Biodegradable polymers are increasingly promoted as sustainable alternatives to conventional plastics, yet their ecotoxicological safety in marine environments remains critically underexplored. Here, we provide a comparative assessment of the acute toxicity of both biodegradable and conventional microplastics — including particles and leachates — on the embryo-larval development of the Pacific oyster Magallana gigas, a sentinel species of major ecological and economic importance in coastal ecosystems. Three biodegradable polymers (polylactic acid (PLA) and two polyhydroxyalcanoates (PHA): poly-3-hydroxybutyrate-co-4-hydroxybutyrate (P3HB4HB) and polyhydroxybutyrate-co-hydroxyvalerate (PHBV)) were tested as well as conventional polystyrene (PS) as reference material. Dose-response exposures to particles (0-100 mg/L) or leachates (0-72 g/L) were performed, and two leaching durations were tested (24h and 14 days). Contrasting effects of PHA were observed: while PHBV leachates had no measurable impact on embryo-larval development, exposure to PHBV particles slightly enhanced D-larval yield and exposure to P3HB4HB particles led to a complete failure of normal D-larvae formation at the highest concentration. Exposure to PLA particles elicited higher toxicity profiles than conventional PS particles and their leachates toxicity increased with the leaching duration. Two trace metals (Fe and Zn) and 22 additives (mainly plasticizers) were detected in the plastic materials and could partly explained the observed toxicity. The use of surfactant and model particles are discussed in regards with the environmental implications of this work. Collectively, our findings challenge the assumption that biodegradable polymers are inherently safer than conventional plastics and provide critical data to inform the environmental risk assessment of next-generation plastic materials.

High-throughput sequencing (HTS) can detect known and novel adventitious viruses in biological materials that might be missed by the conventional in vitro and in vivo assays. We compared HTS with the conventional assays using the same sample preparation of CHO cell harvest spiked with human respiratory syncytial virus (RSV) and mammalian orthoreovirus (Reo1). The viruses were selected with the expectation that one would produce a positive result in each test. The study results indicated both RSV and Reo1 were detected by HTS and in vitro assays, albeit with inter-assay and intra-lab differences in sensitivity of virus detection, and only Reo1 was detected by the in vivo assays. The results support using HTS to replace the in vivo assays. Additionally, based on capabilities of HTS for non-targeted virus detection, it may supplement or replace the in vitro assays. The study further highlighted that the analytical sensitivity of HTS can be improved.

The vibriosis of the European abalone, Haliotis tuberculata, is characterized by the rapidity of the infection by the pathogen Vibrio harveyi ORM4, leading to death of animals only after two days. The lethality of the pathogen is linked to the production of the type III secretion system (T3SS) and to genes regulated by quorum sensing (QS). The aim of this study was to investigate the colonization of the European abalone by both virulent and avirulent V. harveyi strains, as well as the involvement of T3SS and QS during infection. Our results emphasize the importance of gills for the bacterial establishment as the bacterial concentration of the avirulent V. harveyi strain significantly decreased from 189.3 +/- 98.6 CFU/mg to 0.8 +/- 0.5 CFU/mg between 24 and 48 h post-infection (hpi). In opposition, the pathogen V. harveyi ORM4 was able to maintain itself on the gills, with a concentration of 461.9 +/- CFU/mg at 48 hpi, which was allowed by the production of T3SS and a functional QS. Following the infection cycle of V. harveyi ORM4 inside H. tuberculata, we also demonstrated that QS is essential for the ability of V. harveyi ORM4 to colonize the abalone hemolymph and to maintain in it. In response to the presence of V. harveyi, an increase in reactive oxygen species production was recorded, while the phagocytosis activity remained unchanged. We also highlighted the involvement of both QS and T3SS to escape the immune system activity, and that an overproduction of T3SS induced hemocyte mortality. This study provides the evidence that both T3SS and QS are essential for the establishment of V. harveyi ORM4 inside the European abalone.

