Maternal HIV-1 envelope-specific antibody responses and reduced risk of perinatal transmission.

Despite the wide availability of antiretroviral drugs, more than 250,000 infants are vertically infected with HIV-1 annually, emphasizing the need for additional interventions to eliminate pediatric HIV-1 infections. Here, we aimed to define humoral immune correlates of risk of mother-to-child transmission (MTCT) of HIV-1, including responses associated with protection in the RV144 vaccine trial. Eighty-three untreated, HIV-1-transmitting mothers and 165 propensity score-matched nontransmitting mothers were selected from the Women and Infants Transmission Study (WITS) of US nonbreastfeeding, HIV-1-infected mothers. In a multivariable logistic regression model, the magnitude of the maternal IgG responses specific for the third variable loop (V3) of the HIV-1 envelope was predictive of a reduced risk of MTCT. Neutralizing Ab responses against easy-to-neutralize (tier 1) HIV-1 strains also predicted a reduced risk of peripartum transmission in secondary analyses. Moreover, recombinant maternal V3-specific IgG mAbs mediated neutralization of autologous HIV-1 isolates. Thus, common V3-specific Ab responses in maternal plasma predicted a reduced risk of MTCT and mediated autologous virus neutralization, suggesting that boosting these maternal Ab responses may further reduce HIV-1 MTCT.

Outcomes and Predictors of Mortality in Neurosurgical Patients at Mbarara Regional Referral Hospital

Background:

Knowing the scope of neurosurgical disease at Mbarara Hospital is critical for infrastructure planning, education and training. In this study, we aim to evaluate the neurosurgical outcomes and identify predictors of mortality in order to potentiate platforms for more effective interventions and inform future research efforts at Mbarara Hospital.

Methods:

This is retrospective chart review including patients of all ages with a neurosurgical disease or injury presenting to Mbarara Regional Referral Hospital (MRRH) between January 2012 to September 2015. Descriptive statistics were presented. A univariate analysis was used to obtain the odds ratios of mortality and 95% confidence intervals. Predictors of mortality were determined using multivariate logistic regression model.

Results:

A total of 1876 charts were reviewed. Of these, 1854 (had complete data and were?) were included in the analysis. The overall mortality rate was 12.75%; the mortality rates among all persons who underwent a neurosurgical procedure was 9.72%, and was 13.68% among those who did not undergo a neurosurgical procedure. Over 50% of patients were between 19 and 40 years old and the majority of were males (76.10%). The overall median length of stay was 5 days. Of all neurosurgical admissions, 87% were trauma patients. In comparison to mild head injury, closed head injury and intracranial hematoma patients were 5 (95% CI: 3.77, 8.26) and 2.5 times (95% CI: 1.64,3.98) more likely to die respectively. Procedure and diagnostic imaging were independent negative predictors of mortality (P <0.05). While age, ICU admission, admission GCS were positive predictors of mortality (P <0.05).

Conclusions:

The majority of hospital admissions were TBI patients, with RTIs being the most common mechanism of injury. Age, ICU admission, admission GCS, diagnostic imaging and undergoing surgery were independent predictors of mortality. Going forward, further exploration of patient characteristics is necessary to fully describe mortality outcomes and implement resource appropriate interventions that ultimately improve morbidity and mortality.

Risk Prediction for Epithelial Ovarian Cancer in 11 United States-Based Case-Control Studies: Incorporation of Epidemiologic Risk Factors and 17 Confirmed Genetic Loci.

Previously developed models for predicting absolute risk of invasive epithelial ovarian cancer have included a limited number of risk factors and have had low discriminatory power (area under the receiver operating characteristic curve (AUC) < 0.60). Because of this, we developed and internally validated a relative risk prediction model that incorporates 17 established epidemiologic risk factors and 17 genome-wide significant single nucleotide polymorphisms (SNPs) using data from 11 case-control studies in the United States (5,793 cases; 9,512 controls) from the Ovarian Cancer Association Consortium (data accrued from 1992 to 2010). We developed a hierarchical logistic regression model for predicting case-control status that included imputation of missing data. We randomly divided the data into an 80% training sample and used the remaining 20% for model evaluation. The AUC for the full model was 0.664. A reduced model without SNPs performed similarly (AUC = 0.649). Both models performed better than a baseline model that included age and study site only (AUC = 0.563). The best predictive power was obtained in the full model among women younger than 50 years of age (AUC = 0.714); however, the addition of SNPs increased the AUC the most for women older than 50 years of age (AUC = 0.638 vs. 0.616). Adapting this improved model to estimate absolute risk and evaluating it in prospective data sets is warranted.