Smartphone swabs as an emerging tool for toxicology testing: a proof-of-concept study in a nightclub.

OBJECTIVES: Smartphones have become everyday objects on which the accumulation of fingerprints is significant. In addition, a large proportion of the population regularly uses a smartphone, especially younger people. The objective of this study was to evaluate smartphones as a new matrix for toxico-epidemiology. METHODS: This study was conducted during two separate events (techno and trance) at an electronic music nightclub in Grenoble, France. Data on reported drug use and whether drugs were snorted directly from the surface of the smartphone were collected using an anonymous questionnaire completed voluntarily by drug users. Then, a dry swab was rubbed for 20 s on all sides of the smartphone. The extract was analyzed by liquid chromatography coupled to tandem mass spectrometry on a Xevo TQ-XS system (Waters). RESULTS: In total, 122 swabs from 122 drug users were collected. The three main drugs identified were MDMA (n=83), cocaine (n=59), and THC (n=51). Based on declarative data, sensitivity ranged from 73 to 97.2 % and specificity from 71.8 to 88.1 % for MDMA, cocaine, and THC. Other substances were identified such as cocaine adulterants, ketamine, amphetamine, LSD, methamphetamine, CBD, DMT, heroin, mescaline, and several NPS. Numerous medications were also identified, such as antidepressants, anxiolytics, hypnotics, and painkillers. Different use patterns were identified between the two events. CONCLUSIONS: This proof-of-concept study on 122 subjects shows that smartphone swab analysis could provide a useful and complementary tool for drug testing, especially for harm-reduction programs and toxico-epidemiolgy studies, with acceptable test performance, despite declarative data.

PD1 and TIM3 Expression is Associated with Very Early Hepatocellular Carcinoma Recurrence After Percutaneous Thermal Ablation.

Purpose: Percutaneous thermal ablation (PTA) is a cornerstone in the management of early-stage hepatocellular carcinoma (HCC). However, intrahepatic distant recurrence (IDR) occurs in the majority of patients after PTA. The aim of this study was to evaluate the immune signature associated with very early IDR. Patients and Methods: Thirty-one patients (26 men, 5 women; mean age:72.4 ± 8.6; age range:57-86 years) who underwent PTA for HCC were included in this study. After PTA for HCC, patients were followed and later divided into two groups: a "very early recurrence" group in case of IDR within 12 months after PTA, and a "prolonged recurrence-free" group in case of no recurrence before 12 months of follow-up. Freshly harvested intratumoral and nontumoral liver tissues and peripheral blood were obtained before PTA and explored by multiparametric flow cytometry. Results: The frequency of PD1+CD4+ T cells was higher in the early recurrence group than in the prolonged recurrence-free group in the peripheral blood (24.3%, IQR: 22.3-36.5 vs 14.0%, IQR: 11.5-16.4, p<0.0001), in the nontumoral liver (37.9%, IQR: 36.0-50.0 vs 22.5%, IQR: 18.0-29.9, p=0.0004), and in the tumor (37.6%, IQR: 32.3-39.3 vs 24.0%, IQR: 20.0-30.3, p=0.0137). Similarly, the frequency of TIM+CD8+ T cells was higher in the very early recurrence group in the peripheral blood (p=0.0021), non-tumoral liver (p=0.0012), and tumor (p=0.0239). Conclusion: The expression of immune checkpoint molecules, such as PD1 and TIM3 on T cells identified HCC patients at risk of very early IDR after PTA who would likely benefit from adjuvant immunotherapy. Thus, our study contributes to a better understanding of the potential association of PTA with adjuvant immunotherapies.

The Christmas adverse event syndrome: An analysis of the WHO pharmacovigilance database.

