抑菌生的研究总结报告

本文报告一种由枯草杆菌制成的生态制剂,并命名为抑菌生(Subtilobiogen)。该制剂对创、烧伤感染有治疗作用。经过安全试验、急性毒性试验、Ames试验和微核试验证明,该制剂是一种无害、无毒和有致突变作用的活菌制剂。抑菌生有膏剂、乳剂及粉剂3种剂型。抑菌生在试管和体内对金黄色葡萄球菌、绿脓杆菌和大肠杆菌均具有抑菌作用。临床观察证明,将抑菌生喷洒在创面上,对浅Ⅱ°度、深Ⅱ°度及混合型烧伤感染均具有明显疗效。实验组(181例)与对照组(174例)相比较,在统计学上具有显著性差异。抑菌生的作用机制,经过初步试验证明,与营养争夺和占位性保护有关,因为枯草杆菌的生长速度超过金黄色葡萄球菌、绿脓杆菌和大肠杆菌的生长速度。     

Dye standards, Part II.5: Pararosaniline (CI 42500)

The names and affiliations of the SRMTS members are cited in Part I: terminology and general principles,Histochem. J. (1992)24, 217–19.     

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.     

Recovery of Renal Function among ESRD Patients in the US Medicare Program

Background

Patients started on long term hemodialysis have typically had low rates of reported renal recovery with recent estimates ranging from 0.9–2.4% while higher rates of recovery have been reported in cohorts with higher percentages of patients with acute renal failure requiring dialysis.

Study Design

Our analysis followed approximately 194,000 patients who were initiated on hemodialysis during a 2-year period (2008 & 2009) with CMS-2728 forms submitted to CMS by dialysis facilities, cross-referenced with patient record updates through the end of 2010, and tracked through December 2010 in the CMS SIMS registry.

Results

We report a sustained renal recovery (i.e no return to ESRD during the available follow up period) rate among Medicare ESRD patients of > 5% - much higher than previously reported. Recovery occurred primarily in the first 2 months post incident dialysis, and was more likely in cases with renal failure secondary to etiologies associated with acute kidney injury. Patients experiencing sustained recovery were markedly less likely than true long-term ESRD patients to have permanent vascular accesses in place at incident hemodialysis, while non-White patients, and patients with any prior nephrology care appeared to have significantly lower rates of renal recovery. We also found widespread geographic variation in the rates of renal recovery across the United States.

Conclusions

Renal recovery rates in the US Medicare ESRD program are higher than previously reported and appear to have significant geographic variation. Patients with diagnoses associated with acute kidney injury who are initiated on long-term hemodialysis have significantly higher rates of renal recovery than the general ESRD population and lower rates of permanent access placement.     

Analysis of Serum Inflammatory Mediators Identifies Unique Dynamic Networks Associated with Death and Spontaneous Survival in Pediatric Acute Liver Failure

Background

Tools to predict death or spontaneous survival are necessary to inform liver transplantation (LTx) decisions in pediatric acute liver failure (PALF), but such tools are not available. Recent data suggest that immune/inflammatory dysregulation occurs in the setting of acute liver failure. We hypothesized that specific, dynamic, and measurable patterns of immune/inflammatory dysregulation will correlate with outcomes in PALF.

Methods

We assayed 26 inflammatory mediators on stored serum samples obtained from a convenience sample of 49 children in the PALF study group (PALFSG) collected within 7 days after enrollment. Outcomes were assessed within 21 days of enrollment consisting of spontaneous survivors, non-survivors, and LTx recipients. Data were subjected to statistical analysis, patient-specific Principal Component Analysis (PCA), and Dynamic Bayesian Network (DBN) inference.

Findings

Raw inflammatory mediator levels assessed over time did not distinguish among PALF outcomes. However, DBN analysis did reveal distinct interferon-gamma-related networks that distinguished spontaneous survivors from those who died. The network identified in LTx patients pre-transplant was more like that seen in spontaneous survivors than in those who died, a finding supported by PCA.

Interpretation

The application of DBN analysis of inflammatory mediators in this small patient sample appears to differentiate survivors from non-survivors in PALF. Patterns associated with LTx pre-transplant were more like those seen in spontaneous survivors than in those who died. DBN-based analyses might lead to a better prediction of outcome in PALF, and could also have more general utility in other complex diseases with an inflammatory etiology.     

Mapping the Genetic Variation of Regional Brain Volumes as Explained by All Common SNPs from the ADNI Study

Typically twin studies are used to investigate the aggregate effects of genetic and environmental influences on brain phenotypic measures. Although some phenotypic measures are highly heritable in twin studies, SNPs (single nucleotide polymorphisms) identified by genome-wide association studies (GWAS) account for only a small fraction of the heritability of these measures. We mapped the genetic variation (the proportion of phenotypic variance explained by variation among SNPs) of volumes of pre-defined regions across the whole brain, as explained by 512,905 SNPs genotyped on 747 adult participants from the Alzheimer''s Disease Neuroimaging Initiative (ADNI). We found that 85% of the variance of intracranial volume (ICV) (p = 0.04) was explained by considering all SNPs simultaneously, and after adjusting for ICV, total grey matter (GM) and white matter (WM) volumes had genetic variation estimates near zero (p = 0.5). We found varying estimates of genetic variation across 93 non-overlapping regions, with asymmetry in estimates between the left and right cerebral hemispheres. Several regions reported in previous studies to be related to Alzheimer''s disease progression were estimated to have a large proportion of volumetric variance explained by the SNPs.     

Using Manifold Learning for Atlas Selection in Multi-Atlas Segmentation

Multi-atlas segmentation has been widely used to segment various anatomical structures. The success of this technique partly relies on the selection of atlases that are best mapped to a new target image after registration. Recently, manifold learning has been proposed as a method for atlas selection. Each manifold learning technique seeks to optimize a unique objective function. Therefore, different techniques produce different embeddings even when applied to the same data set. Previous studies used a single technique in their method and gave no reason for the choice of the manifold learning technique employed nor the theoretical grounds for the choice of the manifold parameters. In this study, we compare side-by-side the results given by 3 manifold learning techniques (Isomap, Laplacian Eigenmaps and Locally Linear Embedding) on the same data set. We assess the ability of those 3 different techniques to select the best atlases to combine in the framework of multi-atlas segmentation. First, a leave-one-out experiment is used to optimize our method on a set of 110 manually segmented atlases of hippocampi and find the manifold learning technique and associated manifold parameters that give the best segmentation accuracy. Then, the optimal parameters are used to automatically segment 30 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). For our dataset, the selection of atlases with Locally Linear Embedding gives the best results. Our findings show that selection of atlases with manifold learning leads to segmentation accuracy close to or significantly higher than the state-of-the-art method and that accuracy can be increased by fine tuning the manifold learning process.