Do age, gender, BMI and disease duration influence the clinical outcomes in patients of knee osteoarthritis treated with serial injections of autologous platelet rich plasma?

Purpose: To study whether age, gender, body mass index(BMI) and disease duration influence the clinical outcomes in kellgren-Lawrence(K-L) grade II,III knee osteoarthritis(KOA) patients treated with serial injections of platelet rich plasma(PRP). Patients and methods: 65 patients were given three monthly intra-articular injections of PRP in this prospective interventional study. The patients were divided into subgroups depending on the factor studied: by age(in years) into young <45(n = 7), middle age 45-60(n = 35), and elderly >60(n = 23): by BMI(in kg/m2) into; normal <25(n = 25), overweight 25-30(n = 27) and obese >30(n = 13) and disease duration; less(n = 32) or more than 1 year(n = 33) symptom duration. Visual analogue scale (VAS) and Western Ontario and McMaster Universities Arthritis Index (WOMAC) were used as outcome measures and assessed before each injection and then at 6 and 9 months post injection. Groups were homogenous with respect to baseline characteristics. Results: Mean VAS and WOMAC scores showed a statistically significant improvement (P < 0.0001) across all groups and subgroups (age,gender,BMI,disease duration) at follow up. On intra-subgroup comparison, we found no significant differences(P > 0.05) among age, BMI or gender subgroups, however the scores were significantly better in patients with disease duration of less than 1 year than those with more than 1 year duration at both 6 and 9 months[P < 0.001(RC = 9.630,95% CI = 4.037-15.222,P = 0.001)]. Conclusion: PRP injections if given serially can improve the short term subjective scores of VAS and WOMAC scores in patients with K-L grade II and III KOA irrespective of age, gender, BMI or disease duration, however, clinical benefits can be maximized if given early in the disease course.

GFI1/HDAC1-axis differentially regulates immunosuppressive CD73 in human tumor-associated FOXP3+ Th17 and inflammation-linked Th17 cells.

Plasticity between Th17 and Treg cells is regarded as a crucial determinant of tumor-associated immunosuppression. Classically Th17 cells mediate inflammatory responses through production of cytokine IL17. Recently, Th17 cells have also been shown to acquire suppressive phenotypes in tumor microenvironment. However, the mechanism by which they acquire such immunosuppressive properties is still elusive. Here, we report that in tumor microenvironment Th17 cell acquires immunosuppressive properties by expressing Treg lineage-specific transcription factor FOXP3 and ectonucleotidase CD73. We designate this cell as Th17reg cell and perceive that such immunosuppressive property is dependent on CD73. It was observed that in classical Th17 cell, GFI1 recruits HDAC1 to change the euchromatin into tightly-packed heterochromatin at the proximal-promoter region of CD73 to repress its expression. Whereas in Th17reg cells GFI1 cannot get access to CD73-promoter due to heterochromatin state at its binding site and, thus, cannot recruit HDAC1, failing to suppress the expression of CD73.

A Deep Phenotype Association Study Reveals Specific Phenotype Associations with Genetic Variants in Age-related Macular Degeneration: Age-Related Eye Disease Study 2 (AREDS2) Report No. 14.

