Disease Dynamics Research Articles

Diagnostic staging and stratification in psychiatry and oncology: clarifying their conceptual, epistemological and ethical implications.

Staging and stratification are two diagnostic approaches that have introduced a more dynamic outlook on the development of diseases, thus participating in blurring the line between the normal and the pathological. First, diagnostic staging, aiming to capture how diseases evolve in time and/or space through identifiable and gradually more severe stages, may be said to lean on an underlying assumption of "temporal determinism". Stratification, on the other hand, allows for the identification of various prognostic or predictive subgroups based on specific markers, relying on a more "mechanistic" or "statistical" form of determinism. There are two medical fields in which these developments have played a significant role and have given rise to sometimes profound nosological transformations: oncology and psychiatry. Drawing on examples from these two fields, this paper aims to provide much needed conceptual clarifications on both staging and stratification in order to outline how several epistemological and ethical issues may, in turn, arise. We argue that diagnostic staging ought to be detached from the assumption of temporal determinism, though it should still play an essential role in adapting interventions to stage. In doing so, it would help counterbalance stratification's own epistemological and ethical shortcomings. In this sense, the reflections and propositions developed in psychiatry can offer invaluable insights regarding how adopting a more transdiagnostic and cross-cutting perspective on temporality and disease dynamics may help combine both staging and stratification in clinical practice.

Importance of social inequalities to contact patterns, vaccine uptake, and epidemic dynamics.

Individuals' socio-demographic and economic characteristics crucially shape the spread of an epidemic by largely determining the exposure level to the virus and the severity of the disease for those who got infected. While the complex interplay between individual characteristics and epidemic dynamics is widely recognised, traditional mathematical models often overlook these factors. In this study, we examine two important aspects of human behaviour relevant to epidemics: contact patterns and vaccination uptake. Using data collected during the COVID-19 pandemic in Hungary, we first identify the dimensions along which individuals exhibit the greatest variation in their contact patterns and vaccination uptake. We find that generally higher socio-economic groups of the population have a higher number of contacts and a higher vaccination uptake with respect to disadvantaged groups. Subsequently, we propose a data-driven epidemiological model that incorporates these behavioural differences. Finally, we apply our model to analyse the fourth wave of COVID-19 in Hungary, providing valuable insights into real-world scenarios. By bridging the gap between individual characteristics and epidemic spread, our research contributes to a more comprehensive understanding of disease dynamics and informs effective public health strategies.

Landscape drives zoonotic malaria prevalence in non-human primates.

Zoonotic disease dynamics in wildlife hosts are rarely quantified at macroecological scales due to the lack of systematic surveys. Non-human primates (NHPs) host Plasmodium knowlesi, a zoonotic malaria of public health concern and the main barrier to malaria elimination in Southeast Asia. Understanding of regional P. knowlesi infection dynamics in wildlife is limited. Here, we systematically assemble reports of NHP P. knowlesi and investigate geographic determinants of prevalence in reservoir species. Meta-analysis of 6322 NHPs from 148 sites reveals that prevalence is heterogeneous across Southeast Asia, with low overall prevalence and high estimates for Malaysian Borneo. We find that regions exhibiting higher prevalence in NHPs overlap with human infection hotspots. In wildlife and humans, parasite transmission is linked to land conversion and fragmentation. By assembling remote sensing data and fitting statistical models to prevalence at multiple spatial scales, we identify novel relationships between P. knowlesi in NHPs and forest fragmentation. This suggests that higher prevalence may be contingent on habitat complexity, which would begin to explain observed geographic variation in parasite burden. These findings address critical gaps in understanding regional P. knowlesi epidemiology and indicate that prevalence in simian reservoirs may be a key spatial driver of human spillover risk.

