Effectiveness of the bucco-lingual technique within a school-based supervised toothbrushing program on preventing caries: a randomized controlled trial

Abstract Background Supervised toothbrushing programs using fluoride dentifrice have reduced caries increment. However there is no information about the effectiveness of the professional cross-brushing technique within a community intervention. The aim was to assess if the bucco-lingual technique can increase the effectiveness of a school-based supervised toothbrushing program on preventing caries. Methods A randomized double-blinded controlled community intervention trial to be analyzed at an individual level was conducted in a Brazilian low-income fluoridated area. Six preschools were randomly assigned to the test and control groups and 284 five-year-old children presenting at least one permanent molar with emerged/sound occlusal surface participated. In control group, oral health education and dental plaque dying followed by toothbrushing with fluoride dentifrice supervised directly by a dental assistant, was developed four times per year. At the remaining school days the children brushed their teeth under indirect supervising of the teachers. In test group, children also underwent a professional cross-brushing on surfaces of first permanent molar rendered by a specially trained dental assistant five times per year. Enamel and dentin caries were recorded on buccal, occlusal and lingual surfaces of permanent molars during 18-month follow-up. Exposure time of surfaces was calculated and incidence density ratio was estimated using Poisson regression model. Results Difference of 21.6 lesions per 1,000 children between control and test groups was observed. Among boys whose caries risk was higher compared to girls, incidence density was 50% lower in test group (p = 0.016). Conclusion Modified program was effective among the boys. It is licit to project a relevant effect in a larger period suggesting in a broader population substantial reduction of dental care needs. Trial registration ISRCTN18548869

Using social media to support small group learning

Abstract Background Medical curricula are increasingly using small group learning and less didactic lecture-based teaching. This creates new challenges and opportunities in how students are best supported with information technology. We explored how university-supported and external social media could support collaborative small group working on our new undergraduate medical curriculum. Methods We made available a curation platform (Scoop.it) and a wiki within our virtual learning environment as part of year 1 Case-Based Learning, and did not discourage the use of other tools such as Facebook. We undertook student surveys to capture perceptions of the tools and information on how they were used, and employed software user metrics to explore the extent to which they were used during the year. Results Student groups developed a preferred way of working early in the course. Most groups used Facebook to facilitate communication within the group, and to host documents and notes. There were more barriers to using the wiki and curation platform, although some groups did make extensive use of them. Staff engagement was variable, with some tutors reviewing the content posted on the wiki and curation platform in face-to-face sessions, but not outside these times. A small number of staff posted resources and reviewed student posts on the curation platform. Conclusions Optimum use of these tools depends on sufficient training of both staff and students, and an opportunity to practice using them, with ongoing support. The platforms can all support collaborative learning, and may help develop digital literacy, critical appraisal skills, and awareness of wider health issues in society

Evaluating Phospholipid‐Functionalized Gold Nanorods for In Vivo Applications

Gold nanorods (AuNRs) have attracted a great deal of attention due to their potential for use in a wide range of biomedical applications. However, their production typically requires the use of the relatively toxic cationic surfactant cetyltrimethylammonium bromide (CTAB) leading to continued demand for protocols to detoxify them for in vivo applications. In this study, a robust and facile protocol for the displacement of CTAB from the surface of AuNRs using phospholipids is presented. After the displacement, CTAB is not detectable by NMR spectroscopy, surface‐enhanced Raman spectroscopy, or using pH‐dependent ζ‐potential measurements. The phospholipid functionalized AuNRs demonstrated superior stability and biocompatibility (IC50 > 200 µg mL−1) compared to both CTAB and polyelectrolyte functionalized AuNRs and are well tolerated in vivo. Furthermore, they have high near‐infrared (NIR) absorbance and produce large amounts of heat under NIR illumination, hence such particles are well suited for plasmonic medical applications

