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Neurocritical patients frequently exhibit abnormalities in cerebral hemodynamics (CH) and/or intracranial compliance (ICC), all of which significantly impact their clinical outcomes. Transcranial Doppler (TCD) and the cranial micro-deformation sensor (B4C) are valuable techniques for assessing CH and ICC, respectively. However, there is a scarcity of data regarding the predictive value of these techniques in determining patient outcomes. We prospectively included neurocritical patients undergoing intracranial pressure (ICP) monitoring within the first 5 days of hospital admission for TCD and B4C assessments. Comprehensive clinical data were collected alongside parameters obtained from TCD (including the estimated ICP [eICP] and estimated cerebral perfusion pressure [eCPP]) and B4C (measured as the P2/P1 ratio). These parameters were evaluated individually as well as in combination. The short-term outcomes (STO) of interest were the therapy intensity levels (TIL) for ICP management recommended by the Seattle International Brain Injury Consensus Conference, as TIL 0 (STO 1), TIL 1–3 (STO 2) and death (STO 3), at the seventh day after last data collection. The dataset was randomly separated in test and training samples, area under the curve (AUC) was used to represent the noninvasive techniques ability on the STO prediction and association with ICP. A total of 98 patients were included, with 67% having experienced severe traumatic brain injury and 15% subarachnoid hemorrhage, whilst the remaining patients had ischemic or hemorrhagic stroke. ICP, P2/P1, and eCPP demonstrated the highest ability to predict early mortality (p = 0.02, p = 0.02, and p = 0.006, respectively). P2/P1 was the only parameter significant for the prediction of STO 1 (p = 0.03). Combining B4C and TCD parameters, the highest AUC was 0.85 to predict death (STO 3), using P2/P1 + eCPP, whereas AUC was 0.72 to identify ICP > 20 mmHg using P2/P1 + eICP. The combined noninvasive neuromonitoring approach u, SCOPUS: ar.j, info:eu-repo/semantics/published

In a context where technological advancements have enabled increased availability of genetic data through high-throughput sequencing technologies, the complexity of genetic diseases has become increasingly apparent. Oligogenic diseases, characterised by a combination of genetic variants in two or more genes, have emerged as a crucial research area, challenging the traditional model of "one genotype, one phenotype". Thus, understanding the underlying mechanisms and genetic interactions of oligogenic diseases has become a major priority in biomedical research. This context underlines the importance of developing dedicated tools to study these complex diseases.Our first major contribution, OLIDA, is an innovative database designed to collect data on variant combinations responsible for these diseases, filling significant gaps in the current knowledge, focused up until now on the digenic diseases. This resource, accessible via a web platform, adheres to FAIR principles and represents a significant advancement over its predecessor, DIDA, in terms of data curation and quality assessment.Furthermore, to support the biocuration of oligogenic diseases, we used active learning to construct DUVEL, a biomedical corpus focused on digenic variant combinations. To achieve this, we first investigated how to optimise these methods across numerous biomedical relation extraction datasets and developed a web-based platform, ALAMBIC, for text annotation using active learning. Our results and the quality of the corpus obtained demonstrate the effectiveness of active learning methods in biomedical relation annotation tasks.By establishing a curation pipeline for oligogenic diseases, as well as a standards for integrating active learning methods into biocuration, our work represents a significant advancement in the field of biomedical natural language processing and the understanding of oligogenic diseases., Option Informatique du Doctorat en Sciences, info:eu-repo/semantics/nonPublished

Background and Objectives: The extensive collection of electrocardiogram (ECG) recordings stored in paper format has provided opportunities for numerous digitization studies. However, the traditional 10 s 12-lead ECG printout typically splits the ECG signals into four asynchronous sections of 3 leads and 2.5 s each. Since each lead corresponds to different time instants, developing a synchronization method becomes necessary for applications such as vectorcardiogram (VCG) reconstruction. Methods: A beat-level synchronization method has been developed and validated using a dataset of 21,674 signals. This method effectively addresses synchronization distortions caused by RR interval variations and preserves the time lags between R peaks across different leads for each beat. Results: The results demonstrate that the proposed method successfully synchronizes the ECG, allowing a VCG reconstruction with an average Pearson Correlation Coefficient of 0.9815±0.0426. The Normalized Root Mean Squared Error (NRMSE) and Mean Absolute Error (MAE) values for the reconstructed VCG are 0.0248±0.0214 mV and 0.0133±0.0123 mV, respectively. These metrics indicate the reliability of the VCG reconstruction achieved by means of the proposed synchronization method. Conclusions: The synchronization method has demonstrated its robustness and high performance compared to existing techniques in the field. Its effectiveness has been observed across a wide variety of signals, showcasing its applicability in real clinical environments. Moreover, its ability to handle a large number of signals makes it suitable for various applications, including retrospective studies and the development of machine learning methods., SCOPUS: ar.j, info:eu-repo/semantics/published