PURPOSE: Age-related macular degeneration (AMD), a multifactorial disease with variable phenotypic presentation, was associated with 52 single nucleotide polymorphisms (SNPs) at 34 loci in a genome-wide association study (GWAS). These genetic variants could modulate different biological pathways involved in AMD, contributing to phenotypic variability. To better understand the effects of these SNPs, we performed a deep phenotype association study (DeePAS) in the Age-Related Eye Disease Study 2 (AREDS2), followed by replication using AREDS participants, to identify genotype associations with AMD and non-AMD ocular and systemic phenotypes. DESIGN: Cohort study. PARTICIPANTS: AREDS and AREDS2 participants. METHODS: AREDS2 participants (discovery cohort) had detailed phenotyping for AMD; other eye conditions; cardiovascular, neurologic, gastrointestinal, and endocrine disease; cognitive function; serum nutrient levels; and others (total of 139 AMD and non-AMD phenotypes). Genotypes of the 52 GWAS SNPs were obtained. The DeePAS was performed by correlating the 52 SNPs to all phenotypes using logistic and linear regression models. Associations that reached Bonferroni-corrected statistical significance were replicated in AREDS. MAIN OUTCOME MEASURES: Genotype-phenotype associations. RESULTS: A total of 1776 AREDS2 participants had 5 years follow-up; 1435 AREDS participants had 10 years. The DeePAS revealed a significant association of the rs3750846 SNP at the ARMS2/HTRA1 locus with subretinal/sub-retinal pigment epithelial (RPE) hemorrhage related to neovascular AMD (odds ratio 1.55 [95% confidence interval 1.31-1.84], P = 2.67 × 10-7). This novel association remained significant after conditioning on participants with neovascular AMD (P = 2.42 × 10-4). Carriers of rs3750846 had poorer visual acuity during follow-up (P = 6.82 × 10-7) and were more likely to have a first-degree relative with AMD (P = 5.38 × 10-6). Two SNPs at the CFH locus, rs10922109 and rs570618, were associated with the drusen area in the Early Treatment Diabetic Retinopathy Study Report (ETDRS) grid (P = 2.29 × 10-11 and P = 3.20 × 10-9, respectively) and the center subfield (P = 1.24 × 10-9 and P = 6.68 × 10-8, respectively). SNP rs570618 was additionally associated with the presence of calcified drusen (P = 5.38 × 10-6). Except for positive family history of AMD with rs3750846, all genotype-phenotype associations were significantly replicated in AREDS. No pleiotropic associations were identified. CONCLUSIONS: The association of the SNP at the ARMS2/HTRA1 locus with subretinal/sub-RPE hemorrhage and poorer visual acuity and of SNPs at the CFH locus with drusen area may provide new insights in pathophysiological pathways underlying different stages of AMD.

Pressing needs of biomedical text mining in biocuration and beyond: opportunities and challenges.

Text mining in the biomedical sciences is rapidly transitioning from small-scale evaluation to large-scale application. In this article, we argue that text-mining technologies have become essential tools in real-world biomedical research. We describe four large scale applications of text mining, as showcased during a recent panel discussion at the BioCreative V Challenge Workshop. We draw on these applications as case studies to characterize common requirements for successfully applying text-mining techniques to practical biocuration needs. We note that system 'accuracy' remains a challenge and identify several additional common difficulties and potential research directions including (i) the 'scalability' issue due to the increasing need of mining information from millions of full-text articles, (ii) the 'interoperability' issue of integrating various text-mining systems into existing curation workflows and (iii) the 'reusability' issue on the difficulty of applying trained systems to text genres that are not seen previously during development. We then describe related efforts within the text-mining community, with a special focus on the BioCreative series of challenge workshops. We believe that focusing on the near-term challenges identified in this work will amplify the opportunities afforded by the continued adoption of text-mining tools. Finally, in order to sustain the curation ecosystem and have text-mining systems adopted for practical benefits, we call for increased collaboration between text-mining researchers and various stakeholders, including researchers, publishers and biocurators.

Text Mining Genotype-Phenotype Relationships from Biomedical Literature for Database Curation and Precision Medicine.