Freeze-Dried Therapeutic Microbubbles: Stability and Gas Exchange

Microbubbles (MBs) are widely used as contrast enhancement agents for ultrasound imaging and have the potential to enhance therapeutic delivery to diseases such as cancer. Yet, they are only stable in solution for a few hours to days after production, which limits their potential application. Freeze-drying provides long-term storage, ease of transport, and consistency in structure and composition, thereby facilitating their use in clinical settings. Therapeutic microbubbles (thMBs) consisting of MBs with attached therapeutic payload potentially face even greater issues for production, stability, and well-defined drug delivery. The ability to freeze-dry thMBs represents an important step for their translation to the clinic. Here, we show that it is possible to freeze-dry and reconstitute thMBs that consist of lipid-coated MBs with an attached liposomal payload. The thMBs were produced microfluidically, and the liposomes contained either calcein, as a model drug, or gemcitabine. The results show that drug-loaded thMBs can be freeze-dried and stored for at least 6 months. Upon reconstitution, they maintain their structural integrity and drug loading. Furthermore, we show that their in vivo echogenicity is maintained post-freeze-drying. Depending on the gas used in the original bubbles, we also demonstrate that the approach provides a method to exchange the gas core to allow the formulation of thMBs with different gases for combination therapies or improved drug efficacy. Importantly, this work provides an important route for the facile off-site production of thMBs that can be reformulated at the point of care

Development of Second-Generation VEGFR Tyrosine Kinase Inhibitors: Current Status

The vascular endothelial growth factor (VEGF) signaling pathway appears to be the dominant pathway involved in tumor angiogenesis, providing a rationale for targeting the VEGF receptors (VEGFR-1, -2, and -3) in the treatment of cancers. In particular, VEGF signaling is thought to be important in renal cell carcinoma (RCC) because of the deregulation of the pathway through nearly uniform loss of the von Hippel Lindau protein. The tyrosine kinase inhibitors (TKIs) sorafenib, sunitinib, and pazopanib are approved by the US Food and Drug Administration for the treatment of advanced RCC; however, these multitargeted agents inhibit a wide range of kinase targets in addition to the VEGFRs, resulting in a range of adverse effects unrelated to efficient VEGF blockade. This article reviews recent advances in the development of the second-generation VEGFR TKIs, including the more selective VEGFR TKIs tivozanib and axitinib, and focuses on the potential benefits of novel inhibitors with improved potency and selectivity

Assessment of the quality and variability of health information on chronic pain websites using the DISCERN instrument

<p>Abstract</p> <p>Background</p> <p>The Internet is used increasingly by providers as a tool for disseminating pain-related health information and by patients as a resource about health conditions and treatment options. However, health information on the Internet remains unregulated and varies in quality, accuracy and readability. The objective of this study was to determine the quality of pain websites, and explain variability in quality and readability between pain websites.</p> <p>Methods</p> <p>Five key terms (pain, chronic pain, back pain, arthritis, and fibromyalgia) were entered into the Google, Yahoo and MSN search engines. Websites were assessed using the DISCERN instrument as a quality index. Grade level readability ratings were assessed using the Flesch-Kincaid Readability Algorithm. Univariate (using alpha = 0.20) and multivariable regression (using alpha = 0.05) analyses were used to explain the variability in DISCERN scores and grade level readability using potential for commercial gain, health related seals of approval, language(s) and multimedia features as independent variables.</p> <p>Results</p> <p>A total of 300 websites were assessed, 21 excluded in accordance with the exclusion criteria and 110 duplicate websites, leaving 161 unique sites. About 6.8% (11/161 websites) of the websites offered patients' commercial products for their pain condition, 36.0% (58/161 websites) had a health related seal of approval, 75.8% (122/161 websites) presented information in English only and 40.4% (65/161 websites) offered an interactive multimedia experience. In assessing the quality of the unique websites, of a maximum score of 80, the overall average DISCERN Score was 55.9 (13.6) and readability (grade level) of 10.9 (3.9). The multivariable regressions demonstrated that website seals of approval (<it>P </it>= 0.015) and potential for commercial gain (<it>P </it>= 0.189) were contributing factors to higher DISCERN scores, while seals of approval (<it>P </it>= 0.168) and interactive multimedia (<it>P </it>= 0.244) contributed to lower grade level readability, as indicated by estimates of the beta coefficients.</p> <p>Conclusion</p> <p>The overall quality of pain websites is moderate, with some shortcomings. Websites that scored high using the DISCERN questionnaire contained health related seals of approval and provided commercial solutions for pain related conditions while those with low readability levels offered interactive multimedia options and have been endorsed by health seals.</p