We perform an exhaustive follow-up analysis of a subsolar-mass (SSM) gravitational wave (GW) candidate reported by Phukon et al. from the second observing run of Advanced LIGO. This candidate has a reported signal-to-noise ratio (SNR) of 8.6 and false alarm rate of 0.41 yr which are too low to claim a clear gravitational-wave origin. When improving on the search by using more accurate waveforms, extending the frequency range from 45 Hz down to 20 Hz, and removing a prominent blip glitch, we find that the posterior distribution of the network SNR lies mostly below the search value, with the 90% confidence interval being 7.94−1.05+0.70. Assuming that the origin of the signal is a compact binary coalescence (CBC), the secondary component is m2=0.76−0.14+0.50M⊙, with m2<1M⊙ at 84% confidence level, suggesting an unexpectedly light neutron star or a black hole of primordial or exotic origin. The primary mass would be m1=4.71−2.18+1.57M⊙, likely in the hypothesized lower mass gap and the luminosity distance is measured to be DL=124−48+82Mpc. We then probe the CBC origin hypothesis by performing the signal coherence tests, obtaining a log Bayes factor of 4.96±0.13 for the coherent vs. incoherent hypothesis. We demonstrate the capability of performing a parameter estimation follow-up on real data for an SSM candidate with moderate SNR. The improved sensitivity of O4 and subsequent LIGO-Virgo-KAGRA observing runs could make it possible to observe similar signals, if present, with a higher SNR and more precise measurement of the parameters of the binary., SCOPUS: ar.j, info:eu-repo/semantics/published

Over the past years, progress made in next-generation sequencing technologies and bioinformatics have sparked a surge in association studies. Especially, genome-wide association studies (GWASs) have demonstrated their effectiveness in identifying disease associations with common genetic variants. Yet, rare variants can contribute to additional disease risk or trait heterogeneity. Because GWASs are underpowered for detecting association with such variants, numerous statistical methods have been recently proposed. Aggregation tests collapse multiple rare variants within a genetic region (e.g. gene, gene set, genomic loci) to test for association. An increasing number of studies using such methods successfully identified trait-associated rare variants and led to a better understanding of the underlying disease mechanism. In this review, we compare existing aggregation tests, their statistical features and scope of application, splitting them into the five classical classes: burden, adaptive burden, variance-component, omnibus and other. Finally, we describe some limitations of current aggregation tests, highlighting potential direction for further investigations., SCOPUS: re.j, info:eu-repo/semantics/published

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Automatic biomedical relation extraction (bioRE) is an essential task in biomedical research in order to generate high-quality labelled data that can be used for the development of innovative predictive methods. However, building such fully labelled, high quality bioRE data sets of adequate size for the training of state-of-the-art relation extraction models is hindered by an annotation bottleneck due to limitations on time and expertise of researchers and curators. We show here how Active Learning (AL) plays an important role in resolving this issue and positively improve bioRE tasks, effectively overcoming the labelling limits inherent to a data set. Six different AL strategies are benchmarked on seven bioRE data sets, using PubMedBERT as the base model, evaluating their area under the learning curve (AULC) as well as intermediate results measurements. The results demonstrate that uncertainty-based strategies, such as Least-Confident or Margin Sampling, are statistically performing better in terms of F1-score, accuracy and precision, than other types of AL strategies. However, in terms of recall, a diversity-based strategy, called Core-set, outperforms all strategies. AL strategies are shown to reduce the annotation need (in order to reach a performance at par with training on all data), from 6% to 38%, depending on the data set; with Margin Sampling and Least-Confident Sampling strategies moreover obtaining the best AULCs compared to the Random Sampling baseline. We show through the experiments the importance of using AL methods to reduce the amount of labelling needed to construct high-quality data sets leading to optimal performance of deep learning models. The code and data sets to reproduce all the results presented in the article are available at https://github.com/oligogenic/Deep_active_learning_bioRE ., SCOPUS: ar.j, info:eu-repo/semantics/published

