A Viral Exposure Signature Defines Early Onset of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is an aggressive malignancy with its global incidence and mortality rate continuing to rise, although early detection and surveillance are suboptimal. We performed serological profiling of the viral infection history in 899 individuals from an NCI-UMD case-control study using a synthetic human virome, VirScan. We developed a viral exposure signature and validated the results in a longitudinal cohort with 173 at-risk patients who had long-term follow-up for HCC development. Our viral exposure signature significantly associated with HCC status among at-risk individuals in the validation cohort (area under the curve: 0.91 [95% CI 0.87-0.96] at baseline and 0.98 [95% CI 0.97-1] at diagnosis). The signature identified cancer patients prior to a clinical diagnosis and was superior to alpha-fetoprotein. In summary, we established a viral exposure signature that can predict HCC among at-risk patients prior to a clinical diagnosis, which may be useful in HCC surveillance.

Controlling Visually Guided Behavior by Holographic Recalling of Cortical Ensembles.

Neurons in cortical circuits are often coactivated as ensembles, yet it is unclear whether ensembles play a functional role in behavior. Some ensemble neurons have pattern completion properties, triggering the entire ensemble when activated. Using two-photon holographic optogenetics in mouse primary visual cortex, we tested whether recalling ensembles by activating pattern completion neurons alters behavioral performance in a visual task. Disruption of behaviorally relevant ensembles by activation of non-selective neurons decreased performance, whereas activation of only two pattern completion neurons from behaviorally relevant ensembles improved performance, by reliably recalling the whole ensemble. Also, inappropriate behavioral choices were evoked by the mistaken activation of behaviorally relevant ensembles. Finally, in absence of visual stimuli, optogenetic activation of two pattern completion neurons could trigger behaviorally relevant ensembles and correct behavioral responses. Our results demonstrate a causal role of neuronal ensembles in a visually guided behavior and suggest that ensembles implement internal representations of perceptual states.

Decoding the Genomics of Abdominal Aortic Aneurysm.

A key aspect of genomic medicine is to make individualized clinical decisions from personal genomes. We developed a machine-learning framework to integrate personal genomes and electronic health record (EHR) data and used this framework to study abdominal aortic aneurysm (AAA), a prevalent irreversible cardiovascular disease with unclear etiology. Performing whole-genome sequencing on AAA patients and controls, we demonstrated its predictive precision solely from personal genomes. By modeling personal genomes with EHRs, this framework quantitatively assessed the effectiveness of adjusting personal lifestyles given personal genome baselines, demonstrating its utility as a personal health management tool. We showed that this new framework agnostically identified genetic components involved in AAA, which were subsequently validated in human aortic tissues and in murine models. Our study presents a new framework for disease genome analysis, which can be used for both health management and understanding the biological architecture of complex diseases. VIDEO ABSTRACT.

Health system-scale language models are all-purpose prediction engines.

Physicians make critical time-constrained decisions every day. Clinical predictive models can help physicians and administrators make decisions by forecasting clinical and operational events. Existing structured data-based clinical predictive models have limited use in everyday practice owing to complexity in data processing, as well as model development and deployment1-3. Here we show that unstructured clinical notes from the electronic health record can enable the training of clinical language models, which can be used as all-purpose clinical predictive engines with low-resistance development and deployment. Our approach leverages recent advances in natural language processing4,5 to train a large language model for medical language (NYUTron) and subsequently fine-tune it across a wide range of clinical and operational predictive tasks. We evaluated our approach within our health system for five such tasks: 30-day all-cause readmission prediction, in-hospital mortality prediction, comorbidity index prediction, length of stay prediction, and insurance denial prediction. We show that NYUTron has an area under the curve (AUC) of 78.7-94.9%, with an improvement of 5.36-14.7% in the AUC compared with traditional models. We additionally demonstrate the benefits of pretraining with clinical text, the potential for increasing generalizability to different sites through fine-tuning and the full deployment of our system in a prospective, single-arm trial. These results show the potential for using clinical language models in medicine to read alongside physicians and provide guidance at the point of care.