OBJECTIVES: We hypothesized that during the Christmas season the safety profile and the toxicity of some drugs may be exacerbated. We therefore assessed and characterized changes in drug safety profiles over the Christmas period. DESIGN: We performed a retrospective longitudinal analysis of adverse events reported in the World Health Organization (WHO) pharmacovigilance database between April 1st 2017 to March 31th 2023. SETTING: We extracted cases reported by the 5 main contributors' countries of the WHO pharmacovigilance database with a Christmas tradition: USA, France, Germany, Italy and UK. PARTICIPANTS: We analyzed 4,999,459 individual case safety reports from USA (n=3,498,961), France (n=419,018), Germany (n=398,763), Italy (n=251,641) and UK (n=431,076), reported between April 1st 2017 to March 31th 2023. MAIN OUTCOME MEASURES: Monthly reports of adverse events were analyzed. Time trend, seasonal effect a Christmas effect (December-January) were explored. RESULTS: We found 91 adverse events significantly more frequently reported during the Christmas period, independently after controlling for winter effect and general tendency. The main type of adverse events were psychiatric disorders, infections and skin and subcutaneous disorders. The highest numbers of attributable cases to Christmas were found for drug dependence, emotional distress, and drug withdrawal syndrome. The most involved drugs were oxycodone in psychiatric disorders (n=47,527), docetaxel in skin disorders (n=9440) and social circumstances (n=1940), olmesartan in gastrointestinal disorders (n=1263), fentanyl in cardiac disorders (n=929), adalimumab in infections (n=11,316) and immune system disorders (n=3781), and collagenase clostridium histolyticum in reproductive system disorders (n=318). CONCLUSIONS: Our study shows that a range of drugs adverse events are more frequently reported at Christmas compared to other periods of the year, notably psychiatric disorders, infections, and skin disorders.

Trends in Fatal Poisoning Among Drug Users in France From 2011 to 2021: An Analysis of the DRAMES Register.

Importance: The DRAMES (Décès en Relation avec l'Abus de Médicaments Et de Substances) register is a database of drug-related deaths with the aim of identifying the psychoactive substances associated with and estimating the trends in these deaths. Our novel approach is based on the collection of data on all deaths for which toxicology experts have performed analyses. Objective: To describe drug-related deaths in France and report trends over an 11-year period. Design, Setting, and Participants: This case series used a national register to assess 4460 drug-related deaths that occurred from 2011 to 2021 in France. Data analyses were performed from January 1, 2012, to December 31, 2022. Main Outcomes and Measures: Demographic characteristics; medical and substance abuse history; forensic autopsy findings; and toxicology reports. Results: Among the 4460 deceased individuals (mean [SD] age, 37.8 [10.5] years), the mortality rate was highest among men (sex ratio, 4.4:1). Of the deaths involving a single or predominant drug, the legal substitution product, methadone, was the leading cause of death during the entire study period, ahead of heroin-44.7% and 35.9% for methadone vs 15.8% and 21.8% for heroin in 2011 and 2021, respectively. Between 2011 and 2021, most of the drug-related deaths shifted from licit to illicit drugs, and statistically significant variations were found for buprenorphine, cocaine, heroin, methadone, and other licit opioids. Deaths related to polydrug use increased from 23.2% in 2011 to 30.6% in 2021. In this context, opioids remained associated with most deaths, with at least 1 opioid being involved in approximately 9 of 10 cases (85.9%) in 2021. However, the main trend was the dramatic increase in drug combinations with cocaine, from less than one-third of cases in 2011 (30.8%) to more than half in 2021 (57.8%). Conclusions and Relevance: This case series assessment of 4460 drug-related deaths found that opioids used alone or in combination were the main contributor to drug-related deaths, despite having a lower prevalence than other drugs. This finding is similar to that of other countries; however, in France licit methadone was the leading cause of opioid-related deaths (ahead of heroin) during the study period. Deaths associated with use of cannabis, new psychoactive substances, and stimulants (including amphetamine-type stimulants and cocaine, especially in combination) have increased and should be closely monitored.

Assessment of digital perfusion as a surrogate outcome in Raynaud's phenomenon clinical trials.

