Orthopedic Articular and Periarticular Joint Infections.

Acute nontraumatic joint pain has an extensive differential. Emergency physicians must be adept at identifying limb and potentially life-threatening infection. Chief among these is septic arthritis. In addition to knowing how these joint infections typically present, clinicians need to be aware of host and pathogen factors that can lead to more insidious presentations and how these factors impact the interpretation of diagnostic tests.

Pervasive Sharing of Causal Genetic Risk Factors Contributes to Clinical and Molecular Overlap between Sjögren's Disease and Systemic Lupus Erythematosus.

SjD (Sjögren's Disease) and SLE (Systemic Lupus Erythematosus) are similar diseases. There is extensive overlap between the two in terms of both clinical features and pathobiologic mechanisms. Shared genetic risk is a potential explanation of this overlap. In this study, we evaluated whether these diseases share causal genetic risk factors. We compared the causal genetic risk for SLE and SjD using three complementary approaches. First, we examined the published GWAS results for these two diseases by analyzing the predicted causal gene protein-protein interaction networks of both diseases. Since this method does not account for overlapping risk intervals, we examined whether such intervals also overlap. Third, we used two-sample Mendelian randomization (two sample MR) using GWAS summary statistics to determine whether risk variants for SLE are causal for SjD and vice versa. We found that both the putative causal genes and the genomic risk intervals for SLE and SjD overlap 28- and 130-times more than expected by chance (p < 1.1 × 10-24 and p < 1.1 × 10-41, respectively). Further, two sample MR analysis confirmed that alone or in aggregate, SLE is likely causal for SjD and vice versa. [SjD variants predicting SLE: OR = 2.56; 95% CI (1.98-3.30); p < 1.4 × 10-13, inverse-variance weighted; SLE variants predicting SjD: OR = 1.36; 95% CI (1.26-1.47); p < 1.6 × 10-11, inverse-variance weighted]. Notably, some variants have disparate impact in terms of effect size across disease states. Overlapping causal genetic risk factors were found for both diseases using complementary approaches. These observations support the hypothesis that shared genetic factors drive the clinical and pathobiologic overlap between these diseases. Our study has implications for both differential diagnosis and future genetic studies of these two conditions.

Serum kisspeptin is higher in hypertensive than non-hypertensive female subjects and positively correlated with systolic blood pressure.

BACKGROUND: Kisspeptin has a major role in reproductive regulation. Furthermore, it is also involved in metabolic and cardiovascular regulation as well as is a potent vasoconstrictor. This study aimed to: 1) determine correlations between serum kisspeptin levels with obesity/metabolic parameters; 2) compare parameters between non-hypertensive ([non-HT] N.=15) and hypertensive ([HT] N.=15) female subjects; and 3) determine correlations between leptin, systolic blood pressure (SBP) or diastolic blood pressure (DBP) with obesity and metabolic factors. METHODS: Clinical parameters and fasting blood and adipose tissue samples were collected from women undergoing open abdominal surgery. RESULTS: Serum kisspeptin was not correlated with obesity parameters but was positively correlated with only SBP (P<0.05). Serum kisspeptin, SBP, DBP, body weight, waist circumference, hip circumference, plasma glucose, plasma insulin, the homeostatic model assessment for insulin resistance (HOMA-IR), and height of visceral adipocytes (VA) were higher but the Quantitative Insulin Sensitivity Check Index (QUICKI) was lower in hypertensive compared to non-hypertensive female subjects (P<0.05). Leptin was positively correlated with obesity and metabolic paramters including area, width, and perimeter of subcutaneous adipocytes, and area, width, height, and perimeter of VA (P<0.05) but was negatively correlated the QUICKI (P<0.001). SBP had positive correlations with insulin, glucose, HOMA-IR, and kisspeptin, but had a negative correlation with QUICKI (P<0.05). DBP had positive correlations with body weight, BMI, waist circumference, hip circumference, insulin, glucose, HOMA-IR, and width of VA (P<0.05), but had a negative correlation with the QUICKI (P<0.05). CONCLUSIONS: Kisspeptin, obesity especially visceral adiposity, and insulin resistance might contribute to increased blood pressure. Further studies are required to reveal the underlying mechanism of kisspeptin on metabolic and cardiovascular regulation.