KGE-UNIT: toward the unification of molecular interactions prediction based on knowledge graph and multi-task learning on drug discovery.

The prediction of molecular interactions is vital for drug discovery. Existing methods often focus on individual prediction tasks and overlook the relationships between them. Additionally, certain tasks encounter limitations due to insufficient data availability, resulting in limited performance. To overcome these limitations, we propose KGE-UNIT, a unified framework that combines knowledge graph embedding (KGE) and multi-task learning, for simultaneous prediction of drug-target interactions (DTIs) and drug-drug interactions (DDIs) and enhancing the performance of each task, even when data availability is limited. Via KGE, we extract heterogeneous features from the drug knowledge graph to enhance the structural features of drug and protein nodes, thereby improving the quality of features. Additionally, employing multi-task learning, we introduce an innovative predictor that comprises the task-aware Convolutional Neural Network-based (CNN-based) encoder and the task-aware attention decoder which can fuse better multimodal features, capture the contextual interactions of molecular tasks and enhance task awareness, leading to improved performance. Experiments on two imbalanced datasets for DTIs and DDIs demonstrate the superiority of KGE-UNIT, achieving high area under the receiver operating characteristics curves (AUROCs) (0.942, 0.987) and area under the precision-recall curve ( AUPRs) (0.930, 0.980) for DTIs and high AUROCs (0.975, 0.989) and AUPRs (0.966, 0.988) for DDIs. Notably, on the LUO dataset where the data were more limited, KGE-UNIT exhibited a more pronounced improvement, with increases of 4.32$\%$ in AUROC and 3.56$\%$ in AUPR for DTIs and 6.56$\%$ in AUROC and 8.17$\%$ in AUPR for DDIs. The scalability of KGE-UNIT is demonstrated through its extension to protein-protein interactions prediction, ablation studies and case studies further validate its effectiveness.

GMFGRN: a matrix factorization and graph neural network approach for gene regulatory network inference.

The recent advances of single-cell RNA sequencing (scRNA-seq) have enabled reliable profiling of gene expression at the single-cell level, providing opportunities for accurate inference of gene regulatory networks (GRNs) on scRNA-seq data. Most methods for inferring GRNs suffer from the inability to eliminate transitive interactions or necessitate expensive computational resources. To address these, we present a novel method, termed GMFGRN, for accurate graph neural network (GNN)-based GRN inference from scRNA-seq data. GMFGRN employs GNN for matrix factorization and learns representative embeddings for genes. For transcription factor-gene pairs, it utilizes the learned embeddings to determine whether they interact with each other. The extensive suite of benchmarking experiments encompassing eight static scRNA-seq datasets alongside several state-of-the-art methods demonstrated mean improvements of 1.9 and 2.5% over the runner-up in area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC). In addition, across four time-series datasets, maximum enhancements of 2.4 and 1.3% in AUROC and AUPRC were observed in comparison to the runner-up. Moreover, GMFGRN requires significantly less training time and memory consumption, with time and memory consumed <10% compared to the second-best method. These findings underscore the substantial potential of GMFGRN in the inference of GRNs. It is publicly available at https://github.com/Lishuoyy/GMFGRN.

Language model enables end-to-end accurate detection of cancer from cell-free DNA.

We present a language model Affordable Cancer Interception and Diagnostics (ACID) that can achieve high classification performance in the diagnosis of cancer exclusively from using raw cfDNA sequencing reads. We formulate ACID as an autoregressive language model. ACID is pretrained with language sentences that are obtained from concatenation of raw sequencing reads and diagnostic labels. We benchmark ACID against three methods. On testing set subjected to whole-genome sequencing, ACID significantly outperforms the best benchmarked method in diagnosis of cancer [Area Under the Receiver Operating Curve (AUROC), 0.924 versus 0.853; P < 0.001] and detection of hepatocellular carcinoma (AUROC, 0.981 versus 0.917; P < 0.001). ACID can achieve high accuracy with just 10 000 reads per sample. Meanwhile, ACID achieves the best performance on testing sets that were subjected to bisulfite sequencing compared with benchmarked methods. In summary, we present an affordable, simple yet efficient end-to-end paradigm for cancer detection using raw cfDNA sequencing reads.