The practice of precision medicine will ultimately require databases of genes and mutations for healthcare providers to reference in order to understand the clinical implications of each patient's genetic makeup. Although the highest quality databases require manual curation, text mining tools can facilitate the curation process, increasing accuracy, coverage, and productivity. However, to date there are no available text mining tools that offer high-accuracy performance for extracting such triplets from biomedical literature. In this paper we propose a high-performance machine learning approach to automate the extraction of disease-gene-variant triplets from biomedical literature. Our approach is unique because we identify the genes and protein products associated with each mutation from not just the local text content, but from a global context as well (from the Internet and from all literature in PubMed). Our approach also incorporates protein sequence validation and disease association using a novel text-mining-based machine learning approach. We extract disease-gene-variant triplets from all abstracts in PubMed related to a set of ten important diseases (breast cancer, prostate cancer, pancreatic cancer, lung cancer, acute myeloid leukemia, Alzheimer's disease, hemochromatosis, age-related macular degeneration (AMD), diabetes mellitus, and cystic fibrosis). We then evaluate our approach in two ways: (1) a direct comparison with the state of the art using benchmark datasets; (2) a validation study comparing the results of our approach with entries in a popular human-curated database (UniProt) for each of the previously mentioned diseases. In the benchmark comparison, our full approach achieves a 28% improvement in F1-measure (from 0.62 to 0.79) over the state-of-the-art results. For the validation study with UniProt Knowledgebase (KB), we present a thorough analysis of the results and errors. Across all diseases, our approach returned 272 triplets (disease-gene-variant) that overlapped with entries in UniProt and 5,384 triplets without overlap in UniProt. Analysis of the overlapping triplets and of a stratified sample of the non-overlapping triplets revealed accuracies of 93% and 80% for the respective categories (cumulative accuracy, 77%). We conclude that our process represents an important and broadly applicable improvement to the state of the art for curation of disease-gene-variant relationships.

Text Mining for Precision Medicine: Bringing Structure to EHRs and Biomedical Literature to Understand Genes and Health.

The key question of precision medicine is whether it is possible to find clinically actionable granularity in diagnosing disease and classifying patient risk. The advent of next-generation sequencing and the widespread adoption of electronic health records (EHRs) have provided clinicians and researchers a wealth of data and made possible the precise characterization of individual patient genotypes and phenotypes. Unstructured text-found in biomedical publications and clinical notes-is an important component of genotype and phenotype knowledge. Publications in the biomedical literature provide essential information for interpreting genetic data. Likewise, clinical notes contain the richest source of phenotype information in EHRs. Text mining can render these texts computationally accessible and support information extraction and hypothesis generation. This chapter reviews the mechanics of text mining in precision medicine and discusses several specific use cases, including database curation for personalized cancer medicine, patient outcome prediction from EHR-derived cohorts, and pharmacogenomic research. Taken as a whole, these use cases demonstrate how text mining enables effective utilization of existing knowledge sources and thus promotes increased value for patients and healthcare systems. Text mining is an indispensable tool for translating genotype-phenotype data into effective clinical care that will undoubtedly play an important role in the eventual realization of precision medicine.

Text mining for precision medicine: automating disease-mutation relationship extraction from biomedical literature.

OBJECTIVE: Identifying disease-mutation relationships is a significant challenge in the advancement of precision medicine. The aim of this work is to design a tool that automates the extraction of disease-related mutations from biomedical text to advance database curation for the support of precision medicine. MATERIALS AND METHODS: We developed a machine-learning (ML) based method to automatically identify the mutations mentioned in the biomedical literature related to a particular disease. In order to predict a relationship between the mutation and the target disease, several features, such as statistical features, distance features, and sentiment features, were constructed. Our ML model was trained with a pre-labeled dataset consisting of manually curated information about mutation-disease associations. The model was subsequently used to extract disease-related mutations from larger biomedical literature corpora. RESULTS: The performance of the proposed approach was assessed using a benchmarking dataset. Results show that our proposed approach gains significant improvement over the previous state of the art and obtains F-measures of 0.880 and 0.845 for prostate and breast cancer mutations, respectively. DISCUSSION: To demonstrate its utility, we applied our approach to all abstracts in PubMed for 3 diseases (including a non-cancer disease). The mutations extracted were then manually validated against human-curated databases. The validation results show that the proposed approach is useful in a real-world setting to extract uncurated disease mutations from the biomedical literature. CONCLUSIONS: The proposed approach improves the state of the art for mutation-disease extraction from text. It is scalable and generalizable to identify mutations for any disease at a PubMed scale.