Mothers adjust egg size to helper number in a cooperatively breeding cichlid

Mothers should adjust the size of propagules to the selective forces to which these offspring will be exposed. Usually, a larger propagule size is favored when young are exposed to high mortality risk or conspecific competition. Here we test 2 predictions on how egg size should vary with these selective agents. When offspring are cared for by parents and/or alloparents, protection may reduce the predation risk to young, which may allow mothers to invest less per single offspring. In the cooperatively breeding cichlid Neolamprologus pulcher, brood care helpers protect group offspring and reduce the latters' mortality rate. Therefore, females are expected to reduce their investment per egg when more helpers are present. In a first experiment, we tested this prediction by manipulating the helper number. In N. pulcher, helpers compete for dispersal opportunities with similar-sized individuals of neighboring groups. If the expected future competition pressure on young is high, females should increase their investment per offspring to give them a head start. In a second experiment, we tested whether females produce larger eggs when perceived neighbor density is high. Females indeed reduced egg size with increasing helper number. However, we did not detect an effect of local density on egg size, although females took longer to produce the next clutch when local density was high. We argue that females can use the energy saved by adjusting egg size to reduced predation risk to enhance future reproductive output. Adaptive adjustment of offspring size to helper number may be an important, as yet unrecognized, strategy of cooperative breeders.

CD4+ T-cell count increase in HIV-1-infected patients with suppressed viral load within 1 year after start of antiretroviral therapy

BACKGROUND: CD4+ T-cell recovery in patients with continuous suppression of plasma HIV-1 viral load (VL) is highly variable. This study aimed to identify predictive factors for long-term CD4+ T-cell increase in treatment-naive patients starting combination antiretroviral therapy (cART). METHODS: Treatment-naive patients in the Swiss HIV Cohort Study reaching two VL measurements <50 copies/ml >3 months apart during the 1st year of cART were included (n=1816 patients). We studied CD4+ T-cell dynamics until the end of suppression or up to 5 years, subdivided into three periods: 1st year, years 2-3 and years 4-5 of suppression. Multiple median regression adjusted for repeated CD4+ T-cell measurements was used to study the dependence of CD4+ T-cell slopes on clinical covariates and drug classes. RESULTS: Median CD4+ T-cell increases following VL suppression were 87, 52 and 19 cells/microl per year in the three periods. In the multiple regression model, median CD4+ T-cell increases over all three periods were significantly higher for female gender, lower age, higher VL at cART start, CD4+ T-cell <650 cells/microl at start of the period and low CD4+ T-cell increase in the previous period. Patients on tenofovir showed significantly lower CD4+ T-cell increases compared with stavudine. CONCLUSIONS: In our observational study, long-term CD4+ T-cell increase in drug-naive patients with suppressed VL was higher in regimens without tenofovir. The clinical relevance of these findings must be confirmed in, ideally, clinical trials or large, collaborative cohort projects but could influence treatment of older patients and those starting cART at low CD4+ T-cell levels.

CD4+ T cell count recovery in HIV type 1-infected patients is independent of class of antiretroviral therapy

BACKGROUND: In recent years, treatment options for human immunodeficiency virus type 1 (HIV-1) infection have changed from nonboosted protease inhibitors (PIs) to nonnucleoside reverse-transcriptase inhibitors (NNRTIs) and boosted PI-based antiretroviral drug regimens, but the impact on immunological recovery remains uncertain. METHODS: During January 1996 through December 2004 [corrected] all patients in the Swiss HIV Cohort were included if they received the first combination antiretroviral therapy (cART) and had known baseline CD4(+) T cell counts and HIV-1 RNA values (n = 3293). For follow-up, we used the Swiss HIV Cohort Study database update of May 2007 [corrected] The mean (+/-SD) duration of follow-up was 26.8 +/- 20.5 months. The follow-up time was limited to the duration of the first cART. CD4(+) T cell recovery was analyzed in 3 different treatment groups: nonboosted PI, NNRTI, or boosted PI. The end point was the absolute increase of CD4(+) T cell count in the 3 treatment groups after the initiation of cART. RESULTS: Two thousand five hundred ninety individuals (78.7%) initiated a nonboosted-PI regimen, 452 (13.7%) initiated an NNRTI regimen, and 251 (7.6%) initiated a boosted-PI regimen. Absolute CD4(+) T cell count increases at 48 months were as follows: in the nonboosted-PI group, from 210 to 520 cells/muL; in the NNRTI group, from 220 to 475 cells/muL; and in the boosted-PI group, from 168 to 511 cells/muL. In a multivariate analysis, the treatment group did not affect the response of CD4(+) T cells; however, increased age, pretreatment with nucleoside reverse-transcriptase inhibitors, serological tests positive for hepatitis C virus, Centers for Disease Control and Prevention stage C infection, lower baseline CD4(+) T cell count, and lower baseline HIV-1 RNA level were risk factors for smaller increases in CD4(+) T cell count. CONCLUSION: CD4(+) T cell recovery was similar in patients receiving nonboosted PI-, NNRTI-, and boosted PI-based cART.