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though standards and guidelines for the interpretation of variants identified in genes that cause Mendelian disorders have been developed, this is not the case for more complex genetic models including variant combinations in multiple genes. 318 research articles reporting oligogenic cases were extracted from PubMed. A transparent curation protocol was developed assigning a confidence score to each oligogenic case based on the amount of pathogenic evidence at the genetic and functional level, based on the relevant oligogenic information collected by independent curators (i) from the articles and (ii) from public relevant databases. The collection and assessment of this data led to the creation of OLIDA, the Oligogenic Diseases Database. OLIDA contains information on 1229 oligogenic cases linked to 177 different genetic diseases. Each instance is linked with a confidence score depicting the quality of the associated genetic and functional pathogenic evidence. The curation process revealed that the majority of papers do not provide proper genetic evidence refuting a monogenic model, and rarely perform functional experiments for confirmation. Our recommendations stress the necessity of fulfilling both conditions. The use of multiple extended pedigrees showing a clear segregation of the reported variants, control cohorts of a suitable size, as well as functional experiments showing the synergistic effect of the involved variants are essential for this purpose. With our work we reveal the recurrent issues on the reporting of oligogenic cases and stress the need for the development of standards in the field. As the number of papers identifying oligogenic causes to disease is increasing rapidly, initiating this discussion is imperative., info:eu-repo/semantics/published

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Recent years have seen significant progress in medical genetics, with enhanced access to sequenced genomic data and the evolution of computational methods for analysing genetic variation. These advances have improved our understanding of Mendelian 'one gene - one phenotype' genetic models, yet the transition to the study of oligogenic diseases, where a small number of genes are involved, remains a considerable challenge.With the rising reports of clinical oligogenic cases, resources and machine learning tools have been developed to leverage this data. Nevertheless, despite their high accuracy, these predictors can be viewed as “black-boxes” due to their limited interpretability. This complexity poses challenges for medical professionals in validating these predictions and restricts their understanding of potential underlying disease mechanisms. Our research aimed to tackle these limitations using structured background knowledge to provide additional context to predictions.Our first key contribution is a web platform designed to allow geneticists to filter and analyse patient-level variant data with predictors designed for oligogenic diseases. This platform allows in-depth exploration of predictions, including feature contribution analyses, gene pathogenicity networks and gene module mapping to biological networks.Taking a step further towards enhanced explainability, we extended our research to the design of a whitebox machine learning approach that could provide both predictions and meaningful explanations based on background integrated knowledge. Our second contribution is a biological knowledge graph that blends data from known oligogenic diseases with multi-scale biological networks, emphasising the importance of diverse information for understanding oligogenic complexity.Our third contribution builds on this, presenting an interpretable model based on path semantics between gene pairs. This model, capable of learning and applying rules for oligogenic interactio, Doctorat en Sciences, info:eu-repo/semantics/nonPublished

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Analysis of intracranial pressure waveforms (ICPW) provides information on intracranial compliance. We aimed to assess the correlation between noninvasive ICPW (NICPW) and invasively measured intracranial pressure (ICP) and to assess the NICPW prognostic value in this population. In this cohort, acute brain-injured (ABI) patients were included within 5 days from admission in six Intensive Care Units. Mean ICP (mICP) values and the P2/P1 ratio derived from NICPW were analyzed and correlated with outcome, which was defined as: (a) early death (ED); survivors on spontaneous breathing (SB) or survivors on mechanical ventilation (MV) at 7 days from inclusion. Intracranial hypertension (IHT) was defined by ICP > 20 mmHg. A total of 72 patients were included (mean age 39, 68% TBI). mICP and P2/P1 values were significantly correlated (r = 0.49, p < 0.001). P2/P1 ratio was significantly higher in patients with IHT and had an area under the receiving operator curve (AUROC) to predict IHT of 0.88 (95% CI 0.78–0.98). mICP and P2/P1 ratio was also significantly higher for ED group (n = 10) than the other groups. The AUROC of P2/P1 to predict ED was 0.71 [95% CI 0.53–0.87], and the threshold P2/P1 > 1.2 showed a sensitivity of 60% [95% CI 31–83%] and a specificity of 69% [95% CI 57–79%]. Similar results were observed when decompressive craniectomy patients were excluded. In this study, P2/P1 derived from noninvasive ICPW assessment was well correlated with IHT. This information seems to be as associated with ABI patients outcomes as ICP. Trial registration: NCT03144219, Registered 01 May 2017 Retrospectively registered, https://www.clinicaltrials.gov/ct2/show/NCT03144219., SCOPUS: ar.j, info:eu-repo/semantics/published