Rule-Based Forecasting: Using Judgment in Time-Series Extrapolation

Rule-Based Forecasting (RBF) is an expert system that uses judgment to develop and apply rules for combining extrapolations. The judgment comes from two sources, forecasting expertise and domain knowledge. Forecasting expertise is based on more than a half century of research. Domain knowledge is obtained in a structured way; one example of domain knowledge is managers= expectations about trends, which we call “causal forces.” Time series are described in terms of 28 conditions, which are used to assign weights to extrapolations. Empirical results on multiple sets of time series show that RBF produces more accurate forecasts than those from traditional extrapolation methods or equal-weights combined extrapolations. RBF is most useful when it is based on good domain knowledge, the domain knowledge is important, the series is well behaved (such that patterns can be identified), there is a strong trend in the data, and the forecast horizon is long. Under ideal conditions, the error for RBF’s forecasts were one-third less than those for equal-weights combining. When these conditions are absent, RBF neither improves nor harms forecast accuracy. Some of RBF’s rules can be used with traditional extrapolation procedures. In a series of studies, rules based on causal forces improved the selection of forecasting methods, the structuring of time series, and the assessment of prediction intervals

Radiation Impairs Perineural Invasion by Modulating the Nerve Microenvironment

Perineural invasion (PNI) by cancer cells is an ominous clinical event that is associated with increased local recurrence and poor prognosis. Although radiation therapy (RT) may be delivered along the course of an invaded nerve, the mechanisms through which radiation may potentially control PNI remain undefined. murine sciatic nerve model was used to study how RT to nerve or cancer affects nerve invasion by cancer.Cancer cell invasion of the DRG was partially dependent on DRG secretion of glial-derived neurotrophic factor (GDNF). A single 4 Gy dose of radiation to the DRG alone, cultured with non-radiated cancer cells, significantly inhibited PNI and was associated with decreased GDNF secretion but intact DRG viability. Radiation of cancer cells alone, co-cultured with non-radiated nerves, inhibited PNI through predominantly compromised cancer cell viability. In a murine model of PNI, a single 8 Gy dose of radiation to the sciatic nerve prior to implantation of non-radiated cancer cells resulted in decreased GDNF expression, decreased PNI by imaging and histology, and preservation of sciatic nerve motor function.Radiation may impair PNI through not only direct effects on cancer cell viability, but also an independent interruption of paracrine mechanisms underlying PNI. RT modulation of the nerve microenvironment may decrease PNI, and hold significant therapeutic implications for RT dosing and field design for patients with cancers exhibiting PNI

Structure-based classification and ontology in chemistry

<p>Abstract</p> <p>Background</p> <p>Recent years have seen an explosion in the availability of data in the chemistry domain. With this information explosion, however, retrieving <it>relevant </it>results from the available information, and <it>organising </it>those results, become even harder problems. Computational processing is essential to filter and organise the available resources so as to better facilitate the work of scientists. Ontologies encode expert domain knowledge in a hierarchically organised machine-processable format. One such ontology for the chemical domain is ChEBI. ChEBI provides a classification of chemicals based on their structural features and a role or activity-based classification. An example of a structure-based class is 'pentacyclic compound' (compounds containing five-ring structures), while an example of a role-based class is 'analgesic', since many different chemicals can act as analgesics without sharing structural features. Structure-based classification in chemistry exploits elegant regularities and symmetries in the underlying chemical domain. As yet, there has been neither a systematic analysis of the types of structural classification in use in chemistry nor a comparison to the capabilities of available technologies.</p> <p>Results</p> <p>We analyze the different categories of structural classes in chemistry, presenting a list of patterns for features found in class definitions. We compare these patterns of class definition to tools which allow for automation of hierarchy construction within cheminformatics and within logic-based ontology technology, going into detail in the latter case with respect to the expressive capabilities of the Web Ontology Language and recent extensions for modelling structured objects. Finally we discuss the relationships and interactions between cheminformatics approaches and logic-based approaches.</p> <p>Conclusion</p> <p>Systems that perform intelligent reasoning tasks on chemistry data require a diverse set of underlying computational utilities including algorithmic, statistical and logic-based tools. For the task of automatic structure-based classification of chemical entities, essential to managing the vast swathes of chemical data being brought online, systems which are capable of hybrid reasoning combining several different approaches are crucial. We provide a thorough review of the available tools and methodologies, and identify areas of open research.</p