Host variables confound gut microbiota studies of human disease.

Low concordance between studies that examine the role of microbiota in human diseases is a pervasive challenge that limits the capacity to identify causal relationships between host-associated microorganisms and pathology. The risk of obtaining false positives is exacerbated by wide interindividual heterogeneity in microbiota composition1, probably due to population-wide differences in human lifestyle and physiological variables2 that exert differential effects on the microbiota. Here we infer the greatest, generalized sources of heterogeneity in human gut microbiota profiles and also identify human lifestyle and physiological characteristics that, if not evenly matched between cases and controls, confound microbiota analyses to produce spurious microbial associations with human diseases. We identify alcohol consumption frequency and bowel movement quality as unexpectedly strong sources of gut microbiota variance that differ in distribution between healthy participants and participants with a disease and that can confound study designs. We demonstrate that for numerous prevalent, high-burden human diseases, matching cases and controls for confounding variables reduces observed differences in the microbiota and the incidence of spurious associations. On this basis, we present a list of host variables that we recommend should be captured in human microbiota studies for the purpose of matching comparison groups, which we anticipate will increase robustness and reproducibility in resolving the members of the gut microbiota that are truly associated with human disease.

EDP-938, a Respiratory Syncytial Virus Inhibitor, in a Human Virus Challenge.

BACKGROUND: Respiratory syncytial virus (RSV) infection causes substantial morbidity and mortality among infants, older adults, and immunocompromised adults. EDP-938, a nonfusion replication inhibitor of RSV, acts by modulating the viral nucleoprotein. METHODS: In a two-part, phase 2a, randomized, double-blind, placebo-controlled challenge trial, we assigned participants who had been inoculated with RSV-A Memphis 37b to receive EDP-938 or placebo. Different doses of EDP-938 were assessed. Nasal-wash samples were obtained from day 2 until day 12 for assessments. Clinical symptoms were assessed by the participants, and pharmacokinetic profiles were obtained. The primary end point was the area under the curve (AUC) for the RSV viral load, as measured by reverse-transcriptase-quantitative polymerase-chain-reaction assay. The key secondary end point was the AUC for the total symptom score. RESULTS: In part 1 of the trial, 115 participants were assigned to receive EDP-938 (600 mg once daily [600-mg once-daily group] or 300 mg twice daily after a 500-mg loading dose [300-mg twice-daily group]) or placebo. In part 2, a total of 63 participants were assigned to receive EDP-938 (300 mg once daily after a 600-mg loading dose [300-mg once-daily group] or 200 mg twice daily after a 400-mg loading dose [200-mg twice-daily group]) or placebo. In part 1, the AUC for the mean viral load (hours × log10 copies per milliliter) was 204.0 in the 600-mg once-daily group, 217.7 in the 300-mg twice-daily group, and 790.2 in the placebo group. The AUC for the mean total symptom score (hours × score, with higher values indicating greater severity) was 124.5 in the 600-mg once-daily group, 181.8 in the 300-mg twice-daily group, and 478.8 in the placebo group. The results in part 2 followed a pattern similar to that in part 1: the AUC for the mean viral load was 173.9 in the 300-mg once-daily group, 196.2 in the 200-mg twice-daily group, and 879.0 in the placebo group, and the AUC for the mean total symptom score was 99.3, 89.6, and 432.2, respectively. In both parts, mucus production was more than 70% lower in each EDP-938 group than in the placebo group. The four EDP-938 regimens had a safety profile similar to that of placebo. Across all dosing regimens, the EDP-938 median time to maximum concentration ranged from 4 to 5 hours, and the geometric mean half-life ranged from 13.7 to 14.5 hours. CONCLUSIONS: All EDP-938 regimens were superior to placebo with regard to lowering of the viral load, total symptom scores, and mucus weight without apparent safety concerns. (ClinicalTrials.gov number, NCT03691623.).