SQEAIR: an Improved Infectious Disease Dynamics Model

The Covid-19 pandemic poses a significant threat to human health and life. Timely and accurate prediction of the epidemic’s trajectory is crucial for devising effective prevention and control strategies. Traditional infectious disease models may not capture the complexity of modern epidemics, especially when governments implement diverse policies. Drawing from China’s epidemic prevention strategies and Covid-19 transmission characteristics, this study introduces two distinct categories quarantined cases and asymptomatic cases to enhance the traditional SEIR model in depicting disease dynamics. To address the intricate nature of prevention and control efforts, the quarantined cases are further segmented into three subgroups: exposed quarantined, asymptomatic quarantined, and infected quarantined cases. Consequently, a novel SQEAIR model is proposed to model the dynamics of Covid-19. Evaluation metrics such as the Akaike information criterion (AIC) and Absolute Percentage Error (MAE) are employed to assess the efficacy and accuracy of both the newly proposed and traditional models. By fitting the models to the number of infected cases in Shanghai (March to May 2022) and Guangzhou (November 2022), it was observed that the SQEAIR model exhibited a lower AIC value compared to the SEIR model, indicating superior fitting accuracy for Covid-19 infections. Moreover, the high accuracy of the SQEAIR model enabled precise predictions of confirmed cases in Guangzhou. Leveraging the SQEAIR model, various parameters were tested to simulate the impact of different influencing factors, enabling the evaluation of defense strategies. These findings underscore the effectiveness of key epidemic control measures, such as quarantining exposed cases, in enhancing public health and promoting awareness of personal protection.

Correlates of Co-Infection with Coccidiosis and Avian Malaria in House Finches (Haemorhous mexicanus).

Pathogens have traditionally been studied in isolation within host systems; yet in natural settings they frequently coexist. This raises questions about the dynamics of co-infections and how host life-history traits might predict co-infection versus single infection. To address these questions, we investigated the presence of two parasites, a gut parasite (Isospora coccidians) and a blood parasite (Plasmodium spp.), in House Finches (Haemorhous mexicanus), a common passerine bird in North America. We then correlated these parasitic infections with various health and condition metrics, including hematological parameters, plasma carotenoids, lipid-soluble vitamins, blood glucose concentration, body condition, and prior disease history. Our study, based on 48 birds captured in Tempe, Arizona, US, in October 2021, revealed that co-infected birds exhibited elevated circulating lutein levels and a higher heterophil:lymphocyte ratio (H/L ratio) compared to those solely infected with coccidia Isospora spp. This suggests that co-infected birds experience heightened stress and may use lutein to bolster immunity against both pathogens, and that there are potentially toxic effects of lutein in co-infected birds compared to those infected solely with coccidia Isospora sp. Our findings underscore the synergistic impact of coparasitism, emphasizing the need for more co-infection studies to enhance our understanding of disease dynamics in nature, as well as its implications for wildlife health and conservation efforts.

Flip bifurcation analysis and mathematical modeling of cholera disease by taking control measures

To study the dynamical system, it is necessary to formulate the mathematical model to understand the dynamics of various diseases which are spread in the world wide. The objective of the research study is to assess the early diagnosis and treatment of cholera virus by implementing remedial methods with and without the use of drugs. A mathematical model is built with the hypothesis of strengthening the immune system, and a ABC operator is employed to turn the model into a fractional-order model. A newly developed system SEIBR, which is examined both qualitatively and quantitatively to determine its stable position as well as the verification of flip bifurcation has been made for developed system. The local stability of this model has been explored concerning limited observations, a fundamental aspect of epidemic models. We have derived the reproductive number using next generation method, denoted as “R0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R_{0}$$\\end{document}”, to analyze its impact rate across various sub-compartments, which serves as a critical determinant of its community-wide transmission rate. The sensitivity analysis has been verified according to its each parameters to identify that how much rate of change of parameters are sensitive. Atangana–Toufik scheme is employed to find the solution for the developed system using different fractional values which is advanced tool for reliable bounded solution. Also the error analysis has been made for developed scheme. Simulations have been made to see the real behavior and effects of cholera disease with early detection and treatment by implementing remedial methods without the use of drugs in the community. Also identify the real situation the spread of cholera disease after implementing remedial methods with and without the use of drugs. Such type of investigation will be useful to investigate the spread of virus as well as helpful in developing control strategies from our justified outcomes.

