Objective We examined gene expression profiles in peripheral blood leukocytes (PBL) of patients with osteoarthritis (OA) in comparison with non-OA controls to evaluate whether gene expression profiles could serve as biomarkers of symptomatic knee OA. We also determined whether candidate genomic biomarkers (PBL expression of inflammatory genes) predict increased risk of disease progression in subjects with symptomatic radiographic knee OA Methods Three independent cohorts of patients with knee OA and non-OA controls were studied: two cohorts (“learning cohort” and “validation cohort”) recruited at New York University Hospital for Joint Diseases (NYUHJD) and one (“validation cohort”) at Duke University Medical Center. PBL gene expression was assessed using Affymetrix microarray and confirmed by QPCR. Radiographic progression at 2 years was assessed in 86 patients Results We identified 173 genes significantly up- or down-regulated (≥1.5-fold change) in OA PBL, at a False Discovery Rate (FDR) of 5%. Cluster analysis revealed two distinct subclasses among these OA patients: those with increased expression (≥2-fold) of IL-1β compared to controls, and those with expression comparable to controls. Overexpression of IL-1β in OA subclasses was validated using QPCR (p<0.0001) in all three cohorts. Patients with the inflammatory “IL-1β signature” had higher pain scores, decreased function and were at higher risk for radiographic progression. Conclusion PBLs from patients with symptomatic knee OA display a characteristic transcriptome profile. Moreover, increased expression of IL-1β identifies a subset of OA patients with increased pain who are at higher risk for radiographic progression.

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Emerging evidence suggests that there are IgM-autoantibodies that may play protective roles in SLE. While IgM are often considered polyreactive, we postulate that there are distinct sets of IgM-autoantibodies of defined autoreactive specificities relevant to different features of SLE. We examined the relationships between levels of IgM natural autoantibodies (NAbs) to apoptosis-associated phosphorylcholine (PC) or malondialdehyde (MDA) antigens, with lupus-associated autoantibodies and features of disease, in 120 SLE patients. IgM anti-PC was significantly higher in patients with low disease activity and less organ damage determined by the SELENA-SLEDAI, the physician's evaluation and the SLICC damage score. Furthermore, IgM anti-PC was significantly higher in patients without cardiovascular events. In contrast, IgM anti-cardiolipin and IgM anti-dsDNA were significantly higher in patients without renal disease. These results support the hypothesis that some IgM autoantibodies are part of a natural immune repertoire that provide homeostatic functions and protection from certain clinical lupus features.

Single-nucleotide polymorphisms (SNPs) are a class of attractive genetic markers for population genetic studies and for identifying genetic variations underlying complex traits. However, the usefulness and efficiency of SNPs in comparison to microsatellites in different scientific contexts, e.g., population structure inference or association analysis, still must be systematically evaluated through large empirical studies. In this article, we use the Collaborative Studies on Genetics of Alcoholism (COGA) data from Genetic Analysis Workshop 14 (GAW14) to compare the performance of microsatellites and SNPs in the whole human genome in the context of population structure inference. A total of 328 microsatellites and 15,840 SNPs are used to infer population structure in 236 unrelated individuals. We find that, on average, the informativeness of random microsatellites is four to twelve times that of random SNPs for various population comparisons, which is consistent with previous studies. Our results also indicate that for the combined set of microsatellites and SNPs, SNPs constitute the majority among the most informative markers and the use of these SNPs leads to better inference of population structure than the use of microsatellites. We also find that the inclusion of less informative markers may add noise and worsen the results.

After IVF the incidence of MZT is high, with young oocyte age, year of treatment, and extended culture (or embryo stage at transfer) conferring greatest risk. Regarding MZT type, assisted reproductive technology (ART) procedures may influence the timing of embryonic splitting (i.e., division in early embryonic development may be influenced by zona pellucida [ZP] manipulation whereas later splitting may occur during delayed implantation). Poor obstetric/perinatal outcome is significantly impacted by the presence of an "extra" fetus, as high-order multiple gestation concurrent with an MZT conveyed the worst prognosis.

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BACKGROUND Distal junctional kyphosis (DJK) development after cervical deformity (CD)-corrective surgery is a growing concern for surgeons and patients. Few studies have investigated risk factors that predict the occurrence of DJK. OBJECTIVE To predict DJK development after CD surgery using predictive modeling. METHODS CD criteria was at least one of the following: C2-C7 Coronal/Cobb &gt; 10°, C2-7 sagittal vertical axis (cSVA) &gt; 4 cm, chin-brow vertical angle &gt; 25°. DJK was defined as the development of an angle &lt;−10° from the end of fusion construct to the second distal vertebra, and change in this angle by &lt;−10° from baseline to postoperative. Baseline demographic, clinical, and surgical information were used to predict the occurrence of DJK using generalized linear modeling both as one overall model and as submodels using baseline demographic and clinical predictors or surgical predictors. RESULTS One hundred seventeen CD patients were included. At any postoperative visit up to 1 yr, 23.1% of CD patients developed DJK. DJK was predicted with high accuracy using a combination of baseline demographic, clinical, and surgical factors by the following factors: preoperative neurological deficit, use of transition rod, C2-C7 lordosis (CL)&lt;−12°, T1 slope minus CL &gt; 31°, and cSVA &gt; 54 mm. In the model using only baseline demographic/clinical predictors of DJK, presence of comorbidities, presence of baseline neurological deficit, and high preoperative C2-T3 angle were included in the final model (area under the curve = 87%). The final model using only surgical predictors for DJK included combined approach, posterior upper instrumented vertebrae below C4, use of transition rod, lack of anterior corpectomy, more than 3 posterior osteotomies, and performance of a 3-column osteotomy. CONCLUSION Preoperative assessment and consideration should be given to these factors that are predictive of DJK to mitigate poor outcomes.