When gender stereotypes get male adolescents into trouble : a longitudinal study on gender conformity pressure as a predictor of school misconduct

School misconduct is a threat to educational careers and learning. The present study sheds light on why male adolescents in particular are prone to school misconduct. Qualitative research has argued that male adolescents' construction of masculinity is a factor driving their school misbehavior. We examined the role of felt pressure to conform to gender stereotypes in predicting school misconduct among male and female adolescents. Data were provided by a three-wave panel study encompassing more than 4200 Flemish early adolescents (ages 12-14). Three-level growth curve models showed that male adolescents misbehaved more in school than female adolescents did. Male adolescents also demonstrated a steeper increase in school misconduct than female adolescents. Furthermore, greater felt gender conformity pressure predicted an increase in school misconduct in male adolescents but not in female adolescents. We conclude that school misconduct forms part of an enactment of masculine gender identity with detrimental consequences for male adolescents' educational achievement

A Synthetic HIV-1 Subtype C Backbone Generates Comparable PR and RT Resistance Profiles to a Subtype B Backbone in a Recombinant Virus Assay

In order to determine phenotypic protease and reverse transcriptase inhibitor-associated resistance in HIV subtype C virus, we have synthetically constructed an HIV-1 subtype C (HIV-1-C) viral backbone for use in a recombinant virus assay. The in silico designed viral genome was divided into 4 fragments, which were chemically synthesized and joined together by conventional subcloning. Subsequently, gag-protease-reverse-transcriptase (GPRT) fragments from 8 HIV-1 subtype C-infected patient samples were RT-PCR-amplified and cloned into the HIV-1-C backbone (deleted for GPRT) using In-Fusion reagents. Recombinant viruses (1 to 5 per patient sample) were produced in MT4-eGFP cells where cyto-pathogenic effect (CPE), p24 and Viral Load (VL) were monitored. The resulting HIV-1-C recombinant virus stocks (RVS) were added to MT4-eGFP cells in the presence of serial dilutions of antiretroviral drugs (PI, NNRTI, NRTI) to determine the fold-change in IC50 compared to the IC50 of wild-type HIV-1 virus. Additionally, viral RNA was extracted from the HIV-1-C RVS and the amplified GPRT products were used to generate recombinant virus in a subtype B backbone. Phenotypic resistance profiles in a subtype B and subtype C backbone were compared. The following observations were made: i) functional, infectious HIV-1 subtype C viruses were generated, confirmed by VL and p24 measurements; ii) their rate of infection was slower than viruses generated in the subtype B backbone; iii) they did not produce clear CPE in MT4 cells; and iv) drug resistance profiles generated in both backbones were very similar, including re-sensitizing effects like M184V on AZT

HIV-1 Phenotypic Reverse Transcriptase Inhibitor Drug Resistance Test Interpretation Is Not Dependent on the Subtype of the Virus Backbone

To date, the majority of HIV-1 phenotypic resistance testing has been performed with subtype B virus backbones (e.g. HXB2). However, the relevance of using this backbone to determine resistance in non-subtype B HIV-1 viruses still needs to be assessed. From 114 HIV-1 subtype C clinical samples (36 ARV-naïve, 78 ARV-exposed), pol amplicons were produced and analyzed for phenotypic resistance using both a subtype B- and C-backbone in which the pol fragment was deleted. Phenotypic resistance was assessed in resulting recombinant virus stocks (RVS) for a series of antiretroviral drugs (ARV's) and expressed as fold change (FC), yielding 1660 FC comparisons. These Antivirogram® derived FC values were categorized as having resistant or sensitive susceptibility based on biological cut-off values (BCOs). The concordance between resistance calls obtained for the same clinical sample but derived from two different backbones (i.e. B and C) accounted for 86.1% (1429/1660) of the FC comparisons. However, when taking the assay variability into account, 95.8% (1590/1660) of the phenotypic data could be considered as being concordant with respect to their resistance call. No difference in the capacity to detect resistance associated with M184V, K103N and V106M mutations was noted between the two backbones. The following was concluded: (i) A high level of concordance was shown between the two backbone phenotypic resistance profiles; (ii) Assay variability is largely responsible for discordant results (i.e. for FC values close to BCO); (iii) Confidence intervals should be given around the BCO's, when assessing resistance in HIV-1 subtype C; (iv) No systematic resistance under- or overcalling of subtype C amplicons in the B-backbone was observed; (v) Virus backbone subtype sequence variability outside the pol region does not contribute to phenotypic FC values. In conclusion the HXB2 virus backbone remains an acceptable vector for phenotyping HIV-1 subtype C pol amplicons

Cross-validated stepwise regression for identification of novel non-nucleoside reverse transcriptase inhibitor resistance associated mutations

<p>Abstract</p> <p>Background</p> <p>Linear regression models are used to quantitatively predict drug resistance, the phenotype, from the HIV-1 viral genotype. As new antiretroviral drugs become available, new resistance pathways emerge and the number of resistance associated mutations continues to increase. To accurately identify which drug options are left, the main goal of the modeling has been to maximize predictivity and not interpretability. However, we originally selected linear regression as the preferred method for its transparency as opposed to other techniques such as neural networks. Here, we apply a method to lower the complexity of these phenotype prediction models using a 3-fold cross-validated selection of mutations.</p> <p>Results</p> <p>Compared to standard stepwise regression we were able to reduce the number of mutations in the reverse transcriptase (RT) inhibitor models as well as the number of interaction terms accounting for synergistic and antagonistic effects. This reduction in complexity was most significant for the non-nucleoside reverse transcriptase inhibitor (NNRTI) models, while maintaining prediction accuracy and retaining virtually all known resistance associated mutations as first order terms in the models. Furthermore, for etravirine (ETR) a better performance was seen on two years of unseen data. By analyzing the phenotype prediction models we identified a list of forty novel NNRTI mutations, putatively associated with resistance. The resistance association of novel variants at known NNRTI resistance positions: 100, 101, 181, 190, 221 and of mutations at positions not previously linked with NNRTI resistance: 102, 139, 219, 241, 376 and 382 was confirmed by phenotyping site-directed mutants.</p> <p>Conclusions</p> <p>We successfully identified and validated novel NNRTI resistance associated mutations by developing parsimonious resistance prediction models in which repeated cross-validation within the stepwise regression was applied. Our model selection technique is computationally feasible for large data sets and provides an approach to the continued identification of resistance-causing mutations.</p

Overview of the clones per sample, day of harvest (subtype C and B), VL and p24 measurements as well as the corresponding resistance-associated mutations per clone (as referenced by IAS-USA, ANRS) per sample.

<p>“DTH” Days to harvest.</p

Sequencing primers overview.

<p>Sequencing primers overview.</p

Experimental flow.

<p>Flow of the testing of the subtype C GPRT amplicons in the pGEM-HIV-1-C-Δgprt-BstEII-V (pHIV-1-C-Δgprt) and the pGEM-HXB2-Δgprt-BstEII (pHIV-1-B-Δgprt) backbones. “TRF”: transfection (Amaxa); “FC”: Fold Change; Red boxes: phenotypes; Green boxes: genotypes.</p