AGAT-PPIS: a novel protein-protein interaction site predictor based on augmented graph attention network with initial residual and identity mapping.

Identifying protein-protein interaction (PPI) site is an important step in understanding biological activity, apprehending pathological mechanism and designing novel drugs. Developing reliable computational methods for predicting PPI site as screening tools contributes to reduce lots of time and expensive costs for conventional experiments, but how to improve the accuracy is still challenging. We propose a PPI site predictor, called Augmented Graph Attention Network Protein-Protein Interacting Site (AGAT-PPIS), based on AGAT with initial residual and identity mapping, in which eight AGAT layers are connected to mine node embedding representation deeply. AGAT is our augmented version of graph attention network, with added edge features. Besides, extra node features and edge features are introduced to provide more structural information and increase the translation and rotation invariance of the model. On the benchmark test set, AGAT-PPIS significantly surpasses the state-of-the-art method by 8% in Accuracy, 17.1% in Precision, 11.8% in F1-score, 15.1% in Matthews Correlation Coefficient (MCC), 8.1% in Area Under the Receiver Operating Characteristic curve (AUROC), 14.5% in Area Under the Precision-Recall curve (AUPRC), respectively.

GCFMCL: predicting miRNA-drug sensitivity using graph collaborative filtering and multi-view contrastive learning.

Studies have shown that the mechanism of action of many drugs is related to miRNA. In-depth research on the relationship between miRNA and drugs can provide theoretical foundations and practical approaches for various areas, such as drug target discovery, drug repositioning and biomarker research. Traditional biological experiments to test miRNA-drug susceptibility are costly and time-consuming. Thus, sequence- or topology-based deep learning methods are recognized in this field for their efficiency and accuracy. However, these methods have limitations in dealing with sparse topologies and higher-order information of miRNA (drug) feature. In this work, we propose GCFMCL, a model for multi-view contrastive learning based on graph collaborative filtering. To the best of our knowledge, this is the first attempt that incorporates contrastive learning strategy into the graph collaborative filtering framework to predict the sensitivity relationships between miRNA and drug. The proposed multi-view contrastive learning method is divided into topological contrastive objective and feature contrastive objective: (1) For the homogeneous neighbors of the topological graph, we propose a novel topological contrastive learning method via constructing the contrastive target through the topological neighborhood information of nodes. (2) The proposed model obtains feature contrastive targets from high-order feature information according to the correlation of node features, and mines potential neighborhood relationships in the feature space. The proposed multi-view comparative learning effectively alleviates the impact of heterogeneous node noise and graph data sparsity in graph collaborative filtering, and significantly enhances the performance of the model. Our study employs a dataset derived from the NoncoRNA and ncDR databases, encompassing 2049 experimentally validated miRNA-drug sensitivity associations. Five-fold cross-validation shows that the Area Under the Curve (AUC), Area Under the Precision-Recall Curve (AUPR) and F1-score (F1) of GCFMCL reach 95.28%, 95.66% and 89.77%, which outperforms the state-of-the-art (SOTA) method by the margin of 2.73%, 3.42% and 4.96%, respectively. Our code and data can be accessed at https://github.com/kkkayle/GCFMCL.

MSGCL: inferring miRNA-disease associations based on multi-view self-supervised graph structure contrastive learning.

Potential miRNA-disease associations (MDA) play an important role in the discovery of complex human disease etiology. Therefore, MDA prediction is an attractive research topic in the field of biomedical machine learning. Recently, several models have been proposed for this task, but their performance limited by over-reliance on relevant network information with noisy graph structure connections. However, the application of self-supervised graph structure learning to MDA tasks remains unexplored. Our study is the first to use multi-view self-supervised contrastive learning (MSGCL) for MDA prediction. Specifically, we generated a learner view without association labels of miRNAs and diseases as input, and utilized the known association network to generate an anchor view that provides guiding signals for the learner view. The graph structure was optimized by designing a contrastive loss to maximize the consistency between the anchor and learner views. Our model is similar to a pre-trained model that continuously optimizes upstream tasks for high-quality association graph topology, thereby enhancing the latent representation of association predictions. The experimental results show that our proposed method outperforms state-of-the-art methods by 2.79$\%$ and 3.20$\%$ in area under the receiver operating characteristic curve (AUC) and area under the precision/recall curve (AUPR), respectively.

