Comparison of HIV-1 Genotypic Resistance Test Interpretation Systems in Predicting Virological Outcomes Over Time

Background: Several decision support systems have been developed to interpret HIV-1 drug resistance genotyping results. This study compares the ability of the most commonly used systems (ANRS, Rega, and Stanford's HIVdb) to predict virological outcome at 12, 24, and 48 weeks. Methodology/Principal Findings: Included were 3763 treatment-change episodes (TCEs) for which a HIV-1 genotype was available at the time of changing treatment with at least one follow-up viral load measurement. Genotypic susceptibility scores for the active regimens were calculated using scores defined by each interpretation system. Using logistic regression, we determined the association between the genotypic susceptibility score and proportion of TCEs having an undetectable viral load (<50 copies/ml) at 12 (8-16) weeks (2152 TCEs), 24 (16-32) weeks (2570 TCEs), and 48 (44-52) weeks (1083 TCEs). The Area under the ROC curve was calculated using a 10-fold cross-validation to compare the different interpretation systems regarding the sensitivity and specificity for predicting undetectable viral load. The mean genotypic susceptibility score of the systems was slightly smaller for HIVdb, with 1.92±1.17, compared to Rega and ANRS, with 2.22±1.09 and 2.23±1.05, respectively. However, similar odds ratio's were found for the association between each-unit increase in genotypic susceptibility score and undetectable viral load at week 12; 1.6 [95% confidence interval 1.5-1.7] for HIVdb, 1.7 [1.5-1.8] for ANRS, and 1.7 [1.9-1.6] for Rega. Odds ratio's increased over time, but remained comparable (odds ratio's ranging between 1.9-2.1 at 24 weeks and 1.9-2.

Distinguishing Asthma Phenotypes Using Machine Learning Approaches.

Asthma is not a single disease, but an umbrella term for a number of distinct diseases, each of which are caused by a distinct underlying pathophysiological mechanism. These discrete disease entities are often labelled as asthma endotypes. The discovery of different asthma subtypes has moved from subjective approaches in which putative phenotypes are assigned by experts to data-driven ones which incorporate machine learning. This review focuses on the methodological developments of one such machine learning technique-latent class analysis-and how it has contributed to distinguishing asthma and wheezing subtypes in childhood. It also gives a clinical perspective, presenting the findings of studies from the past 5 years that used this approach. The identification of true asthma endotypes may be a crucial step towards understanding their distinct pathophysiological mechanisms, which could ultimately lead to more precise prevention strategies, identification of novel therapeutic targets and the development of effective personalized therapies

Test beam performance of a CBC3-based mini-module for the Phase-2 CMS Outer Tracker before and after neutron irradiation

The Large Hadron Collider (LHC) at CERN will undergo major upgrades to increase the instantaneous luminosity up to 5–7.5×10$^{34}$ cm$^{-2}$s$^{-1}$. This High Luminosity upgrade of the LHC (HL-LHC) will deliver a total of 3000–4000 fb-1 of proton-proton collisions at a center-of-mass energy of 13–14 TeV. To cope with these challenging environmental conditions, the strip tracker of the CMS experiment will be upgraded using modules with two closely-spaced silicon sensors to provide information to include tracking in the Level-1 trigger selection. This paper describes the performance, in a test beam experiment, of the first prototype module based on the final version of the CMS Binary Chip front-end ASIC before and after the module was irradiated with neutrons. Results demonstrate that the prototype module satisfies the requirements, providing efficient tracking information, after being irradiated with a total fluence comparable to the one expected through the lifetime of the experiment

Beam test performance of a prototype module with Short Strip ASICs for the CMS HL-LHC tracker upgrade

The Short Strip ASIC (SSA) is one of the four front-end chips designed for the upgrade of the CMS Outer Tracker for the High Luminosity LHC. Together with the Macro-Pixel ASIC (MPA) it will instrument modules containing a strip and a macro-pixel sensor stacked on top of each other. The SSA provides both full readout of the strip hit information when triggered, and, together with the MPA, correlated clusters called stubs from the two sensors for use by the CMS Level-1 (L1) trigger system. Results from the first prototype module consisting of a sensor and two SSA chips are presented. The prototype module has been characterized at the Fermilab Test Beam Facility using a 120 GeV proton beam

