PLoS One

MOTIVATION: The recent revolution in new sequencing technologies, as a part of the continuous process of adopting new innovative protocols has strongly impacted the interpretation of relations between phenotype and genotype. Thus, understanding the resulting gene sets has become a bottleneck that needs to be addressed. Automatic methods have been proposed to facilitate the interpretation of gene sets. While statistical functional enrichment analyses are currently well known, they tend to focus on well-known genes and to ignore new information from less-studied genes. To address such issues, applying semantic similarity measures is logical if the knowledge source used to annotate the gene sets is hierarchically structured. In this work, we propose a new method for analyzing the impact of different semantic similarity measures on gene set annotations. RESULTS: We evaluated the impact of each measure by taking into consideration the two following features that correspond to relevant criteria for a "good" synthetic gene set annotation: (i) the number of annotation terms has to be drastically reduced and the representative terms must be retained while annotating the gene set, and (ii) the number of genes described by the selected terms should be as large as possible. Thus, we analyzed nine semantic similarity measures to identify the best possible compromise between both features while maintaining a sufficient level of details. Using Gene Ontology to annotate the gene sets, we obtained better results with node-based measures that use the terms' characteristics than with measures based on edges that link the terms. The annotation of the gene sets achieved with the node-based measures did not exhibit major differences regardless of the characteristics of terms used

Running in circles: practical limitations for real-life application of data fission and data thinning in post-clustering differential analysis

Post-clustering inference in scRNA-seq analysis presents significant challenges in controlling Type I error during Differential Expression Analysis. Data fission, a promising approach, aims to split the data into two new independent parts, but relies on strong parametric assumptions of non-mixture distributions, which are violated in clustered data. We show that applying data fission to these mixtures requires knowledge of the clustering structure to accurately estimate component-specific scale parameters. These estimates are critical for ensuring decomposition and independence. We theoretically quantify the direct impact of the bias in estimating this scales parameters on the inflation of the Type I error rate, caused by a deviation from the independence. Since component structures are unknown in practice, we propose a heteroscedastic model with non-parametric estimators for individual scale parameters. This model uses proximity between observations to capture the effect of the underlying mixture on data dispersion. While this approach works well when clusters are well-separated, it introduces bias when separation is weak, highlighting the difficulty of applying data fission in real-world scenarios with unknown degrees of separation.MultiScale AI for SingleCell-Based Precision MedicineUniversity of Bordeaux Graduate School in Digital Public HealthEuropean HIV Vaccine Alliance (EHVA): a EU platform for the discovery and evaluation of novel prophylactic and therapeutic vaccine candidate

Parameter estimation in nonlinear mixed effect models based on ordinary differential equations: An optimal control approach

We present a parameter estimation method for nonlinear mixed effect models based on ordinary differential equations (NLME-ODEs). The method presented here aims at regularizing the estimation problem in presence of model misspecifications, practical identifiability issues and unknown initial conditions. For doing so, we define our estimator as the minimizer of a cost function which incorporates a possible gap between the assumed model at the population level and the specific individual dynamic. The cost function computation leads to formulate and solve optimal control problems at the subject level. This control theory approach allows to bypass the need to know or estimate initial conditions for each subject and it regularizes the estimation problem in presence of poorly identifiable parameters. Comparing to maximum likelihood, we show on simulation examples that our method improves estimation accuracy in possibly partially observed systems with unknown initial conditions or poorly identifiable parameters with or without model error. We conclude this work with a real application on antibody concentration data after vaccination against Ebola virus coming from phase 1 trials. We use the estimated model discrepancy at the subject level to analyze the presence of model misspecification.European Union’s Horizon 2020 research and innovation programm