Mathematical Modeling of the Transmission Dynamics of Gumboro Disease

Gumboro disease is a viral poultry disease that causes immune suppression on the infected birds leading to poor production, mortality, and exposure to secondary infections, hence a major threat in the poultry industry worldwide. A mathematical model of the transmission dynamics of Gumboro disease is developed in this paper having four compartments of chicken population and one compartment of Gumboro pathogen population. The basic reproduction number Rog is derived, and the dynamical behaviors of both the disease-free equilibrium (DFE) and endemic equilibrium are analyzed using the ordinary differential equation theory. From the analysis, we found that the system exhibits an asymptotic stable DFE whenever Rog<1 and an asymptotic stable EE whenever Rog>1. The numerical simulation to verify the theoretical results was carried out using MATLAB ode45 solver, and the results were found to be consistent with the theoretical findings.

A model for the control of transmission dynamics of human monkeypox disease in Sub-Saharan Africa

The Human Monkeypox virus has received significant research interest in recent times. While few researchers have studied the effects of vaccination on human-to-animal or animal-to-human Monkeypox transmission, others just studied the effects of treatment on human Monkeypox. In this article, we made the proposition of a deterministic vaccine model that deals with the dynamics of the effects of vaccination and treatment on human Monkeypox in sub-Saharan Africa. We investigated the effects of vaccination on the various epidemiological classes qualitatively. The findings from the analysis of the model are that the model possesses two equilibria, locally asymptotically stable disease-free equilibrium (DFE) when an epidemiological threshold - the effective reproductive number is less than unity, and locally asymptotically stable endemic equilibrium when the number is greater than unity. We then corroborated the theoretical findings with numerical simulations, which reveal that when the rate of vaccination is increased resulting in many newly born persons in the populace being vaccinated, the prevalence of the deadly scourge is significantly reduced, while newly born individuals that miss vaccination experience a drastic torment of the deadly disease that often occasion death. Further revelation from the simulations is that when greater efforts

Genetic differentiation of wild boar populations in a region affected by African swine fever

In the European Union, African swine fever (ASF) affects wild boar (Sus scrofa) populations in several Member States. Knowledge of population connectivity is important for the implementation of control measures, in particular the establishment of effective barriers. Population genetic comparisons of neighbouring populations can be very helpful in this respect. The present study investigated the genetic differentiation of wild boar in eastern Germany. This region has been affected by ASF since September 2020. A total of 1,262 wild boars from 31 hunting grounds (populations) in ASF-affected and ASF-free districts were sampled over a total area of almost 100,000 km². The study area encompassed a network of geographical factors that promote (roads, rivers, cities) or inhibit (natural areas, habitat corridors) genetic differentiation between wild boar populations. The genetic differentiation of the areas was based on 12 microsatellite markers. Three different Bayesian algorithms were used to analyse the data. The results were combined into a common approach with 9 clusters. Based on the cluster distribution in each population, the connectivity between the areas was quantified. The strongest differentiation was found along an imaginary line along the lower Elbe valley through Berlin and the A11 freeway to the Szczecin Lagoon. In contrast, the Mecklenburg Lake District and the south-east of the study area showed strong connectivity between areas. The special features of the landscapes along the lower Elbe valley, which was assessed as highly connective, and the high barrier effect of the A11 freeway in contrast to the other freeways in the study area show that barrier effects cannot be generalised in principle, but are actually determined by the circumstances of individual structures. The results of the connectivity analysis were compared with the distribution of viral lineages and variants. The genotypes of the wild boar populations and the ASFV lineages and variants showed a good explanatory approach for the observed disease dynamics in the study area. The newly gained knowledge on barriers and regionally different connectivity between wild boar populations can support considerations and measures for the containment of ASF in the affected areas by improving the understanding of wild boar dispersal dynamics.

Abnormal EEG microstates in Alzheimer's disease: predictors of β-amyloid deposition degree and disease classification.

