Surgical Antibiotic Prophylaxis in Children Undergoing Surgery: A Systematic Review and Meta-Analysis.

Background: To establish the role of surgical antibiotic prophylaxis (SAP) in the prevention of surgical site infection (SSI) in children undergoing surgery. Design: A systematic review and meta-analysis of six databases: MEDLINE (PubMed), EMBASE, CINAHL Plus, Cochrane Library, Web of Science, and Scopus. Study Selection: Included studies (irrespective of design) compared outcomes in children undergoing surgery, aged 0 to 21 years who received SAP with those who did not, with SSI as an outcome, using the U.S. Centers for Disease Control and Prevention (CDC) definitions for SSI. Data Extraction: Two independent reviewers applied eligibility criteria, assessed the risk of bias, and extracted data. Results: A total of six randomized control trials and 26 observational studies including 202,593 surgical procedures among 202,405 participants were included in the review. The pooled odds ratio of SSI was 1.20; (95% confidence interval [CI], 0.91-1.58) comparing those receiving SAP with those not receiving SAP, with moderate heterogeneity in effect size between studies (τ2 = 0.246; χ2 = 69.75; p < 0.001; I2 = 57.0%). There was insufficient data on many factors known to be associated with SSI, such as cost, length of stay, re-admission, and re-operation; it was therefore not possible to perform subanalyses on these. Conclusions: This review and metanalysis did not find a preventive action of SAP against SSI, and our results suggest that SAP should not be used in surgical wound class (SWC) I procedures in children. However, considering the poor quality of included studies, the principal message of this study is in highlighting the absence of quality data to drive evidence-based decision-making in SSI prevention in children, and in advocating for more research in this field.

A Systematic Review and Meta-analysis on the Incidence of Patients With Lower-Limb Amputations Who Developed Symptomatic Neuromata in the Residual Limb.

BACKGROUND: Neuromata developed after major extremity amputation can cause pain, limit the use of prosthetics, and negatively affect the quality of life. The frequency of postamputation neuroma varies widely. The objective of this study was to determine the incidence of patients who developed symptomatic neuromata after lower-limb amputation through a systematic review and meta-analysis. METHODS: A systematic review of the literature was performed on 4 major databases. Studies that reported the incidence of symptomatic neuroma in lower-limb amputees were included. A meta-analysis was performed to calculate the pooled incidence of neuromata. RESULTS: Thirteen studies consisting of 1329 patients were included in this meta-analysis. The reported incidence of patients who developed symptomatic neuromata ranged between 4% and 49%. The median duration of follow-up was 8.6 years (interquartile range, 2.0-17.4 years). The pooled percentage (95% confidence interval [CI]) of lower-limb amputees who developed symptomatic neuromata was 19% (12%-29%). In studies with a duration of follow-up at least 3 years, the pooled percentage (95% CI) of lower-limb amputees who developed symptomatic neuromata was 30% (22%-40%). In studies with a follow-up period of fewer than 3 years, the pooled percentage (95% CI) of neuroma incidence was 3% (2%-6%). CONCLUSIONS: In summary, the overall incidence of patients who developed symptomatic neuromata was 19% or approximately 1 in 5 lower-limb amputees. Symptomatic neuromata are more commonly diagnosed when the follow-up period is longer than 3 years. These findings suggest that neuroma after amputation might be underestimated in studies with a short duration of follow-up.

Patterns and levels of illicit drug use among men who have sex with men in Asia.

BACKGROUND: The emergence of gay communities in Asia may predispose men who have sex with men (MSM) to drug use. We describe patterns and levels of illicit drug use, and characteristics of stimulant drug users among MSM in Asia. METHODS: A cross-sectional Internet-based survey was conducted among 10,861 participants recruited through online methods. Pearson's chi-square tests were used to compare patterns of drug use by participants' HIV status. Multivariable logistic regression analysis was conducted to identify significant correlates of stimulant drug use. RESULTS: Overall, 16.7% of participants reported recreational drug use in the past 6 months. Ecstasy (8.1%) and Viagra (7.9%) were the most prevalent drugs being used. HIV-positive MSM reported significantly higher levels of individual drug use and polydrug use compared to HIV-negative/unknown MSM. Being gay (AOR=1.62, 95% CI: 1.28, 2.05), having casual male partners only or having both casual and regular partners (AOR=2.05, 95% CI: 1.66, 2.53; AOR=2.97, 95% CI: 2.39, 3.69), HIV-positive status (AOR=4.54, 95% CI: 3.63, 5.69), sex work (AOR=1.52, 95% CI: 1.19, 1.93), and having more gay friends ("Some" vs. "A few/None" AOR=1.98, 95% CI: 1.62, 2.43; "Most/All" vs. "A few/None" AOR=4.59, 95% CI: 3.77, 5.59) were independently associated with stimulant drug use. CONCLUSIONS: Our findings point to the urgency of incorporating substance use prevention and treatment into current HIV prevention activities in Asia, which must use a harm reduction approach and galvanize dignity.

Ranking candidate disease genes from gene expression and protein interaction: a Katz-centrality based approach.

Many diseases have complex genetic causes, where a set of alleles can affect the propensity of getting the disease. The identification of such disease genes is important to understand the mechanistic and evolutionary aspects of pathogenesis, improve diagnosis and treatment of the disease, and aid in drug discovery. Current genetic studies typically identify chromosomal regions associated specific diseases. But picking out an unknown disease gene from hundreds of candidates located on the same genomic interval is still challenging. In this study, we propose an approach to prioritize candidate genes by integrating data of gene expression level, protein-protein interaction strength and known disease genes. Our method is based only on two, simple, biologically motivated assumptions--that a gene is a good disease-gene candidate if it is differentially expressed in cases and controls, or that it is close to other disease-gene candidates in its protein interaction network. We tested our method on 40 diseases in 58 gene expression datasets of the NCBI Gene Expression Omnibus database. On these datasets our method is able to predict unknown disease genes as well as identifying pleiotropic genes involved in the physiological cellular processes of many diseases. Our study not only provides an effective algorithm for prioritizing candidate disease genes but is also a way to discover phenotypic interdependency, cooccurrence and shared pathophysiology between different disorders.