OBJECTIVES: Measurement of digital perfusion, sometimes coupled with a cold challenge, has been widely used as an objective outcome in trials evaluating drug therapies in Raynaud's Phenomenon (RP), in addition to patient-reported outcomes or to establish the proof-of-concept in preliminary studies. However, whether digital perfusion is a valid surrogate for clinical outcomes in RP trials has never been explored. The principal aim of this study was to evaluate the potential surrogacy of digital perfusion, by combining individual-level and trial-level data. METHODS: We used individual data from a series of n-of-1 trials, and trial data from a network meta-analysis. We estimated individual-level surrogacy through coefficients of determination between digital perfusion and clinical outcomes (R2ind). We further calculated the coefficients of determination between treatment effect on the clinical outcomes and on digital perfusion, at the individual level (R2TEInd) and at the trial level (R2trial), using non-weighted linear regression, with their 95% CI calculated through bootstrapping. RESULTS: Results from 33 patients and 24 trials were included in the final analysis. At the individual level, there was no correlation between digital perfusion and clinical outcomes at rest and in response to various cooling tests (the highest R2ind was 0.03 [-0.07; 0.09]), and R2TEinf was also very low 0.07 [0; 0.29]. At the trial level, the highest value of R2trial was 0.1 [0; 0.477]. CONCLUSIONS: Digital perfusion, at rest or in response to a cold challenge, and whatever the method used, does not fulfill the criteria of a valid surrogate for existing patient-reported outcomes in RP trials.

The Occurrence Birth-Death Process for Combined-Evidence Analysis in Macroevolution and Epidemiology.

Phylodynamic models generally aim at jointly inferring phylogenetic relationships, model parameters, and more recently, the number of lineages through time, based on molecular sequence data. In the fields of epidemiology and macroevolution, these models can be used to estimate, respectively, the past number of infected individuals (prevalence) or the past number of species (paleodiversity) through time. Recent years have seen the development of "total-evidence" analyses, which combine molecular and morphological data from extant and past sampled individuals in a unified Bayesian inference framework. Even sampled individuals characterized only by their sampling time, that is, lacking morphological and molecular data, which we call occurrences, provide invaluable information to estimate the past number of lineages. Here, we present new methodological developments around the fossilized birth-death process enabling us to (i) incorporate occurrence data in the likelihood function; (ii) consider piecewise-constant birth, death, and sampling rates; and (iii) estimate the past number of lineages, with or without knowledge of the underlying tree. We implement our method in the RevBayes software environment, enabling its use along with a large set of models of molecular and morphological evolution, and validate the inference workflow using simulations under a wide range of conditions. We finally illustrate our new implementation using two empirical data sets stemming from the fields of epidemiology and macroevolution. In epidemiology, we infer the prevalence of the coronavirus disease 2019 outbreak on the Diamond Princess ship, by taking into account jointly the case count record (occurrences) along with viral sequences for a fraction of infected individuals. In macroevolution, we infer the diversity trajectory of cetaceans using molecular and morphological data from extant taxa, morphological data from fossils, as well as numerous fossil occurrences. The joint modeling of occurrences and trees holds the promise to further bridge the gap between traditional epidemiology and pathogen genomics, as well as paleontology and molecular phylogenetics. [Birth-death model; epidemiology; fossils; macroevolution; occurrences; phylogenetics; skyline.].

Quantification of the spread of SARS-CoV-2 variant B.1.1.7 in Switzerland.

BACKGROUND: In December 2020, the United Kingdom (UK) reported a SARS-CoV-2 Variant of Concern (VoC) which is now named B.1.1.7. Based on initial data from the UK and later data from other countries, this variant was estimated to have a transmission fitness advantage of around 40-80 % (Volz et al., 2021; Leung et al., 2021; Davies et al., 2021). AIM: This study aims to estimate the transmission fitness advantage and the effective reproductive number of B.1.1.7 through time based on data from Switzerland. METHODS: We generated whole genome sequences from 11.8 % of all confirmed SARS-CoV-2 cases in Switzerland between 14 December 2020 and 11 March 2021. Based on these data, we determine the daily frequency of the B.1.1.7 variant and quantify the variant's transmission fitness advantage on a national and a regional scale. RESULTS: We estimate B.1.1.7 had a transmission fitness advantage of 43-52 % compared to the other variants circulating in Switzerland during the study period. Further, we estimate B.1.1.7 had a reproductive number above 1 from 01 January 2021 until the end of the study period, compared to below 1 for the other variants. Specifically, we estimate the reproductive number for B.1.1.7 was 1.24 [1.07-1.41] from 01 January until 17 January 2021 and 1.18 [1.06-1.30] from 18 January until 01 March 2021 based on the whole genome sequencing data. From 10 March to 16 March 2021, once B.1.1.7 was dominant, we estimate the reproductive number was 1.14 [1.00-1.26] based on all confirmed cases. For reference, Switzerland applied more non-pharmaceutical interventions to combat SARS-CoV-2 on 18 January 2021 and lifted some measures again on 01 March 2021. CONCLUSION: The observed increase in B.1.1.7 frequency in Switzerland during the study period is as expected based on observations in the UK. In absolute numbers, B.1.1.7 increased exponentially with an estimated doubling time of around 2-3.5 weeks. To monitor the ongoing spread of B.1.1.7, our plots are available online.