Evaluation of planar silicon pixel sensors with the RD53A readout chip for the Phase-2 Upgrade of the CMS Inner Tracker

The Large Hadron Collider at CERN will undergo an upgrade in order to increase its luminosity to 7.5 × 10³⁴ cm⁻²s⁻¹. The increased luminosity during this High-Luminosity running phase, starting around 2029, means a higher rate of proton-proton interactions, hence a larger ionizing dose and particle fluence for the detectors. The current tracking system of the CMS experiment will be fully replaced in order to cope with the new operating conditions. Prototype planar pixel sensors for the CMS Inner Tracker with square 50 μm × 50 μm and rectangular 100 μm × 25 μm pixels read out by the RD53A chip were characterized in the lab and at the DESY-II testbeam facility in order to identify designs that meet the requirements of CMS during the High-Luminosity running phase. A spatial resolution of approximately 3.4 μm (2 μm) is obtained using the modules with 50 μm × 50 μm (100 μm × 25 μm) pixels at the optimal angle of incidence before irradiation. After irradiation to a 1 MeV neutron equivalent fluence of Φeq = 5.3 × 10¹⁵ cm⁻², a resolution of 9.4 μm is achieved at a bias voltage of 800 V using a module with 50 μm × 50 μm pixel size. All modules retain a hit efficiency in excess of 99% after irradiation to fluences up to 2.1 × 10¹⁶ cm⁻². Further studies of the electrical properties of the modules, especially crosstalk, are also presented in this paper

Computer-Aided Optimization of Combined Anti-Retroviral Therapy for HIV: New Drugs, New Drug Targets and Drug Resistance

BACKGROUND: Resistance to antiretroviral drugs is a complex and evolving area with relevant implications in the treatment of human immunodeficiency virus (HIV) infection. Several rules, algorithms and full-fledged computer programs have been developed to assist the HIV specialist in the choice of the best patient-tailored therapy. METHODS: Experts' rules and statistical/machine learning algorithms for interpreting HIV drug resistance, along with their program implementations, were retrieved from PubMed and other on-line resources to be critically reviewed in terms of technical approach, performance, usability, update, and evolution (i.e. inclusion of novel drugs or expansion to other viral agents). RESULTS: Several drug resistance prediction algorithms for the nucleotide/nucleoside/non-nucleoside reverse transcriptase, protease and integrase inhibitors as well as coreceptor antagonists are currently available, routinely used, and have been validated thoroughly in independent studies. Computer tools that combine single-drug genotypic/phenotypic resistance interpretation and optimize combination antiretroviral therapy have been also developed and implemented as web applications. Most of the systems have been updated timely to incorporate new drugs and few have recently been expanded to meet a similar need in the Hepatitis C area. Prototype systems aiming at predicting virological response from both virus and patient indicators have been recently developed but they are not yet being routinely used. CONCLUSION: Computing HIV genotype to predict drug susceptibility in vitro or response to combination antiretroviral therapy in vivo is a continuous and productive research field, translating into successful treatment decision support tools, an essential component of the management of HIV patients

Transmission of HIV in sexual networks in sub-Saharan Africa and Europe

We are reviewing the literature regarding sexual networks and HIV transmission in sub-Saharan Africa and Europe. On Likoma Island in Malawi, a sexual network was reconstructed using a sociometric survey in which individuals named their sexual partners. The sexual network identified one giant component including half of all sexually active individuals. More than 25% of respondents were linked through independent chains of sexual relations. HIV was more common in the sparser regions of the network due to over-representation of groups with higher HIV prevalence. A study from KwaZulu-Natal in South-Africa collected egocentric data about sexual partners and found that new infections in women in a particular area was associated with the number of life-time partners in men. Data about sexual networks and HIV transmission are not reported in Europe. It is, however, found that the annual number of sexual partners follows a scale-free network. Phylogenetic studies that determine genetic relatedness between HIV isolates obtained from infected individuals, found that patients in the early stages of infections explain a high number of new infections. In conclusion, the limited information that is available suggest that sexual networks play a role in spread of HIV. Obtaining more information about sexual networks can be of benefit for modeling studies on HIV transmission and prevention. © 2013 EDP Sciences and Springer.DynaNets acknowledges the financial support of the Future and Emerging Technologies (FET) program within the Seventh Framework Program for Research of the European Commission, under FET-Open grant number:233847.Peer Reviewe