Milk derived colloid as a novel drug delivery carrier for breast cancer

Triple negative breast cancer has an extremely poor prognosis when chemotherapy is no longer effective. To overcome drug resistance, novel drug delivery systems based on nanoparticles have had remarkable success. We produced a novel nanoparticle component ‘MDC’ from milk-derived colloid. In order to evaluate the anti-cancer effect of MDC, we conducted in vitro and in vivo experiments on cancer cell lines and a primary tumor derived breast xenograft. Doxorubicin (Dox) conjugated to MDC (MDC-Dox) showed higher cancer cell growth inhibition than MDC alone especially in cell lines with high EGFR expression. In a mouse melanoma model, MDC-Dox significantly suppressed tumor growth when compared with free Dox. Moreover, in a primary tumor derived breast xenograft, one of the mice treated with MDC-Dox showed partial regression, while mice treated with free Dox failed to show any suppression of tumor growth. We have shown that a novel nanoparticle compound made of simple milk-derived colloid has the capability for drug conjugation, and serves as a tumor-specific carrier of anti-cancer drugs. Further research on its safety and ability to carry various anti-cancer drugs into multiple drug-resistant primary breast models is warranted.     

In vitro activity of EDTA and TOL-463 against Neisseria gonorrhoeae

Neisseria gonorrhoeae quickly develops drug resistance. Time-kill curves revealed that EDTA and TOL-463 inhibit growth similar to penicillin, ciprofloxacin, and azithromycin. Furthermore, synergistic and additive antimicrobial interactions occurred when EDTA and TOL-463 were combined with penicillin or azithromycin, respectively, suggesting that further investigations into these unconventional antimicrobials may be advantageous.     

Recognition of medication information from discharge summaries using ensembles of classifiers

ABSTRACT: BACKGROUND: Extraction of clinical information such as medications or problems from clinical text is an important task of clinical natural language processing (NLP). Rule-based methods are often used in clinical NLP systems because they are easy to adapt and customize. Recently, supervised machine learning methods have proven to be effective in clinical NLP as well. However, combining different classifiers to further improve the performance of clinical entity recognition systems has not been investigated extensively. Combining classifiers into an ensemble classifier presents both challenges and opportunities to improve performance in such NLP tasks. METHODS: We investigated ensemble classifiers that used different voting strategies to combine outputs from three individual classifiers: a rule-based system, a support vector machine (SVM) based system, and a conditional random field (CRF) based system. Three voting methods were proposed and evaluated using the annotated data sets from the 2009 i2b2 NLP challenge: simple majority, local SVM-based voting, and local CRF-based voting. RESULTS: Evaluation on 268 manually annotated discharge summaries from the i2b2 challenge showed that the local CRF-based voting method achieved the best F-score of 90.84% (94.11% Precision, 87.81% Recall) for 10-fold cross-validation. We then compared our systems with the first-ranked system in the challenge by using the same training and test sets. Our system based on majority voting achieved a better F-score of 89.65% (93.91% Precision, 85.76% Recall) than the previously reported F-score of 89.19% (93.78% Precision, 85.03% Recall) by the first-ranked system in the challenge. CONCLUSIONS: Our experimental results using the 2009 i2b2 challenge datasets showed that ensemble classifiers that combine individual classifiers into a voting system could achieve better performance than a single classifier in recognizing medication information from clinical text. It suggests that simple strategies that can be easily implemented such as majority voting could have the potential to significantly improve clinical entity recognition.     

Marche allergique chez l’enfant,de la rhinite à l’asthme : prise en charge,place de la désensibilisation

Allergic rhinitis (AR) is a common IgE dependent disorder. AR is maybe one of the steps of the allergic march, which starts with atopic dermatitis and food allergy and includes atopic asthma. AR and asthma are frequently associated. AR is frequently under-diagnosed and undertreated although it affects quality of life and school performance. Management of AR depends on its severity and will associate environmental control (best guided by environmental investigation and skin testing of specific IgE antibodies), pharmacotherapy (with antihistamines and intranasal corticosteroids as first line drugs). At present allergen immunotherapy is considered in patients with severe AR, insufficiently controlled by pharmacotherapy and who demonstrate specific IgE antibodies to relevant allergens. Sublingual immunotherapy is well tolerated. Only immunotherapy with the right allergens has the potential to alter the natural history of the allergic march, by preventing the development of new allergen sensitizations and reducing the risk for the subsequent development of asthma. This fact might extend the indications of specific allergen immunotherapy. Patients (and parents) education is of utmost importance in the management of allergic disorders.