Accurate detection and prevalence estimation of behavioral health conditions, such as opioid use disorder (OUD), are crucial for identifying at-risk individuals, determining treatment needs, monitoring prevention and intervention efforts, and recruiting treatment-naive participants for clinical trials. The availability of extensive health data, combined with advancements in machine learning (ML) frameworks, has enabled researchers to employ various ML techniques to predict or identify OUD within patient health data. Ideally, we could directly estimate the prevalence, or the proportion of a population with a condition over time. However, underdiagnosis and undercoding of conditions in patient health records make it challenging to determine the true prevalence of these conditions and to identify at-risk patients with less severe conditions who are more likely to be missed. Consequently, patients without diagnoses may comprise positive and negative examples for a given condition. Treating all undiagnosed (uncoded) patients as negative when applying ML methods can introduce bias into models, affecting their predictive power. To address this issue, we employed Positive Unlabeled Learning Selected Not At Random (PULSNAR), a Positive and Unlabeled (PU) learning technique, to estimate the probability of a given patient having OUD during a time window and the overall population prevalence of OUD. In a sample of 3,342,044 commercially insured US patients with at least one opioid prescription filled, PULSNAR estimated that 5.08% of patients have a cumulative prevalence of OUD over a 2-5 a observation period, compared to the 1.35% with a recorded OUD diagnosis, with 73.5% of cases not diagnosed/coded. The prevalence estimates provided by PULSNAR are consistent with those reported in other studies.

The safety of all biological products includes demonstrating the absence of adventitious viruses by testing various types of samples at different stages of the manufacturing process. Seven laboratories evaluated short-read high-throughput sequencing (HTS) for sensitivity and breadth of adventitious virus detection using viruses with distinct physicochemical and genome properties. These five viruses are currently designated as CBER NGS Virus Reagents and include: Epstein-Barr virus (EBV; or human herpes virus 4), feline leukemia virus (FeLV), respiratory syncytial virus (RSV), mammalian orthoreovirus type 1 (Reo1), and porcine circovirus type 1 (PCV1). To evaluate adventitious virus detection in a biological material with a high production virus titer and low cellular background, the 5 viruses were mixed and different copies of the viral genomes spiked into 1 – 5 × 109 genome copies per mL (GC/mL) of purified adenovirus 5. Independent protocols were used by each laboratory for the entire HTS workflow. All laboratories detected 104 GC/mL of the five viruses by both targeted and non-targeted bioinformatic analyses. Additionally, the limit of detection of squirrel monkey retrovirus and porcine endogenous retrovirus, which pre-existed in EBV and PCV1 virus stocks, respectively, was evaluated. The five laboratories that tested 103 GC/mL, detected all 5 viruses with the targeted analysis, and Reo1 and EBV with the non-targeted analysis. It was noted that some laboratories achieved a better sensitivity for detection of the five viruses ( ≤102 GC/mL). This study presents an approach for HTS validation for viral safety testing of vaccines and other biologics using a panel of reference viruses. The results highlight that optimization of steps in the HTS workflow can improve the limit of detection for adventitious viruses.

TIN-X (Target Importance and Novelty eXplorer) is an interactive visualization tool for illuminating associations between diseases and potential drug targets and is publicly available at newdrugtargets.org. TIN-X uses natural language processing to identify disease and protein mentions within PubMed content using previously published tools for named entity recognition (NER) of gene/protein and disease names. Target data is obtained from the Target Central Resource Database (TCRD). Two important metrics, novelty and importance, are computed from this data and when plotted as log(importance) vs. log(novelty), aid the user in visually exploring the novelty of drug targets and their associated importance to diseases. TIN-X Version 3.0 has been significantly improved with an expanded dataset, modernized architecture including a REST API, and an improved user interface (UI). The dataset has been expanded to include not only PubMed publication titles and abstracts, but also full-text articles when available. This results in approximately 9-fold more target/disease associations compared to previous versions of TIN-X. Additionally, the TIN-X database containing this expanded dataset is now hosted in the cloud via Amazon RDS. Recent enhancements to the UI focuses on making it more intuitive for users to find diseases or drug targets of interest while providing a new, sortable table-view mode to accompany the existing plot-view mode. UI improvements also help the user browse the associated PubMed publications to explore and understand the basis of TIN-X’s predicted association between a specific disease and a target of interest. While implementing these upgrades, computational resources are balanced between the webserver and the user’s web browser to achieve adequate performance while accommodating the expanded dataset. Together, these advances aim to extend the duration that users can benefit from TIN-X while providing both an expanded dataset and new features that researchers can use to better illuminate understudied proteins.

