Haplotype association analysis of North American Rheumatoid Arthritis Consortium data using a generalized linear model with regularization

The Genetic Analysis Workshop 16 rheumatoid arthritis data include a set of 868 cases and 1194 controls genotyped at 545,080 single-nucleotide polymorphisms (SNPs) from the Illumina 550 k chip. We focus on investigating chromosomes 6 and 18, which have 35,574 and 16,450 SNPs, respectively. Association studies, including single SNP and haplotype-based analyses, were applied to the data on those two chromosomes. Specifically, we conducted a generalized linear model with regularization (rGLM) approach for detecting disease-haplotype association using unphased SNP data. A total of 444 and 43 four-SNP tests were found to be significant at the Bonferroni corrected 5% significance level on chromosome 6 and 18, respectively

Retroperitoneal Metastatic Adenocarcinoma Complicated with Necrotizing Fasciitis of the Thigh in a Patient with Advanced Rectal Colon Cancer

Background: Necrotizing fasciitis of the thigh due to colon cancer has not been previously reported, especially during radiotherapy. Case Presentation: A 73-year-old woman admitted to our hospital was diagnosed with sigmoid colon cancer that had spread to the left psoas muscle; radiotherapy was performed. Three months after the initiation of radiotherapy, the patient developed gait disturbance, poor appetite and high fever and was therefore admitted to the emergency department of our hospital. Blood examination revealed generalized inflammation with a high white blood cell count and C-reactive protein level. Computed tomography of the abdomen revealed fluid and gas tracking from the retroperitoneum into the intramuscular plane of the grossly enlarged right thigh. Consequently, emergent debridement was not performed and conservative therapy was done. The patient died. Conclusion: Necrotizing fasciitis of the thigh due to the spread of rectal colon cancer is unusual, but this fatal complication should be considered during radiotherapy in patients with unresectable colorectal cancer

Guided tissue regeneration combined with bone allograft in infrabony defects: Clinical outcomes and assessment of prognostic factors

BackgroundClinical data on the outcomes of guided tissue regeneration (GTR) is scarce. The aim of this retrospective cohort study was to evaluate the outcomes after GTR, their stability and the survival of the treated teeth with periodontal infrabony defects.MethodsInfrabony defects treated with GTR using a bioabsorbable membrane and a bone graft substitute with at least 1- year follow- up were included. Survival and regression analyses were conducted to evaluate the outcomes, their stability, and the retention of the teeth. The effect of recorded variables on clinical attachment gain (CAL) and tooth survival were assessed via Cox proportional- hazards models and multivariate generalized linear models.ResultsOne hundred seventy- five treated defects were selected from a total of 641 charts. The average follow- up was 5.75 ± 4.6 years. At baseline, the mean CAL was 9.56 ± 1.93 mm with a mean pocket depth (PD) of 8.41 ± 1.42 mm. At the 1- year post- surgical recall, 3.55 ± 1.85 mm of CAL gain and 3.87 ± 1.87 mm PD reduction were observed (P < 0.05). The 5- and 10- year survival rates of the treated teeth were 85.0% and 72.7%, respectively. Baseline PD, smoking, and membrane exposure were significantly related to CAL gain, whereas baseline CAL, age, frequency in maintenance visits significantly affected tooth survival.ConclusionWithin the limitations of this study, data suggests GTR is a good option for the treatment of infrabony defects because it can improve both tooth retention rate and overall clinical outcomes.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/156006/1/jper10462.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/156006/2/jper10462_am.pd

EmbeddingTree: Hierarchical Exploration of Entity Features in Embedding

Embedding learning transforms discrete data entities into continuous numerical representations, encoding features/properties of the entities. Despite the outstanding performance reported from different embedding learning algorithms, few efforts were devoted to structurally interpreting how features are encoded in the learned embedding space. This work proposes EmbeddingTree, a hierarchical embedding exploration algorithm that relates the semantics of entity features with the less-interpretable embedding vectors. An interactive visualization tool is also developed based on EmbeddingTree to explore high-dimensional embeddings. The tool helps users discover nuance features of data entities, perform feature denoising/injecting in embedding training, and generate embeddings for unseen entities. We demonstrate the efficacy of EmbeddingTree and our visualization tool through embeddings generated for industry-scale merchant data and the public 30Music listening/playlists dataset.Comment: 5 pages, 3 figures, accepted by PacificVis 202