico-mapping procedure developed by Parisseaux and Bernardo (2004) as a starting point. The original single-point approach is extended to a multi-point approach that facilitates interval mapping procedures. For computational and conceptual reasons, we partition the full set of relationships from founders to parents of hybrids into two types of relations by defining so-called intermediate founders. QTL effects are defined in terms of those intermediate founders. Marker based identity by descent relationships between intermediate founders define structuring matrices for the QTL effects that change along the genome. The dimension of the vector of QTL effects is reduced by the fact that there are fewer intermediate founders than parents. Furthermore, additional reduction in the number of QTL effects follows from the identification of founder groups by various algorithms. As a result, we obtain a powerful mixed model based statistical framework to identify QTLs in genetic backgrounds relevant to the elite germplasm of a commercial breeding program. The identification of such QTLs will provide the foundation for effective marker assisted and genome wide selection strategies. Analyses of an example data set show that QTLs are primarily identified in different heterotic groups and point to complementation of additive QTL effects as an important factor in hybrid performance

Euphol, a tetracyclic triterpene, from Euphorbia tirucalli induces autophagy and sensitizes temozolomide cytotoxicity on glioblastoma cells

Glioblastoma (GBM) is the most frequent and aggressive type of brain tumor. There are limited therapeutic options for GBM so that new and effective agents are urgently needed. Euphol is a tetracyclic triterpene alcohol, and it is the main constituent of the sap of the medicinal plant Euphorbia tirucalli. We previously identified anti-cancer activity in euphol based on the cytotoxicity screening of 73 human cancer cells. We now expand the toxicological screening of the inhibitory effect and bioactivity of euphol using two additional glioma primary cultures. Euphol exposure showed similar cytotoxicity against primary glioma cultures compared to commercial glioma cells. Euphol has concentration-dependent cytotoxic effects on cancer cell lines, with more than a five-fold difference in the IC50 values in some cell lines. Euphol treatment had a higher selective cytotoxicity index (0.64-3.36) than temozolomide (0.11-1.13) and reduced both proliferation and cell motility. However, no effect was found on cell cycle distribution, invasion and colony formation. Importantly, the expression of the autophagy-associated protein LC3-II and acidic vesicular organelle formation were markedly increased, with Bafilomycin A1 potentiating cytotoxicity. Finally, euphol also exhibited antitumoral and antiangiogenic activity in vivo, using the chicken chorioallantoic membrane assay, with synergistic temozolomide interactions in most cell lines. In conclusion, euphol exerted in vitro and in vivo cytotoxicity against glioma cells, through several cancer pathways, including the activation of autophagy-associated cell death. These findings provide experimental support for further development of euphol as a novel therapeutic agent for GBM, either alone or in combination chemotherapy.The work was supported by the Amazonia Fitomedicamentos (FITO05/2012) Ltda. and Barretos Cancer Hospital, all from Brazil

A Novel Semi-Supervised Methodology for Extracting Tumor Type-Specific MRS Sources in Human Brain Data

BackgroundThe clinical investigation of human brain tumors often starts with a non-invasive imaging study, providing information about the tumor extent and location, but little insight into the biochemistry of the analyzed tissue. Magnetic Resonance Spectroscopy can complement imaging by supplying a metabolic fingerprint of the tissue. This study analyzes single-voxel magnetic resonance spectra, which represent signal information in the frequency domain. Given that a single voxel may contain a heterogeneous mix of tissues, signal source identification is a relevant challenge for the problem of tumor type classification from the spectroscopic signal.Methodology/Principal FindingsNon-negative matrix factorization techniques have recently shown their potential for the identification of meaningful sources from brain tissue spectroscopy data. In this study, we use a convex variant of these methods that is capable of handling negatively-valued data and generating sources that can be interpreted as tumor class prototypes. A novel approach to convex non-negative matrix factorization is proposed, in which prior knowledge about class information is utilized in model optimization. Class-specific information is integrated into this semi-supervised process by setting the metric of a latent variable space where the matrix factorization is carried out. The reported experimental study comprises 196 cases from different tumor types drawn from two international, multi-center databases. The results indicate that the proposed approach outperforms a purely unsupervised process by achieving near perfect correlation of the extracted sources with the mean spectra of the tumor types. It also improves tissue type classification.Conclusions/SignificanceWe show that source extraction by unsupervised matrix factorization benefits from the integration of the available class information, so operating in a semi-supervised learning manner, for discriminative source identification and brain tumor labeling from single-voxel spectroscopy data. We are confident that the proposed methodology has wider applicability for biomedical signal processing

Oral intake of Lactobacillus pentosus strain b240 accelerates salivary immunoglobulin A secretion in the elderly: A randomized, placebo-controlled, double-blind trial

<p>Abstract</p> <p>Background</p> <p>Immunoglobulin A (IgA) secretion in saliva decreases with age and may be the cause of increased vulnerability of the elderly to respiratory infections. The effect of oral intake of lactic acid bacteria on salivary secretory IgA (SIgA) in the elderly has not been reported. The objective of this study was to demonstrate the acceleration of salivary SIgA secretion by oral intake of <it>Lactobacillus pentosus </it>strain b240 (b240) in the elderly.</p> <p>Results</p> <p>A total of 80 healthy elderly individuals were randomly allocated to either an intervention (i.e., b240) or a control (i.e., placebo) group. The elderly individuals in the b240 group were given a sterile water beverage (125 mL) containing heat-killed b240 (4 × 10⁹cells), while those in the placebo group were given only a sterile water beverage (125 mL); both groups received their respective beverages once daily for 12 weeks. Saliva was collected before initiation of the study and every 2 weeks thereafter. Saliva flow rate and SIgA concentration were determined, and the SIgA secretion rate was calculated. The mean salivary SIgA secretion rate in the b240 group steadily increased until week 4 (exhibiting a 20% elevation relative to that at week 0), and then remained stable until week 12. Changes in SIgA secretion rate over the intervention period were significantly greater in the b240 group than in the placebo group. The treatment groups exhibited no significant differences in adverse events.</p> <p>Conclusions</p> <p>Oral intake of <it>L. pentosus </it>strain b240 for 12 weeks significantly accelerated salivary SIgA secretion, thereby indicating its potential utility in the improvement of mucosal immunity and resistance against infection in the elderly.</p