甲型肝炎病毒对理化因子的稳定性

用放射免疫成斑法研究了甲型肝炎病毒FM-175株的理化稳定性和灭活条件,证明甲型肝炎病毒理化稳定性与其它肠道病毒相似,具有广范围的pH(2～10)稳定性;Mg~(++)和Ca可增强其热稳定性,不能抵抗冷冻干燥,但对热的抵抗力明显高于普通肠道病毒,可被紫外线迅速杀灭,也可被多种消毒剂如3～8％的甲醛液,50～90％的乙醇,2％的石炭酸及400ppm的有效氯等杀灭,但可抵抗0.1％甲醛液,2～5％的来苏儿及200ppm的有效氯1小时以上,本研究工作为甲型肝炎病毒的理化性状、保存及消毒条件等提供了实验数据。     

吉林省寄螨亚科三新种(啤螨目:寄螨科)

本文记述寄螨亚科(Parasitinae)三新种。模式标本保存于吉林省地方病第一防治研究所。文内测量单位为微米,括号内为测量平均值。     

Loss to Clinic and Five-Year Mortality among HIV-Infected Antiretroviral Therapy Initiators

Missing outcome data due to loss to follow-up occurs frequently in clinical cohort studies of HIV-infected patients. Censoring patients when they become lost can produce inaccurate results if the risk of the outcome among the censored patients differs from the risk of the outcome among patients remaining under observation. We examine whether patients who are considered lost to follow up are at increased risk of mortality compared to those who remain under observation. Patients from the US Centers for AIDS Research Network of Integrated Clinical Systems (CNICS) who newly initiated combination antiretroviral therapy between January 1, 1998 and December 31, 2009 and survived for at least one year were included in the study. Mortality information was available for all participants regardless of continued observation in the CNICS. We compare mortality between patients retained in the cohort and those lost-to-clinic, as commonly defined by a 12-month gap in care. Patients who were considered lost-to-clinic had modestly elevated mortality compared to patients who remained under observation after 5 years (risk ratio (RR): 1.2; 95% CI: 0.9, 1.5). Results were similar after redefining loss-to-clinic as 6 months (RR: 1.0; 95% CI: 0.8, 1.3) or 18 months (RR: 1.2; 95% CI: 0.8, 1.6) without a documented clinic visit. The small increase in mortality associated with becoming lost to clinic suggests that these patients were not lost to care, rather they likely transitioned to care at a facility outside the study. The modestly higher mortality among patients who were lost-to-clinic implies that when we necessarily censor these patients in studies of time-varying exposures, we are likely to incur at most a modest selection bias.     

Programmatic Cost Evaluation of Nontargeted Opt-Out Rapid HIV Screening in the Emergency Department

Background

The Centers for Disease Control and Prevention recommends nontargeted opt-out HIV screening in healthcare settings. Cost effectiveness is critical when considering potential screening methods. Our goal was to compare programmatic costs of nontargeted opt-out rapid HIV screening with physician-directed diagnostic rapid HIV testing in an urban emergency department (ED) as part of the Denver ED HIV Opt-Out Trial.

Methods

This was a prospective cohort study nested in a larger quasi-experiment. Over 16 months, nontargeted rapid HIV screening (intervention) and diagnostic rapid HIV testing (control) were alternated in 4-month time blocks. During the intervention phase, patients were offered HIV testing using an opt-out approach during registration; during the control phase, physicians used a diagnostic approach to offer HIV testing to patients. Each method was fully integrated into ED operations. Direct program costs were determined using the perspective of the ED. Time-motion methodology was used to estimate personnel activity costs. Costs per patient newly-diagnosed with HIV infection by intervention phase, and incremental cost effectiveness ratios were calculated.

Results

During the intervention phase, 28,043 eligible patients were included, 6,933 (25%) completed testing, and 15 (0.2%, 95% CI: 0.1%–0.4%) were newly-diagnosed with HIV infection. During the control phase, 29,925 eligible patients were included, 243 (0.8%) completed testing, and 4 (1.7%, 95% CI: 0.4%–4.2%) were newly-diagnosed with HIV infection. Total annualized costs for nontargeted screening were $148,997, whereas total annualized costs for diagnostic HIV testing were $31,355. The average costs per HIV diagnosis were $9,932 and $7,839, respectively. Nontargeted HIV screening identified 11 more HIV infections at an incremental cost of $10,693 per additional infection.

Conclusions

Compared to diagnostic testing, nontargeted HIV screening was more costly but identified more HIV infections. More effective and less costly testing strategies may be required to improve the identification of patients with undiagnosed HIV infection in the ED.     

Stevens-Johnson Syndrome Associated with Drugs and Vaccines in Children: A Case-Control Study

Objective

Stevens-Johnson Syndrome (SJS) is one of the most severe muco-cutaneous diseases and its occurrence is often attributed to drug use. The aim of the present study is to quantify the risk of SJS in association with drug and vaccine use in children.