BACKGROUND:We determined the relationships between cytokine expression in sputum and clinical data to characterize and understand chronic obstructive pulmonary disease (COPD) exacerbations in people with COPD. METHODS:We measured 30 cytokines in 936 sputum samples, collected at stable state and exacerbation visits from 99 participants in the Acute Exacerbation and Respiratory InfectionS in COPD (AERIS) study (ClinicalTrials.gov NCT01360398). We determined their longitudinal expression and examined differential expression based on disease status or exacerbation type. RESULTS:Of the cytokines, 17 were suitable for analysis. As for disease states, in exacerbation sputum samples, interleukin (IL) 17A, tumor necrosis factor alpha (TNF-α), IL-1β, and IL-10 were significantly increased compared to stable state sputum samples, but a logistic mixed model could not predict disease state. As for exacerbation types, bacteria-associated exacerbations showed higher expression of IL-17A, TNF-α, IL-1β, and IL-1α. IL-1α, IL-1β, and TNF-α were identified as suitable biomarkers for bacteria-associated exacerbation. Bacteria-associated exacerbations also formed a cluster separate from other exacerbation types in principal component analysis. CONCLUSIONS:Measurement of cytokines in sputum from COPD patients could help identify bacteria-associated exacerbations based on increased concentrations of IL-1α, IL-1β, or TNF-α. This finding may provide a point-of-care assessment to distinguish a bacterial exacerbation of COPD from other exacerbation types.

Chronic obstructive pulmonary disease (COPD) is an etiologically complex disease characterized by acute exacerbations and stable phases. We aimed to identify biological functions modulated in specific COPD conditions, using whole blood samples collected in the AERIS clinical study (NCT01360398). Considered conditions were exacerbation onset, severity of airway obstruction, and presence of respiratory pathogens in sputum samples. With an integrative multi-network gene community detection (MNGCD) approach, we analyzed expression profiles to identify communities of correlated genes. The approach combined different layers of gene interactions for each explored condition/subset of samples: gene expression similarity, protein-protein interactions, transcription factors, and microRNAs validated regulons. Heme metabolism, interferon-alpha, and interferon-gamma pathways were modulated in patients at both exacerbation and stable-state visits, but with the involvement of distinct sets of genes. An important gene community was enriched with G2M checkpoint, E2F targets, and mitotic spindle pathways during exacerbation. Targets of TAL1 regulator and hsa-let-7b - 5p microRNA were modulated with increasing severity of airway obstruction. Bacterial infections with Moraxella catarrhalis and, particularly, Haemophilus influenzae triggered a specific cellular and inflammatory response in acute exacerbations, indicating an active reaction of the host to infections. In conclusion, COPD is a complex multifactorial disease that requires in-depth investigations of its causes and features during its evolution and whole blood transcriptome profiling can contribute to capturing some relevant regulatory mechanisms associated with this disease. In this work, we explored multi-network modeling that integrated diverse layers of regulatory gene networks and enhanced our comprehension of the biological functions implicated in the COPD pathogenesis.

Severe malarial anemia (SMA, Hb < 6.0 g/dL) is a leading cause of childhood morbidity and mortality in holoendemic Plasmodium falciparum transmission zones. This study explored the entire expressed human transcriptome in whole blood from 66 Kenyan children with non-SMA (Hb ≥ 6.0 g/dL, n = 41) and SMA (n = 25), focusing on host immune response networks. RNA-seq analysis revealed 6862 differentially expressed genes, with equally distributed up-and down-regulated genes, indicating a complex host immune response. Deconvolution analyses uncovered leukocytic immune profiles indicative of a diminished antigenic response, reduced immune priming, and polarization toward cellular repair in SMA. Weighted gene co-expression network analysis revealed that immune-regulated processes are central molecular distinctions between non-SMA and SMA. A top dysregulated immune response signaling network in SMA was the HSP60-HSP70-TLR2/4 signaling pathway, indicating altered pathogen recognition, innate immune activation, stress responses, and antigen recognition. Validation with high-throughput gene expression from a separate cohort of Kenyan children (n = 50) with varying severities of malarial anemia (n = 38 non-SMA and n = 12 SMA) confirmed the RNA-seq findings. Proteomic analyses in 35 children with matched transcript and protein abundance (n = 19 non-SMA and n = 16 SMA) confirmed dysregulation in the HSP60-HSP70-TLR2/4 signaling pathway. Additionally, glutamine transporter and glutamine synthetase genes were differentially expressed, indicating altered glutamine metabolism in SMA. This comprehensive analysis underscores complex immune dysregulation and novel pathogenic features in SMA.