Primordial black holes (PBHs) with a wide mass distribution imprinted by the thermal history of the Universe, which naturally produces a high peak at the solar mass scale, could explain the gravitational-wave events seen by LIGO/Virgo and up to the totality of the dark matter. We show that compared to monochromatic or log-normal mass functions, the gravitational wave backgrounds (GWBs) from early PBH binaries and from late binaries in clusters are strongly enhanced at low frequency and could even explain the NANOGrav observations. This enhancement comes from binaries with very low mass ratios, involving solar-mass and intermediate-mass PBHs at low frequency, solar-mass and subsolar-mass at high frequency. LISA could distinguish the various models, while in the frequency band of ground-based detectors, we find that the GWB from early binaries is just below the current LIGO/Virgo limits and above the astrophysical background, if they also explain black hole mergers. The GWB from binaries in clusters is less boosted but has a different spectral index than for neutron stars, astrophysical black holes or early PBH binaries. It is detectable with Einstein Telescope or even with the LIGO/Virgo design sensitivity., SCOPUS: ar.j, info:eu-repo/semantics/published

Background/Aims:Reports of oligogenic cases (i.e. individuals whose disease phenotype can only be explained by the co-occurrence of multiple variants in several genes) have been rapidly increasing, in an effort to close the gap of missing genetic diagnoses. Nevertheless, the quality of this data had never been properly assessed, especially as standards and guidelines for such cases are currently missing. This work, aimed to collect all reported oligogenic cases in one database, OLIDA, assess the quality of the reported information and provide, for the first time, recommendations for their proper reporting. Methods:318 research articles reporting oligogenic cases were extracted from PubMed. Independent curators collected the relevant oligogenic information (i) from the articles and (ii) from public relevant databases. With this data, a transparent curation protocol was developed assigning a confidence score to each oligogenic case based on the amount of pathogenic evidence at the genetic and functional level. The collection and assessment of this data led to the creation of OLIDA, the Oligogenic Diseases Database. Results:OLIDA contains information on oligogenic cases linked to 177 different genetic diseases. Each instance is linked with a confidence score depicting the quality of the associated genetic and functional pathogenic evidence. The data revealed that the majority of papers do not provide proper genetic evidence excluding a monogenic model, while this evidence is rarely coupled with functional experiments for confirmation. Our recommendations stress the necessity of fulfilling both conditions. The use of multiple extended pedigrees showing a clear segregation of the reported variants, control cohorts of a suitable size, as well as functional experiments showing the synergistic effect of the involved variants are essential for this purpose. Conclusion:With our work we reveal the recurrent issues on the reporting of oligogenic cases and stress the need for th, info:eu-repo/semantics/published

Venous access procedures are painful and feared by children and their parents. Virtual reality has become increasingly prominent and has been shown to be effective in various procedures. The aim of this meta-analysis was to examine virtual reality’s effect on pain and fear in children from 4 to 12 in the context of vascular access. From the 20th to the 26th December 2020, we searched Sciencedirect, Springerlink, CENTRAL, Pubmed and PMC. Studies using virtual reality versus a control in vascular access for children were included in a meta-analysis to evaluate the effect of virtual reality regarding pain as a primary and fear/anxiety as a secondary endpoint during the procedures. The Jadad scale and Delphi List were used to assess study quality. 20,894 citations were identified, 9 met our inclusion criteria. One publication was conducted in two different situations and was thus considered as 2 studies. Compared to standard of care, virtual reality significantly reduced pain (10 studies, 930 participants: standardized mean difference [SMD] 2.54, 95%CI 0.14–4.93, p = 0.038), and fear/anxiety (6 studies, 648 participants: SMD 0.89, 95%Cl 0.16–1.63, p = 0.017). For both parameters, we found significant heterogeneity between studies. This is the first meta-analysis to look at the use virtual reality in young children undergoing vascular access procedures, providing weak to moderate evidence for its use. Although large effect sizes provide evidence for a positive effect of virtual reality in reducing pain and fear, there is significant heterogeneity between studies. More research with larger groups and age stratification is required., SCOPUS: ar.j, info:eu-repo/semantics/published