NPJ Vaccines

The persistence of the long-term immune response induced by the heterologous Ad26.ZEBOV, MVA-BN-Filo two-dose vaccination regimen against Ebola has been investigated in several clinical trials. Longitudinal data on IgG-binding antibody concentrations were analyzed from 487 participants enrolled in six Phase I and Phase II clinical trials conducted by the EBOVAC1 and EBOVAC2 consortia. A model based on ordinary differential equations describing the dynamics of antibodies and short- and long-lived antibody-secreting cells (ASCs) was used to model the humoral response from 7 days after the second vaccination to a follow-up period of 2 years. Using a population-based approach, we first assessed the robustness of the model, which was originally estimated based on Phase I data, against all data. Then we assessed the longevity of the humoral response and identified factors that influence these dynamics. We estimated a half-life of the long-lived ASC of at least 15 years and found an influence of geographic region, sex, and age on the humoral response dynamics, with longer antibody persistence in Europeans and women and higher production of antibodies in younger participants.Initiative for the creation of a Vaccine Research InstituteHorizon 2020 research and innovation programm

HIV Testing and Diagnosis Rates in Kiev, Ukraine: April 2013-March 2014

Data from Ukraine on risk factors for HIV acquisition are limited. We describe the characteristics of individuals testing for HIV in the main testing centres of the Ukrainian capital Kiev, including HIV risk factors, testing rates, and positivity rates. As part of a larger study to estimate HIV incidence within Kiev City, we included questions on possible risk factors for HIV acquisition and testing history to existing systems in 4 infectious disease clinics. Data were provided by the person requesting an HIV test using a handheld electronic tablet. All persons (≥16 yrs) presenting for an HIV test April 2013-March 2014 were included. Rates per 100,000 were calculated using region-specific denominators for Kiev. During the study period 6370 individuals tested for HIV, equivalent to a testing rate of 293.2 per 100,000. Of these, 467 (7.8%) were HIV-positive, with the highest proportion positive among 31-35 year olds (11.2%), males (9.4%), people who inject drugs (PWID) (17.9%) and men who have sex with men (MSM) (24.1%). Using published population size estimates of MSM, diagnosis rates for MSM ranged from 490.6 to 1548.3/100,000. A higher proportion of heterosexual women compared to heterosexual men reported contact with PWID, (16% vs. 4.7%) suggesting a bridging in risk between PWID and their sexual partners. Collection of HIV risk factor information in Kiev, essential for the purposes of developing effective HIV prevention and response tools, is feasible. The high percentage of MSM among those testing positive for HIV, may indicate a significant level of undisclosed sex between men in national figures

BMC Nephrol

An amendment to this paper has been published and can be accessed via the original article

A machine learning approach for predicting suicidal thoughts and behaviours among college students

Suicidal thoughts and behaviours are prevalent among college students. Yet little is known about screening tools to identify students at higher risk. We aimed to develop a risk algorithm to identify the main predictors of suicidal thoughts and behaviours among college students within one-year of baseline assessment. We used data collected in 2013-2019 from the French i-Share cohort, a longitudinal population-based study including 5066 volunteer students. To predict suicidal thoughts and behaviours at follow-up, we used random forests models with 70 potential predictors measured at baseline, including sociodemographic and familial characteristics, mental health and substance use. Model performance was measured using the area under the receiver operating curve (AUC), sensitivity, and positive predictive value. At follow-up, 17.4% of girls and 16.8% of boys reported suicidal thoughts and behaviours. The models achieved good predictive performance: AUC, 0.8; sensitivity, 79% for girls, 81% for boys; and positive predictive value, 40% for girls and 36% for boys. Among the 70 potential predictors, four showed the highest predictive power: 12-month suicidal thoughts, trait anxiety, depression symptoms, and self-esteem. We identified a parsimonious set of mental health indicators that accurately predicted one-year suicidal thoughts and behaviours in a community sample of college students.Program Initiative d’Excellenc

Reservoir Computing for Short High-Dimensional Time Series: an Application to SARS-CoV-2 Hospitalization Forecast

