Chitosan Nanoparticles Act as an Adjuvant to Promote both Th1 and Th2 Immune Responses Induced by Ovalbumin in Mice

The study was conducted to investigate the promoted immune response to ovalbumin in mice by chitosan nanoparticles (CNP) and its toxicity. CNP did not cause any mortality or side effects when mice were administered subcutaneously twice with a dose of 1.5 mg at 7-day intervals. Institute of Cancer Research (ICR) mice were immunized subcutaneously with 25 μg ovalbumin (OVA) alone or with 25 μg OVA dissolved in saline containing Quil A (10 μg), chitosan (CS) (50 μg) or CNP (12.5, 50 or 200 μg) on days 1 and 15. Two weeks after the secondary immunization, serum OVA-specific antibody titers, splenocyte proliferation, natural killer (NK) cell activity, and production and mRNA expression of cytokines from splenocytes were measured. The serum OVA-specific IgG, IgG1, IgG2a, and IgG2b antibody titers and Con A-, LPS-, and OVA-induced splenocyte proliferation were significantly enhanced by CNP (P < 0.05) as compared with OVA and CS groups. CNP also significantly promoted the production of Th1 (IL-2 and IFN-γ) and Th2 (IL-10) cytokines and up-regulated the mRNA expression of IL-2, IFN-γ and IL-10 cytokines in splenocytes from the immunized mice compared with OVA and CS groups. Besides, CNP remarkably increased the killing activities of NK cells activity (P < 0.05). The results suggested that CNP had a strong potential to increase both cellular and humoral immune responses and elicited a balanced Th1/Th2 response, and that CNP may be a safe and efficacious adjuvant candidate suitable for a wide spectrum of prophylactic and therapeutic vaccines

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

"I'm healthy, I don't have pain"-health screening participation and its association with chronic pain in a low socioeconomic status Singaporean population

10.3344/kjp.2017.30.1.34KOREAN JOURNAL OF PAIN30134-4

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press