Herbal food supplements are commonly used and can be an important part of patient self-care. Like all other bio-active and therapeutic products, they have a benefit/risk balance. These products are not without adverse effects and potentially interact with other therapies. Educating patients and providing information for health professionals about the risk of herb–drug interactions is key. One of the purposes of the biomedical literature is to inform prescribers. Scientific literature accessible on databases such as PubMed is dense and careful reading is time consuming. We propose a reading aid tool named “HDI highlighter” to help readers to find key information in clinical studies and case reports describing herb–drug interactions. It uses natural language processing algorithms (artificial intelligence) with a pharmaceutical focus. Semantic relation extraction for herb–drug interactions from the biomedical literature are overexpressed using keywords. We have tested it to review 120 published articles over the last 10 years. In these articles, we have shown that case reports often involved long-term or semi-long-term treatments such as cancer or human immunodeficiency virus therapies, antiepileptic drugs, or central nervous system drugs. Similarly, these classes of drugs are more extensively targeted by clinical studies. Herb–drug interactions described in case reports are identified in medicinal, recreational, and alimentary uses. They also usually lack a rigorous description of the herb(s) involved. Typically, clinical studies provide a complete description of protocols and dosages, with a few exceptions explained by patients’ needs. Clinical studies on herbs are nevertheless conducted on a limited number of patients. All these limitations make the interpretation of herb–drug interactions complicated, but the HDI highlighter provides a quick overview of the herb–drug interaction literature., SCOPUS: re.j, info:eu-repo/semantics/published

Improving the understanding of the oligogenic nature of diseases requires access to high-quality, well-curated Findable, Accessible, Interoperable, Reusable (FAIR) data. Although first steps were taken with the development of the Digenic Diseases Database, leading to novel computational advancements to assist the field, these were also linked with a number of limitations, for instance, the ad hoc curation protocol and the inclusion of only digenic cases. The OLIgogenic diseases DAtabase (OLIDA) presents a novel, transparent and rigorous curation protocol, introducing a confidence scoring mechanism for the published oligogenic literature. The application of this protocol on the oligogenic literature generated a new repository containing 916 oligogenic variant combinations linked to 159 distinct diseases. Information extracted from the scientific literature is supplemented with current knowledge support obtained from public databases. Each entry is an oligogenic combination linked to a disease, labelled with a confidence score based on the level of genetic and functional evidence that supports its involvement in this disease. These scores allow users to assess the relevance and proof of pathogenicity of each oligogenic combination in the database, constituting markers for reporting improvements on disease-causing oligogenic variant combinations. OLIDA follows the FAIR principles, providing detailed documentation, easy data access through its application programming interface and website, use of unique identifiers and links to existing ontologies. Database URL: https://olida.ibsquare.be, SCOPUS: ar.j, info:eu-repo/semantics/published

The study of genetic variation associated with disease has shown the inadequacy of the “one gene - one disease phenotype” paradigm for many cases, leading to the notion of a conceptual continuum starting from monogenic disorders to oligogenic and polygenic diseases. An important step towards understanding non-Mendelian disorders was the creation of the Digenic Diseases Database (DIDA), collecting curated scientific information on digenic variant combinations involved in digenic diseases. Different machine learning methods aiming to tackle the cause of digenic diseases have successfully used DIDA as a benchmark dataset and have been in turn used in scientific studies analysing novel oligogenic cases. While this marked a new age of predictive tools and underlined the importance of DIDA, these advances also demonstrated the need to expand further in the genetic disease continuum, beyond digenic diseases, in a continuous and more careful manner. Moreover, a structured re-evaluation of the inclusion of oligogenic combinations in such a database and their pathogenic link to diseases has become essential, in order to aid researchers in using high-quality and properly curated information when assessing their medical cases. We present OLIDA (https://olida.ibsquare.be/), the Oligogenic Diseases Database, which reinvents DIDA, containing newly and fully re-curated data and freely accessible information on oligogenic variant combinations, i.e. combinations of variants in multiple genes involved in an oligogenic disease, published in the scientific literature until February 2020. The database includes 916 oligogenic variant combinations, 192 of them involving more than two genes, linked to 159 genetic diseases. OLIDA provides, for the first time in the field, a structured protocol for the evaluation of the pathogenicity of each oligogenic combination, based on the genetic and functional evidence supporting it, paying special attention to their joint variant effect. The evidence, info:eu-repo/semantics/published