In this work, we aimed at forecasting the number of SARS-CoV-2 hospitalized patients at 14 days to help anticipate the bed requirements of a large scale hospital using public data and electronic health records data. Previous attempts ledto mitigated performance in this high-dimension setting; we introduce a novel approach to time series forecasting by providing an alternative to conventional methods to deal with high number of potential features of interest (409 predictors). We integrate Reservoir Computing (RC) with feature selection using a genetic algorithm (GA) to gatheroptimal non-linear combinations of inputs to improve prediction in sample-efficient context. We illustrate that the RC-GA combination exhibitsexcellent performance in forecasting SARS-CoV-2 hospitalizations. This approach outperformed the use of RC alone and other conventional methods: LSTM, Transformers, Elastic-Net, XGBoost. Notably, this work marks the pioneering use of RC (along with GA) in the realm of short and high-dimensional time series, positioning it as a competitive and innovative approach in comparison to standard methods

Temporal trends of population viral suppression in the context of Universal Test and Treat: the ANRS 12249 TasP trial in rural South Africa

Introduction: The universal test-and-treat (UTT) strategy aims to maximize population viral suppression (PVS), that is, the proportion of all people living with HIV (PLHIV) on antiretroviral treatment (ART) and virally suppressed, with the goal of reducing HIV transmission at the population level. This article explores the extent to which temporal changes in PVS explain the observed lack of association between universal treatment and cumulative HIV incidence seen in the ANRS 12249 TasP trial conducted in rural South Africa. Methods: The TasP cluster-randomized trial (2012 to 2016) implemented six-monthly repeat home-based HIV counselling and testing (RHBCT) and referral of PLHIV to local HIV clinics in 2 9 11 clusters opened sequentially. ART was initiated according to national guidelines in control clusters and regardless of CD4 count in intervention clusters. We measured residency status, HIV status, and HIV care status for each participant on a daily basis. PVS was computed per cluster among all resident PLHIV (≥16, including those not in care) at cluster opening and daily thereafter. We used a mixed linear model to explore time patterns in PVS, adjusting for sociodemographic changes at the cluster level. Results: 8563 PLHIV were followed. During the course of the trial, PVS increased significantly in both arms (23.5% to 46.2% in intervention, +22.8, p < 0.001; 26.0% to 44.6% in control, +18.6, p < 0.001). That increase was similar in both arms (p = 0.514). In the final adjusted model, PVS increase was most associated with increased RHBCT and the implementation of local trial clinics (measured by time since cluster opening). Contextual changes (measured by calendar time) also contributed slightly. The effect of universal ART (trial arm) was positive but limited. Conclusions: PVS was improved significantly but similarly in both trial arms, explaining partly the null effect observed in terms of cumulative HIV incidence between arms. The PVS gains due to changes in ART-initiation guidelines alone are relatively small compared to gains obtained by strategies to maximize testing and linkage to care. The achievement of the 90-90-90 targets will not be met if the operational and implementational challenges limiting access to care and treatment, often context-specific, are not properly addressed. Clinical trial number: NCT01509508 (clinicalTrials.gov)/DOH-27-0512-3974 (South African National Clinical Trials Register)

Br J Haematol

Immune thrombocytopenia (ITP) is defined by a low platelet count that can trigger potentially life-threatening haemorrhages. Three-quarters of adult patients exhibit persistent or chronic disease and require second-line treatments. Among these, rituximab, an anti-CD20 antibody, has yielded valuable results, with global responses in 60% of patients at 6 months and complete responses in 30% at 5 years. Factors predictive of response to ITP therapy would help physicians choose optimal treatments. We retrospectively analysed clinical courses, biological markers and blood lymphocyte subset numbers of 72 patients on rituximab to treat persistent/chronic ITP followed-up in our department between 2007 and 2021, divided into three groups according to the platelet count at 6 months: complete, partial or no response. Among all studied parameters, a low number of CD3 CD16 CD56 circulating NK cells was associated with the complete response to rituximab. We also found that, after rituximab therapy, complete responders exhibited increased NK and decreased activated CD8 T cell percentages. These results emphasize that the role played by NK cells in ITP remains incompletely known but that factors predictive of response to rituximab can be easily derived using blood lymphocyte subset data