The probability distribution of the ancestral population size conditioned on the reconstructed phylogenetic tree with occurrence data.

We consider a homogeneous birth-death process with three different sampling schemes. First, individuals can be sampled through time and included in a reconstructed phylogenetic tree. Second, they can be sampled through time and only recorded as a point 'occurrence' along a timeline. Third, extant individuals can be sampled and included in the reconstructed phylogenetic tree with a fixed probability. We further consider that sampled individuals can be removed or not from the process, upon sampling, with fixed probability. We derive the probability distribution of the population size at any time in the past conditional on the joint observation of a reconstructed phylogenetic tree and a record of occurrences not included in the tree. We also provide an algorithm to simulate ancestral population size trajectories given the observation of a reconstructed phylogenetic tree and occurrences. This distribution can be readily used to draw inferences about the ancestral population size in the field of epidemiology and macroevolution. In epidemiology, these results will allow data from epidemiological case count studies to be used in conjunction with molecular sequencing data (yielding reconstructed phylogenetic trees) to coherently estimate prevalence through time. In macroevolution, it will foster the joint examination of the fossil record and extant taxa to reconstruct past biodiversity.

Model-Based Inference of Punctuated Molecular Evolution.

In standard models of molecular evolution, DNA sequences evolve through asynchronous substitutions according to Poisson processes with a constant rate (called the molecular clock) or a rate that can vary (relaxed clock). However, DNA sequences can also undergo episodes of fast divergence that will appear as synchronous substitutions affecting several sites simultaneously at the macroevolutionary timescale. Here, we develop a model, which we call the Relaxed Clock with Spikes model, combining basal, clock-like molecular substitutions with episodes of fast divergence called spikes arising at speciation events. Given a multiple sequence alignment and its time-calibrated species phylogeny, our model is able to detect speciation events (including hidden ones) cooccurring with spike events and to estimate the probability and amplitude of these spikes on the phylogeny. We identify the conditions under which spikes can be distinguished from the natural variance of the clock-like component of molecular substitutions and from variations of the clock. We apply the method to genes underlying snake venom proteins and identify several spikes at gene-specific locations in the phylogeny. This work should pave the way for analyses relying on whole genomes to inform on modes of species diversification.

The probability distribution of the reconstructed phylogenetic tree with occurrence data.

We study the problem of computing the probability distribution of phylogenetic trees that commonly arise in areas ranging from epidemiology to macroevolution. We focus on homogeneous birth death trees with incomplete sampling and consider observations from three distinct sampling schemes. First, individuals can be sampled and removed, through time, and included in the tree. Second, they can be occurrences which are sampled and removed through time and not included in the tree. Third, extant individuals can be sampled and included in the tree. The outcome of the process is thus composed of the reconstructed phylogenetic tree spanning all individuals sampled and included in the tree, and a timeline of occurrence events which are not placed along the tree. We derive a formula for computing the joint probability density of this outcome, which can readily be used to perform maximum likelihood or Bayesian estimation of the parameters of the model. In the context of epidemiology, our probability density enables the estimation of transmission rates through a joint analysis of epidemiological case count data and phylogenetic trees reconstructed from pathogen sequences. Within macroevolution, our equations form the basis for incorporating fossil occurrences from paleontological databases together with extant species phylogenies for estimating speciation and extinction rates. This work provides the theoretical framework for bridging not only the gap between phylogenetics and epidemiology, but also that between phylogenetics and paleontology.

The Species Problem from the Modeler's Point of View.