Electroencephalography (EEG) microstates are used to study cognitive processes and brain disease-related changes. However, dysfunctional patterns of microstate dynamics in Alzheimer's disease (AD) remain uncertain. To investigate microstate changes in AD using EEG and assess their association with cognitive function and pathological changes in cerebrospinal fluid (CSF). We enrolled 56 patients with AD and 38 age- and sex-matched healthy controls (HC). All participants underwent various neuropsychological assessments and resting-state EEG recordings. Patients with AD also underwent CSF examinations to assess biomarkers related to the disease. Stepwise regression was used to analyze the relationship between changes in microstate patterns and CSF biomarkers. Receiver operating characteristics analysis was used to assess the potential of these microstate patterns as diagnostic predictors for AD. Compared with HC, patients with AD exhibited longer durations of microstates C and D, along with a decreased occurrence of microstate B. These microstate pattern changes were associated with Stroop Color Word Test and Activities of Daily Living scale scores (all P < 0.05). Mean duration, occurrences of microstate B, and mean occurrence were correlated with CSF Aβ 1-42 levels, while duration of microstate C was correlated with CSF Aβ 1-40 levels in AD (all P < 0.05). EEG microstates are used to predict AD classification with moderate accuracy. Changes in EEG microstate patterns in patients with AD correlate with cognition and disease severity, relate to Aβ deposition, and may be useful predictors for disease classification.

Prevalence and risk factors for Anaplasma marginale seropositivity in cattle in California

Bovine anaplasmosis, a tick-borne disease caused by Anaplasma marginale, imposes considerable economic bur­den on cattle industries throughout the world. Clinical signs of bovine anaplasmosis include fever, weight loss, lethargy, jaundice, abortion and death. The severity of the disease in­creases with age, and calves under 1 year of age generally do not progress to clinical disease or develop mild disease. Once infected, cattle remain persistently infected carriers for life. Climate change and drought conditions may affect the distri­bution and prevalence of tick populations and tick-borne dis­eases and re-evaluating the seroprevalence of Anaplasma would provide a better understanding of the disease dynamics in California. To our knowledge, there has been no updated study regarding the distribution of Anaplasma infection status in California since 2008, and the information on weather factors related to the disease is also limited. There is an urgent need to evaluate the prevalence of Anaplasma infection in different regions of California. The goal of this study is to estimate the seroprevalence of Anaplasma infection in California cattle over time, and the specific goal is to identify potential risk factors for infection including animal demographics, region, Anaplasma seroprevalence of wild ruminants, Dermacentor tick species presence, and weather effects.

Dynamics analysis and optimal control of delayed SEIR model in COVID-19 epidemic

The coronavirus disease 2019 (COVID-19) remains serious around the world and causes huge deaths and economic losses. Understanding the transmission dynamics of diseases and providing effective control strategies play important roles in the prevention of epidemic diseases. In this paper, to investigate the effect of delays on the transmission of COVID-19, we propose a delayed SEIR model to describe COVID-19 virus transmission, where two delays indicating the incubation and recovery periods are introduced. For this system, we prove its solutions are nonnegative and ultimately bounded with the nonnegative initial conditions. Furthermore, we calculate the disease-free and endemic equilibrium points and analyze the asymptotical stability and the existence of Hopf bifurcations at these equilibrium points. Then, by taking the weighted sum of the opposite number of recovered individuals at the terminal time, the number of exposed and infected individuals during the time horizon, and the system cost of control measures as the cost function, we present a delay optimal control problem, where two controls represent the social contact and the pharmaceutical intervention. Necessary optimality conditions of this optimal control problem are exploited to characterize the optimal control strategies. Finally, numerical simulations are performed to verify the theoretical analysis of the stability and Hopf bifurcations at the equilibrium points and to illustrate the effectiveness of the obtained optimal strategies in controlling the COVID-19 epidemic.

How do host population dynamics impact Lyme disease risk dynamics in theoretical models?