By 2040, tire particles (TP) are expected to dominate marine plastic contamination, raising concerns about their effects on marine animals. This study employed a multidisciplinary and multigenerational approach on the Pacific oyster Magallana gigas to investigate the effects of TP and their leachates (LEA). Effects were analyzed at the individual scale, from cellular, molecular, and microbiota changes to reproductive outputs and offspring performance. Microbiota characterization revealed potential dysbiosis in oysters treated with high concentration of both TP and LEA. RNA-seq analyses highlighted the activation of energy metabolism and stress responses in the LEA treatment. Additionally, transcriptional changes in oocytes and the reduction of motile spermatozoa suggested potential effects on gamete quality. Notably, possible oyster resilience was pointed out by the lack of significant ecophysiological modifications in adults and impacts on the growth and reproductive outputs of the offspring. Overall, the implications of the observed oyster resilience under our experimental setting are discussed in relation to available toxicity data and within a comprehensive view of coastal ecosystems, where a higher diversity of plastic/rubber materials and harsher environmental conditions occur.

This study on severe malarial anemia (SMA: Hb < 6.0 g/dL), a leading global cause of childhood morbidity and mortality, analyzed the entire expressed transcriptome in whole blood from children with non-SMA (Hb ≥ 6.0 g/dL, n = 41) and SMA (n = 25). Analyses revealed 3,420 up-regulated and 3,442 down-regulated transcripts, signifying impairments in host inflammasome activation, cell death, innate immune responses, and cellular stress responses in SMA. Immune cell profiling showed a decreased antigenic and immune priming response in children with SMA, favoring polarization toward cellular proliferation and repair. Enrichment analysis further identified altered neutrophil and autophagy-related processes, consistent with neutrophil degranulation and altered ubiquitination and proteasome degradation. Pathway analyses highlighted SMA-related alterations in cellular homeostasis, signaling, response to environmental cues, and cellular and immune stress responses. Validation with a qRT-PCR array showed strong concordance with the sequencing data. These findings identify key molecular themes in SMA pathogenesis, providing potential targets for new malaria therapies.

Positive and unlabeled (PU) learning is a type of semi-supervised binary classification where the machine learning algorithm differentiates between a set of positive instances (labeled) and a set of both positive and negative instances (unlabeled). PU learning has broad applications in settings where confirmed negatives are unavailable or difficult to obtain, and there is value in discovering positives among the unlabeled (e.g., viable drugs among untested compounds). Most PU learning algorithms make the selected completely at random (SCAR) assumption, namely that positives are selected independently of their features. However, in many real-world applications, such as healthcare, positives are not SCAR (e.g., severe cases are more likely to be diagnosed), leading to a poor estimate of the proportion, α, of positives among unlabeled examples and poor model calibration, resulting in an uncertain decision threshold for selecting positives. PU learning algorithms vary; some estimate only the proportion, α, of positives in the unlabeled set, while others calculate the probability that each specific unlabeled instance is positive, and some can do both. We propose two PU learning algorithms to estimate α, calculate calibrated probabilities for PU instances, and improve classification metrics: i) PULSCAR (positive unlabeled learning selected completely at random), and ii) PULSNAR (positive unlabeled learning selected not at random). PULSNAR employs a divide-and-conquer approach to cluster SNAR positives into subtypes and estimates α for each subtype by applying PULSCAR to positives from each cluster and all unlabeled. In our experiments, PULSNAR outperformed state-of-the-art approaches on both synthetic and real-world benchmark datasets.