How to define a partition of individuals into species is a long-standing question called the species problem in systematics. Here, we focus on this problem in the thought experiment where individuals reproduce clonally and both the differentiation process and the population genealogies are explicitly known. We specify three desirable properties of species partitions: (A) Heterotypy between species, (B) Homotypy within species and (M) Genealogical monophyly of each species. We then ask: How and when is it possible to delineate species in a way satisfying these properties? We point out that the three desirable properties cannot in general be satisfied simultaneously, but that any two of them can. We mathematically prove the existence of the finest partition satisfying (A) and (M) and the coarsest partition satisfying (B) and (M). For each of them, we propose a simple algorithm to build the associated phylogeny out of the genealogy. The ways we propose to phrase the species problem shed new light on the interaction between the genealogical and phylogenetic scales in modeling work. The two definitions centered on the monophyly property can readily be used at a higher taxonomic level as well, e.g., to cluster species into monophyletic genera.

A Unifying Comparative Phylogenetic Framework Including Traits Coevolving Across Interacting Lineages.

Models of phenotypic evolution fit to phylogenetic comparative data are widely used to make inferences regarding the tempo and mode of trait evolution. A wide range of models is already available for this type of analysis, and the field is still under active development. One of the most needed development concerns models that better account for the effect of within- and between-clade interspecific interactions on trait evolution, which can result from processes as diverse as competition, predation, parasitism, or mutualism. Here, we begin by developing a very general comparative phylogenetic framework for (multi)-trait evolution that can be applied to both ultrametric and nonultrametric trees. This framework not only encapsulates many previous models of continuous univariate and multivariate phenotypic evolution, but also paves the way for the consideration of a much broader series of models in which lineages coevolve, meaning that trait changes in one lineage are influenced by the value of traits in other, interacting lineages. Next, we provide a standard way for deriving the probabilistic distribution of traits at tip branches under our framework. We show that a multivariate normal distribution remains the expected distribution for a broad class of models accounting for interspecific interactions. Our derivations allow us to fit various models efficiently, and in particular greatly reduce the computation time needed to fit the recently proposed phenotype matching model. Finally, we illustrate the utility of our framework by developing a toy model for mutualistic coevolution. Our framework should foster a new era in the study of coevolution from comparative data.

Estimating the Effect of Competition on Trait Evolution Using Maximum Likelihood Inference.

Many classical ecological and evolutionary theoretical frameworks posit that competition between species is an important selective force. For example, in adaptive radiations, resource competition between evolving lineages plays a role in driving phenotypic diversification and exploration of novel ecological space. Nevertheless, current models of trait evolution fit to phylogenies and comparative data sets are not designed to incorporate the effect of competition. The most advanced models in this direction are diversity-dependent models where evolutionary rates depend on lineage diversity. However, these models still treat changes in traits in one branch as independent of the value of traits on other branches, thus ignoring the effect of species similarity on trait evolution. Here, we consider a model where the evolutionary dynamics of traits involved in interspecific interactions are influenced by species similarity in trait values and where we can specify which lineages are in sympatry. We develop a maximum likelihood based approach to fit this model to combined phylogenetic and phenotypic data. Using simulations, we demonstrate that the approach accurately estimates the simulated parameter values across a broad range of parameter space. Additionally, we develop tools for specifying the biogeographic context in which trait evolution occurs. In order to compare models, we also apply these biogeographic methods to specify which lineages interact sympatrically for two diversity-dependent models. Finally, we fit these various models to morphological data from a classical adaptive radiation (Greater Antillean Anolis lizards). We show that models that account for competition and geography perform better than other models. The matching competition model is an important new tool for studying the influence of interspecific interactions, in particular competition, on phenotypic evolution. More generally, it constitutes a step toward a better integration of interspecific interactions in many ecological and evolutionary processes.

Phylogenies support out-of-equilibrium models of biodiversity.

There is a long tradition in ecology of studying models of biodiversity at equilibrium. These models, including the influential Neutral Theory of Biodiversity, have been successful at predicting major macroecological patterns, such as species abundance distributions. But they have failed to predict macroevolutionary patterns, such as those captured in phylogenetic trees. Here, we develop a model of biodiversity in which all individuals have identical demographic rates, metacommunity size is allowed to vary stochastically according to population dynamics, and speciation arises naturally from the accumulation of point mutations. We show that this model generates phylogenies matching those observed in nature if the metacommunity is out of equilibrium. We develop a likelihood inference framework that allows fitting our model to empirical phylogenies, and apply this framework to various mammalian families. Our results corroborate the hypothesis that biodiversity dynamics are out of equilibrium.