Lyme disease is the most common wildlife-to-human transmitted disease reported in North America. The study of this disease requires an understanding of the ecology of the complex communities of ticks and host species involved in harboring and transmitting this disease. Much of the ecology of this system is well understood, such as the life cycle of ticks, and how hosts are able to support tick populations and serve as disease reservoirs, but there is much to be explored about how the population dynamics of different host species and communities impact disease risk to humans. In this study, we construct a stage-structured, empirically-informed model with host dynamics to investigate how host population dynamics can affect disease risk to humans. The model describes a tick population and a simplified community of three host species, where primary nymph host populations are made to fluctuate on an annual basis, as commonly observed in host populations. We tested the model under different environmental conditions to examine the effect of environment on the interactions of host dynamics and disease risk. Results show that allowing for host dynamics in the model reduces mean nymphal infection prevalence and increases the maximum annual prevalence of nymphal infection and the density of infected nymphs. Effects of host dynamics on disease measures of nymphal infection prevalence were nonlinear and patterns in the effect of dynamics on amplitude in nymphal infection prevalence varied across environmental conditions. These results highlight the importance of further study of the effect of community dynamics on disease risk. This will involve the construction of further theoretical models and collection of robust field data to inform these models. With a more complete understanding of disease dynamics we can begin to better determine how to predict and manage disease risk using these models.

Micromobility systems and disease transmission: a spatially explicit agent-based approach

We propose a novel micro-level and spatially explicit agent-based modeling framework dubbed TranEpiSim to model the spread of infectious diseases through micromobility systems and a baseline population in an urban area. A case study is conducted on the Chicago City public bikesharing system to demonstrate the ability of the model to isolate the role of transportation as a vector and assess the efficacy of the model. Results show that the emergence of viral disease through micromobility transportation is possible, but the overall impact of the system on the disease dynamics in a worst-case scenario, especially with the current size of the system, is rather small. The proposed model offers a comprehensive approach to estimate the impact of micromobility (and more generally, specific transportation modes) on disease spread by considering infections that occur both at destinations and in/on transportation vehicles and infrastructure. The spatial pattern for the risk of exposure shows a higher risk in the central business district and north of it, where most of the shared bike transportation occurs. Because of its intrinsic features, the proposed framework is uniquely placed to assess the efficacy of interventions and make trade-offs between competing scenarios when dealing with epidemics.

Global stability analysis and modelling onchocerciasis transmission dynamics with control measures

ABSTRACT Background: Onchocerciasis infection is one of the neglected tropical diseases targeted for eradication by 2030. The disease is usually transmitted to humans through the bites of black flies. These black flies mostly breed near well-oxygenated fast-running water bodies. The disease is common in mostly remote agricultural villages near rivers and streams. Objective: In this study, a deterministic model describing the infection dynamics of human onchocerciasis disease with control measures is presented. Methods: We derived the model’s reproductive number and used a stability theorem of a Metzler matrix to show that disease-free equilibrium is both locally and globally asymptotically stable whenever the reproductive number is less than one. Parameter contribution was conducted using sensitivity analysis. The model endemic equation is shown to be a cubic polynomial in the presence of infected immigrants and a quadratic form in their absence. Results: When the inflow of infected immigrants is null, the model endemic equation may admit a unique equilibrium if the reproductive number is greater than one, or admits multiple endemic equilibria if the reproductive number is less than unity. We carried out a sensitivity analysis to identify the significant parameters that contribute to onchocerciasis spread. Conclusion: Onchocerciasis disease can be eradicated if the importation of infected immigrants is properly monitored. The integration of the One Health concept in the public health system is key in tackling the emergence and spread of diseases.

Modelling the impact of precaution on disease dynamics and its evolution.

In this paper, we introduce the notion of practically susceptible population, which is a fraction of the biologically susceptible population. Assuming that the fraction depends on the severity of the epidemic and the public's level of precaution (as a response of the public to the epidemic), we propose a general framework model with the response level evolving with the epidemic. We firstly verify the well-posedness and confirm the disease's eventual vanishing for the framework model under the assumption that the basic reproduction number . For , we study how the behavioural response evolves with epidemics and how such an evolution impacts the disease dynamics. More specifically, when the precaution level is taken to be the instantaneous best response function in literature, we show that the endemic dynamic is convergence to the endemic equilibrium; while when the precaution level is the delayed best response, the endemic dynamic can be either convergence to the endemic equilibrium, or convergence to a positive periodic solution. Our derivation offers a justification/explanation for the best response used in some literature. By replacing "adopting the best response" with "adapting toward the best response", we also explore the adaptive long-term dynamics.