No reference point-of-care, web-based medical compendium is available in general practice in France. We have then conducted the experimentation of EBMPracticeNet, a Belgian website of guidelines translated and adapted from the Finnish EBM Guidelines. We collected data from three sources: (i) the website logbook; (ii) a search-specific assessment questionnaire; (iii) a global assessment questionnaire. A cumulative number of 262 (62.8%) physicians performed at least one search on the website and clicked on average 5.9 times per month. Physicians globally got an accurate answer (74.2%). They found the information provided by the website reliable (92.2%) and useful for practice (78.6%). They perceived the website ergonomics as good. The main reported barriers were the time and effort required to find an accurate answer and the uneven relevance of the information retrieved. Improvements should focus on guidelines indexing and their adaptation to the French context, and training physicians to search medical databases., SCOPUS: ar.j, info:eu-repo/semantics/published

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A universal mechanism may be responsible for several unresolved cosmic conundra. The sudden drop in the pressure of relativistic matter at W±∕Z0 decoupling, the quark–hadron transition and e+e− annihilation enhances the probability of primordial black hole (PBH) formation in the early Universe. Assuming the amplitude of the primordial curvature fluctuations is approximately scale-invariant, this implies a multi-modal PBH mass spectrum with peaks at 10−6, 1, 30, and 106M⊙. This suggests a unified PBH scenario which naturally explains the dark matter and recent microlensing observations, the LIGO/Virgo black hole mergers, the correlations in the cosmic infrared and X-ray backgrounds, and the origin of the supermassive black holes in galactic nuclei at high redshift. A distinctive prediction of our model is that LIGO/Virgo should observe black hole mergers in the mass gaps between 2 and 5M⊙ (where no stellar remnants are expected) and above 65M⊙ (where pair-instability supernovae occur) and low-mass-ratios in between. Therefore the recent detection of events GW190425, GW190814 and GW190521 with these features is striking confirmation of our prediction and may indicate a primordial origin for the black holes. In this case, the exponential sensitivity of the PBH abundance to the equation of state would offer a unique probe of the QCD phase transition. The detection of PBHs would also offer a novel way to probe the existence of new particles or phase transitions with energy between 1MeV and 1010GeV., SCOPUS: ar.j, info:eu-repo/semantics/published

Objective: Determine the effectiveness of digital mental health interventions for individuals with a concomitant chronic disease. Design: We conducted a rapid review of systematic reviews. Two reviewers independently conducted study selection and risk of bias evaluation. A standardised extraction form was used. Data are reported narratively. Interventions: We included systematic reviews of digital health interventions aiming to prevent, detect or manage mental health problems in individuals with a pre-existing chronic disease, including chronic mental health illnesses, published in 2010 or after. Main outcome measure: Reports on mental health outcomes (eg, anxiety symptoms and depression symptoms). Results: We included 35 reviews, totalling 702 primary studies with a total sample of 50 692 participants. We structured the results in four population clusters: (1) chronic diseases, (2) cancer, (3) mental health and (4) children and youth. For populations presenting a chronic disease or cancer, health provider directed digital interventions (eg, web-based consultation, internet cognitive–behavioural therapy) are effective and safe. Further analyses are required in order to provide stronger recommendations regarding relevance for specific population (such as children and youth). Web-based interventions and email were the modes of administration that had the most reports of improvement. Virtual reality, smartphone applications and patient portal had limited reports of improvement. Conclusions: Digital technologies could be used to prevent and manage mental health problems in people living with chronic conditions, with consideration for the age group and type of technology used.