Predicting Drug-drug Interaction with Graph Mutual Interaction Attention Mechanism.

Effective representation of molecules is a crucial step in AI-driven drug design and drug discovery, especially for drug-drug interaction (DDIs) prediction. Previous work usually models the drug information from the drug-related knowledge graph or the single drug molecules, but the interaction information between molecular substructures of drug pair is seldom considered, thus often ignoring the influence of bond information on atom node representation, leading to insufficient drug representation. Moreover, key molecular substructures have significant contribution to the DDIs prediction results. Therefore, in this work, we propose a novel Graph learning framework of Mutual Interaction Attention mechanism (called GMIA) to predict DDIs by effectively representing the drug molecules. Specifically, we build the node-edge message communication encoder to aggregate atom node and the incoming edge information for atom node representation and design the mutual interaction attention decoder to capture the mutual interaction context between molecular graphs of drug pairs. GMIA can bridge the gap between two encoders for the single drug molecules by attention mechanism. We also design a co-attention matrix to analyze the significance of different-size substructures obtained from the encoder-decoder layer and provide interpretability. In comparison with other recent state-of-the-art methods, our GMIA achieves the best results in terms of area under the precision-recall-curve (AUPR), area under the ROC curve (AUC), and F1 score on two different scale datasets. The case study indicates that our GMIA can detect the key substructure for potential DDIs, demonstrating the enhanced performance and interpretation ability of GMIA.

PGCNMDA: Learning node representations along paths with graph convolutional network for predicting miRNA-disease associations.

Identifying miRNA-disease associations (MDAs) is crucial for improving the diagnosis and treatment of various diseases. However, biological experiments can be time-consuming and expensive. To overcome these challenges, computational approaches have been developed, with Graph Convolutional Network (GCN) showing promising results in MDA prediction. The success of GCN-based methods relies on learning a meaningful spatial operator to extract effective node feature representations. To enhance the inference of MDAs, we propose a novel method called PGCNMDA, which employs graph convolutional networks with a learning graph spatial operator from paths. This approach enables the generation of meaningful spatial convolutions from paths in GCN, leading to improved prediction performance. On HMDD v2.0, PGCNMDA obtains a mean AUC of 0.9229 and an AUPRC of 0.9206 under 5-fold cross-validation (5-CV), and a mean AUC of 0.9235 and an AUPRC of 0.9212 under 10-fold cross-validation (10-CV), respectively. Additionally, the AUC of PGCNMDA also reaches 0.9238 under global leave-one-out cross-validation (GLOOCV). On HMDD v3.2, PGCNMDA obtains a mean AUC of 0.9413 and an AUPRC of 0.9417 under 5-CV, and a mean AUC of 0.9419 and an AUPRC of 0.9425 under 10-CV, respectively. Furthermore, the AUC of PGCNMDA also reaches 0.9415 under GLOOCV. The results show that PGCNMDA is superior to other compared methods. In addition, the case studies on pancreatic neoplasms, thyroid neoplasms and leukemia show that 50, 50 and 48 of the top 50 predicted miRNAs linked to these diseases are confirmed, respectively. It further validates the effectiveness and feasibility of PGCNMDA in practical applications.

A deep eutectic-based, self-emulsifying subcutaneous depot system for apomorphine therapy in Parkinson's disease.