Herbivores can benefit both plants and their pathogens through selective herbivory on diseased tissue

Abstract Infectious disease can shape community structure, particularly when pathogens affect foundation species. Seagrasses are foundation species that form meadows along coasts worldwide, controlling sediment deposition and biogeochemical cycling while supporting a diverse community of fish and invertebrates. These plants are hosts to wasting diseases that cause necrotic tissue lesions, which could alter seagrass value as food, habitat and mediators of ecosystem processes. However, such biotic interactions and influences on disease dynamics are still not well understood. We investigated whether a common herbivore affected the development of wasting disease on eelgrass. We measured the severity and prevalence of eelgrass wasting disease in a meadow across two summers through repeated field surveys. We assessed the role of the eelgrass herbivore on disease spread and growth using microcosm and mesocosm experiments. We further explored herbivore feeding preferences in a choice trial, which was paired with chemical analysis of plant tissue and analysed using a structural equation model. While the herbivore facilitates the growth of new disease lesions among isolated leaves, on balance they reduce lesion severity by more than 50% in comparison with no‐herbivore controls in field‐realistic settings. This was likely because the herbivore strongly prefers to eat diseased rather than healthy tissue, consuming nearly twice as much lesion area in choice trials. This preference arises from pathogen‐driven changes in the host plant; lesioned tissue requires less force to penetrate than non‐lesioned tissue. Additionally, as lesions increase in size, their polyphenolic concentrations drop, which further increases the magnitude of preference for lesioned tissue. Synthesis: These results suggest that these herbivores could help maintain disease in this system at a high prevalence (by facilitating disease development) but low severity (through preferential consumption), which is consistent with our field observations of nearly 100% prevalence and low severity in a natural bed where herbivore density is high. Describing such multi‐species interactions in marine systems will advance our predictions of future disease states, as current understandings focus primarily on how environmental change contributes to pathogen outbreaks.

Epidemiological model including spatial connection features improves prediction of the spread of pine wilt disease

In recent decades, the tree killing pine wilt disease (PWD) has been spreading globally and has caused substantial damage, particularly in China, resulting in annual losses of nearly $15 billion. This disease is caused by the pine wood nematode (Bursaphelenchus xylophilus) and is transmitted by its vector insect (Monochamus spp.). However, there remains limited understanding of the factors influencing PWD transmission, especially anthropogenic factors. For the first time, we employed a dataset of propagation factors that based on the transportation of pine wood transmission and used an enhanced infectious disease dynamics method to quantitatively predict the likelihood of future PWD infections across the entire distribution range of pine trees in China. The validation results show that the risk of infection in potential areas of PWD will further increase in China, with the average risk surpassing 30% in 2023 and reaching 50% by 2025 in the study areas. Meanwhile, we observe that human factors, such as transportation of timber, are becoming the main cause of the rapid spread of PWD and require extra vigilance. By incorporating spatial connections between samples as features for PWD prediction, the Area Under the Curve (AUC) score of the model reached 0.90. This signifies a highly accurate prediction of the pivotal areas for PWD dissemination, providing valuable guidance for the precise development of prevention and control measures, and presenting innovative approaches for analogous studies.

Qualitative analysis of solutions for a degenerate partial differential equations model of epidemic spread dynamics

Compartmental models are widely used in mathematical epidemiology to describe the dynamics of infectious diseases or in mathematical models of population genetics. In this study, we study a time-dependent Susceptible-Infectious-Susceptible (SIS) Partial Differential Equation (PDE) model that is based on a diffusion-drift approximation of a probability density from a well-known discrete-time Markov chain model. This SIS-PDE model is conservative due to the degeneracy of the diffusion term at the origin. The main results of this article are the qualitative behavior of weak solutions, the dependence of the local asymptotic property of these solutions on initial data, and the existence of Dirac delta function type solutions. Moreover, we study the long-term behavior of solutions and confirm our analysis with numerical computations.