Gravitational waves can probe the existence of planetary-mass primordial black holes. Considering a mass range of [10−7−10−2]M⊙, inspiraling primordial black holes could emit either continuous gravitational waves, quasi-monochromatic signals that last for many years, or transient continuous waves, signals whose frequency evolution follows a power law and last for O(hours-months). We show that primordial black hole binaries in our galaxy may produce detectable gravitational waves for different mass functions and formation mechanisms. In order to detect these inspirals, we adapt methods originally designed to search for gravitational waves from asymmetrically rotating neutron stars. The first method, the Frequency-Hough, exploits the continuous, quasi-monochromatic nature of inspiraling black holes that are sufficiently light and far apart such that their orbital frequencies can be approximated as linear with a small spin-up. The second method, the Generalized Frequency-Hough, drops the assumption of linearity and allows the signal frequency to follow a power-law evolution. We explore the parameter space to which each method is sensitive, derive a theoretical sensitivity estimate, determine optimal search parameters and calculate the computational cost of all-sky and directed searches. We forecast limits on the abundance of primordial black holes within our galaxy, showing that we can constrain the fraction of dark matter that primordial black holes compose, fPBH, to be fPBH≲1 for chirp masses between [4×10−5−10−3]M⊙ for current detectors. For the Einstein Telescope, we expect the constraints to improve to fPBH≲10−2 for chirp masses between [10−4−10−3]M⊙., SCOPUS: ar.j, info:eu-repo/semantics/published

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Succinylcholine is still used in clinical practice. Its duration is highly variable and neuromuscular monitoring is recommended to assess its recovery. Unfortunately, depolarizing neuromuscular block cannot be evaluated by train-of-four (TOF) as no fade occurs. Only single twitch (ST) allows to monitor depolarizing block. The TOFScan monitor proposes a modified TOF ratio (T4/Tref) not referring to fade within a TOF series. We hypothesized that the T4/Tref allows to assess recovery from succinylcholine. This observational study aims to compare recovery from depolarizing block simultaneously assessed with T4/Tref and ST. After Ethics Committee approval and written informed consent 45 patients receiving a single dose of succinylcholine were included. ST recovery, measured with TOF-Watch SX, and T4/Tref recovery, measured by TOFScan, were assessed simultaneously. Difference between measurements was compared using Mann–Whitney U test and overall concordance with the intraclass correlation coefficient (ICC). The ICC was 0.85 [95% CI 0.82–0.87]. The first T4/Tref ratio ≥ 90% was 97% [95% CI 93–101%] the corresponding ST value was 91% [95% CI 87–97%]. The first T4/Tref ratio ≥ 100% was 103% [95% CI 102–107%], the corresponding ST value was 104% [95% CI 100–106%]; median [95% confidence interval of the median]. Time to recovery to 90% and 100% of baseline did not differ between ST or T4/Tref. It was10.0 [8.0–13.0] min vs 10.0 [8.0–12.0] min, P = 0.307 and 10.0 [9.0–15.0] min vs 10.0 [8.0–15.0] min, P = 0.546; respectively; median [IQR]. Agreement between both parameters was excellent. The T4/Tref can be used accurately to measure recovery from depolarizing neuromuscular block., SCOPUS: ar.j, info:eu-repo/semantics/published

Non-invasive BMI applications are increasingly used in different contexts ranging from industrial, clinical and gaming. After having tested the difference between a classical EEG recorder with electroconductive gel (ANT system) and the MUSE EEG headband, we studied the BCI performances of the later during the control of a small robot. We demonstrated that the participants were able to successfully control the robot using an online brain-computer interface based on the signal power in different frequency bands (delta, theta and alpha) characterizing the eyes-opened and relaxed eyes-closed states. Additionally, we performed a correlation analysis which demonstrated that the BCI commands were more related to a delta or theta power decrease for the determination of the classifier output probability and to the alpha power increase for the speed control of the robot., info:eu-repo/semantics/published