Apomorphine, a dopamine agonist, is a highly effective therapeutic to prevent intermittent off episodes in advanced Parkinson's disease. However, its short systemic half-life necessitates three injections per day. Such a frequent dosing regimen imposes a significant compliance challenge, especially given the nature of the disease. Here, we report a deep eutectic-based formulation that slows the release of apomorphine after subcutaneous injection and extends its pharmacokinetics to convert the current three-injections-a-day therapy into an every-other-day therapy. The formulation comprises a homogeneous mixture of a deep eutectic solvent choline-geranate, a cosolvent n-methyl-pyrrolidone, a stabilizer polyethylene glycol, and water, which spontaneously emulsifies into a microemulsion upon injection in the subcutaneous space, thereby entrapping apomorphine and significantly slowing its release. Ex vivo studies with gels and rat skin demonstrate this self-emulsification process as the mechanism of action for sustained release. In vivo pharmacokinetics studies in rats and pigs further confirmed the extended release and improvement over the clinical comparator Apokyn. In vivo pharmacokinetics, supported by a pharmacokinetic simulation, demonstrate that the deep eutectic formulation reported here allows the maintenance of the therapeutic drug concentration in plasma in humans with a dosing regimen of approximately three injections per week compared to the current clinical practice of three injections per day.

Epistatic models predict mutable sites in SARS-CoV-2 proteins and epitopes.

The emergence of new variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a major concern given their potential impact on the transmissibility and pathogenicity of the virus as well as the efficacy of therapeutic interventions. Here, we predict the mutability of all positions in SARS-CoV-2 protein domains to forecast the appearance of unseen variants. Using sequence data from other coronaviruses, preexisting to SARS-CoV-2, we build statistical models that not only capture amino acid conservation but also more complex patterns resulting from epistasis. We show that these models are notably superior to conservation profiles in estimating the already observable SARS-CoV-2 variability. In the receptor binding domain of the spike protein, we observe that the predicted mutability correlates well with experimental measures of protein stability and that both are reliable mutability predictors (receiver operating characteristic areas under the curve ∼0.8). Most interestingly, we observe an increasing agreement between our model and the observed variability as more data become available over time, proving the anticipatory capacity of our model. When combined with data concerning the immune response, our approach identifies positions where current variants of concern are highly overrepresented. These results could assist studies on viral evolution and future viral outbreaks and, in particular, guide the exploration and anticipation of potentially harmful future SARS-CoV-2 variants.

External Validation of the Discriminative Validity of the ReSVinet Score and Development of Simplified ReSVinet Scores in Secondary Care.

BACKGROUND: There is no consensus on how to best quantify disease severity in infants with respiratory syncytial virus (RSV) and/or bronchiolitis; this lack of a sufficiently validated score complicates the provision of clinical care and, the evaluation of trials of therapeutics and vaccines. The ReSVinet score appears to be one of the most promising; however, it is too time consuming to be incorporated into routine clinical care. We aimed to develop and externally validate simplified versions of this score. METHODS: Data from a multinational (the Netherlands, Spain, and United Kingdom) multicenter case-control study of infants with RSV were used to develop simplified versions of the ReSVinet score by conducting a grid search to determine the best combination of equally weighted parameters to maximize for the discriminative ability (measured by area under the receiver operating characteristic curve [AUROC]) across a range of outcomes (hospitalization, intensive care unit admission, ventilation requirement). Subsequently discriminative validity of the score for a range of secondary care outcomes was externally validated by secondary analysis of datasets from Rwanda and Colombia. RESULTS: Three candidate simplified scores were identified using the development dataset; they were excellent (AUROC >0.9) at discriminating for a range of outcomes, and their performance was not significantly different from the original ReSVinet score despite having fewer parameters. In the external validation datasets, the simplified scores were moderate to excellent (AUROC, 0.7-1) across a range of outcomes. In all outcomes, except in a single dataset for predicting admission to the high-dependency unit, they performed at least as well as the original ReSVinet score. CONCLUSIONS: The candidate simplified scores developed require further external validation in larger datasets, ideally from resource-limited settings before any recommendation regarding their use.

Using Data-Driven Algorithms with Large-Scale Plasma Proteomic Data to Discover Novel Biomarkers for Diagnosing Depression.