Methods

A multicenter surveillance of children hospitalized through the emergency departments for acute conditions of interest is currently ongoing in Italy. Cases with a diagnosis of SJS were retrieved from all admissions. Parents were interviewed on child’s use of drugs and vaccines preceding the onset of symptoms that led to the hospitalization. We compared the use of drugs and vaccines in cases with the corresponding use in a control group of children hospitalized for acute neurological conditions.

Results

Twenty-nine children with a diagnosis of SJS and 1,362 with neurological disorders were hospitalized between 1^st November 1999 and 31^st October 2012. Cases were more frequently exposed to drugs (79% vs 58% in the control group; adjusted OR 2.4; 95% CI 1.0–6.1). Anticonvulsants presented the highest adjusted OR: 26.8 (95% CI 8.4–86.0). Significantly elevated risks were also estimated for antibiotics use (adjusted OR 3.3; 95% CI 1.5–7.2), corticosteroids (adjusted OR 4.2; 95% CI 1.8–9.9) and paracetamol (adjusted OR 3.2; 95% CI 1.5–6.9). No increased risk was estimated for vaccines (adjusted OR: 0.9; 95% CI 0.3–2.8).

Discussion

Our study provides additional evidence on the etiologic role of drugs and vaccines in the occurrence of SJS in children.     

Assessment of Overlap of Phylogenetic Transmission Clusters and Communities in Simple Sexual Contact Networks: Applications to HIV-1

Background

Transmission patterns of sexually-transmitted infections (STIs) could relate to the structure of the underlying sexual contact network, whose features are therefore of interest to clinicians. Conventionally, we represent sexual contacts in a population with a graph, that can reveal the existence of communities. Phylogenetic methods help infer the history of an epidemic and incidentally, may help detecting communities. In particular, phylogenetic analyses of HIV-1 epidemics among men who have sex with men (MSM) have revealed the existence of large transmission clusters, possibly resulting from within-community transmissions. Past studies have explored the association between contact networks and phylogenies, including transmission clusters, producing conflicting conclusions about whether network features significantly affect observed transmission history. As far as we know however, none of them thoroughly investigated the role of communities, defined with respect to the network graph, in the observation of clusters.

Methods

The present study investigates, through simulations, community detection from phylogenies. We simulate a large number of epidemics over both unweighted and weighted, undirected random interconnected-islands networks, with islands corresponding to communities. We use weighting to modulate distance between islands. We translate each epidemic into a phylogeny, that lets us partition our samples of infected subjects into transmission clusters, based on several common definitions from the literature. We measure similarity between subjects’ island membership indices and transmission cluster membership indices with the adjusted Rand index.

Results and Conclusion

Analyses reveal modest mean correspondence between communities in graphs and phylogenetic transmission clusters. We conclude that common methods often have limited success in detecting contact network communities from phylogenies. The rarely-fulfilled requirement that network communities correspond to clades in the phylogeny is their main drawback. Understanding the link between transmission clusters and communities in sexual contact networks could help inform policymaking to curb HIV incidence in MSMs.     

Epistatic Gene-Based Interaction Analyses for Glaucoma in eMERGE and NEIGHBOR Consortium

Primary open angle glaucoma (POAG) is a complex disease and is one of the major leading causes of blindness worldwide. Genome-wide association studies have successfully identified several common variants associated with glaucoma; however, most of these variants only explain a small proportion of the genetic risk. Apart from the standard approach to identify main effects of variants across the genome, it is believed that gene-gene interactions can help elucidate part of the missing heritability by allowing for the test of interactions between genetic variants to mimic the complex nature of biology. To explain the etiology of glaucoma, we first performed a genome-wide association study (GWAS) on glaucoma case-control samples obtained from electronic medical records (EMR) to establish the utility of EMR data in detecting non-spurious and relevant associations; this analysis was aimed at confirming already known associations with glaucoma and validating the EMR derived glaucoma phenotype. Our findings from GWAS suggest consistent evidence of several known associations in POAG. We then performed an interaction analysis for variants found to be marginally associated with glaucoma (SNPs with main effect p-value <0.01) and observed interesting findings in the electronic MEdical Records and GEnomics Network (eMERGE) network dataset. Genes from the top epistatic interactions from eMERGE data (Likelihood Ratio Test i.e. LRT p-value <1e-05) were then tested for replication in the NEIGHBOR consortium dataset. To replicate our findings, we performed a gene-based SNP-SNP interaction analysis in NEIGHBOR and observed significant gene-gene interactions (p-value <0.001) among the top 17 gene-gene models identified in the discovery phase. Variants from gene-gene interaction analysis that we found to be associated with POAG explain 3.5% of additional genetic variance in eMERGE dataset above what is explained by the SNPs in genes that are replicated from previous GWAS studies (which was only 2.1% variance explained in eMERGE dataset); in the NEIGHBOR dataset, adding replicated SNPs from gene-gene interaction analysis explain 3.4% of total variance whereas GWAS SNPs alone explain only 2.8% of variance. Exploring gene-gene interactions may provide additional insights into many complex traits when explored in properly designed and powered association studies.