Purpose: Neoadjuvant chemotherapy (NAC) aims to minimize the tumor size before surgery. Predicting response to NAC could reduce toxicity and delays to effective intervention. Computational analysis of dynamic contrast-enhanced magnetic resonance images (DCE-MRI) through deep convolution neural network (CNN) has shown a significant performance to distinguish responders and no responder’s patients. This study intends to present a new deep learning (DL) model predicting the breast cancer response to NAC based on multiple MRI inputs. Methods: A cohort of 723 axial slices extracted from 42 breast cancer patients who underwent NAC therapy was used to train and validate the developed DL model. This dataset was provided by our collaborator institute of radiology in Brussels. Fourteen external cases were used to validate the best obtained model to predict pCR based on pre- and post-chemotherapy DCE-MRI. The model performance was assessed by area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and feature map visualization. Results: The developed multi-inputs deep learning architecture was able to predict the pCR to NAC treatment in the validation dataset with an AUC of 0.91 using combined pre- and post-NAC images. The visual results showed that the most important extracted features from non-pCR tumors are in the peripheral region. The proposed method was more productive than the previous ones. Conclusion: Even with a limited training dataset size, the proposed and developed CNN model using DCE-MR images acquired before and after the first chemotherapy was able to classify pCR and non-pCR patients with substantial accuracy. This model could be used hereafter in clinical analysis after its evaluation based on more extra data., SCOPUS: ar.j, DecretOANoAutActif, info:eu-repo/semantics/published

The emergence of statistical and predictive methods able to analyse genomic data has revolutionised the field of medical genetics, allowing the identification of disease-causing gene variants (i.e. mutations) for several human genetic diseases. Although these approaches have greatly improved our understanding of Mendelian «one gene – one phenotype» genetic models, studying diseases related to more intricate models that involve causative variants in several genes (i.e. oligogenic diseases) still remains a challenge, either due to the lack of sufficient methodologies and disease-specific cohorts to study or the phenotypic complexity associated with such diseases. This situation makes it difficult to not only understand the genetic mechanisms of the disease, but to also offer proper counseling and support to the patient. Until recently, no specialized predictive methods existed to directly predict causative variant combinations for oligogenic diseases. However, with the advent of data on variant combinations in gene pairs (i.e. bilocus variant combinations) leading to disease, collected at the Digenic Diseases Database (DIDA), we hypothesized that the transition from single to variant combination pathogenicity predictors is now possible.To investigate this hypothesis, we organised our research on two main routes. At first, we developed an interpretable variant combination pathogenicity predictor, called VarCoPP, for gene pairs. For this goal, we trained multiple Random Forest algorithms on pathogenic bilocus variant combinations from DIDA against neutral data from the 1000 Genomes Project and investigated the contribution of the incorporated variant, gene and gene pair features to the prediction outcome. In the second part, we explored the usefulness of different gene pair burden scores based on this novel predictive method, in discovering oligogenic signatures in neurodevelopmental diseases, which involve a spectrum of monogenic to polygenic cases. We performed a prel, Doctorat en Sciences, info:eu-repo/semantics/nonPublished

Background: Currently, physicians are limited in their ability to provide an accurate prognosis for COVID-19 positive patients. Existing scoring systems have been ineffective for identifying patient decompensation. Machine learning (ML) may offer an alternative strategy. A prospectively validated method to predict the need for ventilation in COVID-19 patients is essential to help triage patients, allocate resources, and prevent emergency intubations and their associated risks. Methods: In a multicenter clinical trial, we evaluated the performance of a machine learning algorithm for prediction of invasive mechanical ventilation of COVID-19 patients within 24 h of an initial encounter. We enrolled patients with a COVID-19 diagnosis who were admitted to five United States health systems between March 24 and May 4, 2020. Results: 197 patients were enrolled in the REspirAtory Decompensation and model for the triage of covid-19 patients: a prospective studY (READY) clinical trial. The algorithm had a higher diagnostic odds ratio (DOR, 12.58) for predicting ventilation than a comparator early warning system, the Modified Early Warning Score (MEWS). The algorithm also achieved significantly higher sensitivity (0.90) than MEWS, which achieved a sensitivity of 0.78, while maintaining a higher specificity (p < 0.05). Conclusions: In the first clinical trial of a machine learning algorithm for ventilation needs among COVID-19 patients, the algorithm demonstrated accurate prediction of the need for mechanical ventilation within 24 h. This algorithm may help care teams effectively triage patients and allocate resources. Further, the algorithm is capable of accurately identifying 16% more patients than a widely used scoring system while minimizing false positive results., SCOPUS: ar.j, info:eu-repo/semantics/published