Given recent technological advances in proteomics, it is now possible to quantify plasma proteomes in large cohorts of patients to screen for biomarkers and to guide the early diagnosis and treatment of depression. Here we used CatBoost machine learning to model and discover biomarkers of depression in UK Biobank data sets (depression n = 4,479, healthy control n = 19,821). CatBoost was employed for model construction, with Shapley Additive Explanations (SHAP) being utilized to interpret the resulting model. Model performance was corroborated through 5-fold cross-validation, and its diagnostic efficacy was evaluated based on the area under the receiver operating characteristic (AUC) curve. A total of 45 depression-related proteins were screened based on the top 20 important features output by the CatBoost model in six data sets. Of the nine diagnostic models for depression, the performance of the traditional risk factor model was improved after the addition of proteomic data, with the best model having an average AUC of 0.764 in the test sets. KEGG pathway analysis of 45 screened proteins showed that the most significant pathway involved was the cytokine-cytokine receptor interaction. It is feasible to explore diagnostic biomarkers of depression using data-driven machine learning methods and large-scale data sets, although the results require validation.

Fragmentomics features of ovarian cancer.

Ovarian cancer (OC) is a major cause of cancer mortality in women worldwide. Due to the occult onset of OC, its nonspecific clinical symptoms in the early phase, and a lack of effective early diagnostic tools, most OC patients are diagnosed at an advanced stage. In this study, shallow whole-genome sequencing was utilized to characterize fragmentomics features of circulating tumor DNA (ctDNA) in OC patients. By applying a machine learning model, multiclass fragmentomics data achieved a mean area under the curve (AUC) of 0.97 (95% CI 0.962-0.976) for diagnosing OC. OC scores derived from this model strongly correlated with the disease stage. Further comparative analysis of OC scores illustrated that the fragmentomics-based technology provided additional clinical benefits over the traditional serum biomarkers cancer antigen 125 (CA125) and the Risk of Ovarian Malignancy Algorithm (ROMA) index. In conclusion, fragmentomics features in ctDNA are potential biomarkers for the accurate diagnosis of OC.

A phase I, randomized, double-blind, placebo-controlled, ascending single- and multiple-dose study of the pharmacokinetics, safety, and tolerability of oral ceftibuten in healthy adult subjects.

This was a phase I, randomized, double-blind, placebo-controlled, ascending single- and multiple-dose study of oral ceftibuten to describe the pharmacokinetics (PK) of cis-ceftibuten (administered form) and trans-ceftibuten (metabolite), and to describe safety and tolerability at higher than licensed doses. Subjects received single 400, 600, or 800 mg doses of ceftibuten on Days 1 and 4, followed by 7 days of twice-daily dosing from Days 4 to 10. Non-compartmental methods were used to describe parent drug and metabolite PK in plasma and urine. Dose proportionality was examined using C max, AUC0-12, and AUC0-INF. Accumulation was calculated as the ratio of AUC0-12 on Days 4 and 10. Adverse events (AEs) were monitored throughout the study. Following single ascending doses, mean cis- and trans-ceftibuten C max were 17.6, 24.1, and 28.1 mg/L, and 1.1, 1.5, and 2.2 mg/L, respectively; cis-ceftibuten urinary recovery accounted for 64.3%-86.9% of the administered dose over 48 h. Following multiple ascending doses, mean cis- and trans-ceftibuten C max were 21.7, 28.1, and 38.8 mg/L, and 1.4, 1.9, and 2.8 mg/L, respectively; cis-ceftibuten urinary recovery accounted for 72.2%-96.4% of the administered dose at steady state. The exposure of cis- and trans-ceftibuten increased proportionally with increasing doses. Cis- and trans-ceftibuten accumulation factor was 1.14-1.19 and 1.28-1.32. The most common gastrointestinal treatment emergent AEs were mild and resolved without intervention. Ceftibuten was well tolerated. Dose proportionality and accumulation of cis- and trans-ceftibuten were observed. These results support the ongoing development of ceftibuten at doses up to 800 mg twice-daily. (The study was registered at ClinicalTrials.gov under the identifier NCT03939429.).