Human and mouse dorsal root ganglia (hDRG and mDRG) neurons are important tools in understanding the molecular and electrophysiological mechanisms that underlie nociception and drive pain behaviors. One of the simplest differences in firing phenotypes is that neurons are single-firing (exhibit only one action potential) or multi-firing (exhibit 2 or more action potentials). To determine if single- and multi-firing hDRG neurons exhibit differences in intrinsic properties, firing phenotypes, and AP waveform properties, and if these properties could be used to predict multi-firing, we measured 22 electrophysiological properties by whole-cell patch-clamp electrophysiology of 94 hDRG neurons from six male and four female donors. We then analyzed the data using several machine learning models to determine if these properties could be used to predict multi-firing. We used 1,000 iterations of Monte Carlo cross-validation to split the data into different train and test sets and tested the logistic regression, k-nearest neighbors, random forest, support vector classifier, and XGBoost machine learning models. All models tested had a >80% accuracy on average, with support vector classifier, and XGBoost performing the best. We found that several properties correlated with multi-firing hDRG neurons and together could be used to predict multi-firing neurons in hDRG including a long decay time, a low rheobase, and long first spike latency. We also found that the hDRG models were able to predict multi-firing with 90% accuracy in mDRG neurons. Understanding these properties could be beneficial in the elucidation of targets on peripheral sensory neurons related to pain.

Introduction While crucial to ensuring the production of accurate and high-quality data—and to avoid erroneous conclusions—data quality control (QC) in environmental monitoring datasets is still poorly documented. Methods With a focus on annual inter-laboratory comparison (ILC) exercises performed in the context of the French coastal monitoring SOMLIT network, we share here a pragmatic approach to QC, which allows the calculation of systematic and random errors, measurement uncertainty, and individual performance. After an overview of the different QC actions applied to fulfill requirements for quality and competence, we report equipment, accommodation, design of the ILC exercises, and statistical methodology specially adapted to small environmental networks (<20 laboratories) and multivariate datasets. Finally, the expanded uncertainty of measurement for 20 environmental variables routinely measured by SOMLIT from discrete sampling—including Essential Ocean Variables—is provided. Results, Discussion, Conclusion The examination of the temporal variations (2001–2021) in the repeatability, reproducibility, and trueness of the SOMLIT network over time confirms the essential role of ILC exercises as a tool for the continuous improvement of data quality in environmental monitoring datasets.

IntroductionThe immune mechanisms supporting partial protection from reinfection and disease by the respiratory syncytial virus (RSV) have not been fully characterized. In older adults, symptoms are typically mild but can be serious in patients with comorbidities when the infection extends to the lower respiratory tract.MethodsThis study formed part of the RESCEU older-adults prospective-cohort study in Northern Europe (2017–2019; NCT03621930) in which a thousand participants were followed over an RSV season. Peripheral-blood samples (taken pre-season, post-season, during illness and convalescence) were analyzed from participants who (i) had a symptomatic acute respiratory tract infection by RSV (RSV-ARTI; N=35) or (ii) asymptomatic RSV infection (RSV-Asymptomatic; N=16). These analyses included evaluations of antibody (Fc-mediated–) functional features and cell-mediated immunity, in which univariate and machine-learning (ML) models were used to explore differences between groups.ResultsPre–RSV-season peripheral-blood biomarkers were predictive of symptomatic RSV infection. T-cell data were more predictive than functional antibody data (area under receiver operating characteristic curve [AUROC] for the models were 99% and 76%, respectively). The pre-RSV season T-cell phenotypes which were selected by the ML modelling and which were more frequent in RSV-Asymptomatic group than in the RSV-ARTI group, coincided with prominent phenotypes identified during convalescence from RSV-ARTI (e.g., IFN-γ+, TNF-α+ and CD40L+ for CD4+, and IFN-γ+ and 4-1BB+ for CD8+).ConclusionThe evaluation and statistical modelling of numerous immunological parameters over the RSV season suggests a primary role of cellular immunity in preventing symptomatic RSV infections in older adults.

IntroductionGlobal aquaculture activity has expanded considerably over the past decades. According to the 2020 State of World Fisheries and Aquaculture (SOFIA) report published by the United Nations Food and Agriculture Organization (FAO), total global aquaculture production was 82.1 million tonnes (Mt) in 2018, of which 30.8 Mt was from mariculture and coastal a