CD4(+) T cell count decreases by ethnicity among untreated patients with HIV infection in South Africa and Switzerland

BACKGROUND: Estimates of the decrease in CD4(+) cell counts in untreated patients with human immunodeficiency virus (HIV) infection are important for patient care and public health. We analyzed CD4(+) cell count decreases in the Cape Town AIDS Cohort and the Swiss HIV Cohort Study. METHODS: We used mixed-effects models and joint models that allowed for the correlation between CD4(+) cell count decreases and survival and stratified analyses by the initial cell count (50-199, 200-349, 350-499, and 500-750 cells/microL). Results are presented as the mean decrease in CD4(+) cell count with 95% confidence intervals (CIs) during the first year after the initial CD4(+) cell count. RESULTS: A total of 784 South African (629 nonwhite) and 2030 Swiss (218 nonwhite) patients with HIV infection contributed 13,388 CD4(+) cell counts. Decreases in CD4(+) cell count were steeper in white patients, patients with higher initial CD4(+) cell counts, and older patients. Decreases ranged from a mean of 38 cells/microL (95% CI, 24-54 cells/microL) in nonwhite patients from the Swiss HIV Cohort Study 15-39 years of age with an initial CD4(+) cell count of 200-349 cells/microL to a mean of 210 cells/microL (95% CI, 143-268 cells/microL) in white patients in the Cape Town AIDS Cohort > or =40 years of age with an initial CD4(+) cell count of 500-750 cells/microL. CONCLUSIONS: Among both patients from Switzerland and patients from South Africa, CD4(+) cell count decreases were greater in white patients with HIV infection than they were in nonwhite patients with HIV infection.

Accuracy of WHO CD4 cell count criteria for virological failure of antiretroviral therapy

OBJECTIVES: To examine the accuracy of the World Health Organization immunological criteria for virological failure of antiretroviral treatment. METHODS: Analysis of 10 treatment programmes in Africa and South America that monitor both CD4 cell counts and HIV-1 viral load. Adult patients with at least two CD4 counts and viral load measurements between month 6 and 18 after starting a non-nucleoside reverse transcriptase inhibitor-based regimen were included. WHO immunological criteria include CD4 counts persistently <100 cells/microl, a fall below the baseline CD4 count, or a fall of >50% from the peak value. Virological failure was defined as two measurements > or =10 0000 copies/ml (higher threshold) or > or =500 copies/ml (lower threshold). Measures of accuracy with exact binomial 95% confidence intervals (CI) were calculated. RESULTS: A total of 2009 patients were included. During 1856 person-years of follow up 63 patients met the immunological criteria and 35 patients (higher threshold) and 95 patients (lower threshold) met the virological criteria. Sensitivity [95% confidence interval (CI)] was 17.1% (6.6-33.6%) for the higher and 12.6% (6.7-21.0%) for the lower threshold. Corresponding results for specificity were 97.1% (96.3-97.8%) and 97.3% (96.5-98.0%), for positive predictive value 9.5% (3.6-19.6%) and 19.0% (10.2-30.9%) and for negative predictive value 98.5% (97.9-99.0%) and 95.7% (94.7-96.6%). CONCLUSIONS: The positive predictive value of the WHO immunological criteria for virological failure of antiretroviral treatment in resource-limited settings is poor, but the negative predictive value is high. Immunological criteria are more appropriate for ruling out than for ruling in virological failure in resource-limited settings.

Independent association of sleep quality, fatigue, and vital exhaustion with platelet count in patients with a previous venous thromboembolic event

Elevated platelet count might reflect increased inflammation as an etiological factor for venous thromboembolism (VTE). Poor sleep, fatigue, and exhaustion are all associated with inflammation and are also common sequelae of chronic psychological stress that previously predicted increased risk of VTE. We hypothesized that platelet count would be high in patients with VTE who sleep poorly and who are fatigued and exhausted. We investigated 205 patients scheduled for thrombophilia work-up > or =3 months after an objectively diagnosed venous thromboembolic event. They completed the Jenkins Sleep Questionnaire to rate subjective sleep quality and the short forms of the Multidimensional Fatigue Symptom Inventory and Maastricht Vital Exhaustion Questionnaire. Platelet count was determined by a mechanical Coulter counter. Analyses controlled for age, sex, body mass index, time since the index event, and medication. After taking into account these covariates, poorer sleep quality (p = 0.001; DeltaR(2)= 0.046), high fatigue (p = 0.008; DeltaR(2)= 0.032), and vital exhaustion (p = 0.050; DeltaR(2)= 0.017) were all associated with elevated platelet count. In addition, high level of fatigue mediated the relationship between poor sleep quality and elevated platelet count (p = 0.046). Poor sleep quality, high levels of fatigue, and vital exhaustion were identified as correlates of an elevated platelet count in patients with a previous episode of VTE. Given the emerging role of inflammatory processes in VTE, the findings suggest a mechanism through which behavioral and chronic psychological stressors might contribute to incident and recurrent venous thrombotic events.