Abstract PO5-15-06: NVIGEN X® Comprehensive Liquid Biopsy for Sensitive ctDNA, Circulating Tumor Cells (CTC), and Protein Detection in Breast Cancer: Next-Generation Sequencing, Patient Case Studies, and Clinical Implications

Abstract Background Breast cancer (BC) represents the most prevalent cancer and is a leading cause of cancer related mortality among women worldwide. Despite notable therapeutic advancements, concern for metastatic recurrence remains a significant challenge. Liquid biopsy has emerged as a promising method for monitoring cancer treatment efficacy and disease progression. However, existing technologies predominantly focus on ctDNA, and their clinical utility is hindered by limited detection sensitivity and accuracy. To address these challenges, NVIGEN has developed NVIGEN X® comprehensive liquid biopsy, a novel minimally invasive approach that integrates ctDNA, CTC, and protein biomarkers. This comprehensive method could enable longitudinal monitoring of tumor dynamics, offering valuable insights into cancer biology, treatment responses, and emerging therapeutic targets. Here, we showcase compelling patient case studies, and the potential clinical implications of our technology for an exciting and evolving field in cancer diagnostics. Method Patients with advanced breast cancer were prospectively identified and consented. 10mL blood per timepoint was collected in Streck tubes. ctDNA, CTC and proteins were captured from a single tube of blood using NVIGEN magnetic nanoparticles according to established protocols. Following DNA extraction, ctDNA and CTC samples underwent NVIGEN’s highly sensitive sample workflow, optimized for NGS data accuracy and efficiency, employing hybridization capture with a 180 gene customizable cancer panel. Sequenced data were processed with NVIGEN’s proprietary high-sensitivity data analysis pipeline. Gene alterations identified from both ctDNA and CTC samples were analyzed and integrated to provide a comprehensive view of the tumor profile. Protein biomarkers of interest were detected using NVIGEN’s on-beads ELISA assay. Correlation analysis was performed to examine the relationship between the detected gene alterations, protein biomarkers, ctDNA/CTC data, and clinicopathologic features. Treatment response and identification of potential new treatment targets were evaluated based on the integrated analysis of ctDNA, CTC, protein biomarkers, and patient information. Result We collected 94 blood samples from 85 patients. Here we will present data from initial 36 patient samples. Of these 36 patients, 18 were tested with ctDNA/protein biomarkers (duo assays), and 18 were tested with ctDNA/CTC/protein biomarkers (trio assays). Comparison between duo and trio samples revealed that trio samples provided 2.5 times the number of detected variants compared to duo samples, with the median of variant numbers in duo assays being 52 (range 22-442), and in trio assays being 130 (range 73-703). Over 50% of the detected variants fell within the 0.1-0.5% variant allele frequency (VAF) range (number 2599) with total variant number being 5123. Several patient case studies were conducted using serial blood samples. These case studies revealed notable patterns of important gene alterations, such as ESR1, ERBB2 and PIK3CA, and demonstrated trends in the presence of these genes in ctDNA, CTC, or their coexistence. Protein biomarker information was correlated with ctDNA, CTC NGS data, and patient clinical status. Conclusions The analysis of the presented data from 36 patient samples with advanced breast cancer highlight the clinical relevance and potential significance of utilizing ctDNA, CTC and protein biomarkers in monitoring disease dynamics, treatment response, and guiding personalized treatment approaches. Our results suggest that individual protein biomarker analysis may be crucial in supporting personalized diagnostics and enabling more effective personalized medicine strategies. Citation Format: Aihua Fu, Minh Ton, Kevan Wang, Weiwei Gu, Henry Jin, Nuzhat Shaikh, Sasha Madan, Shreya Perepa, Tianhong Li, Minetta Liu, Heather Parsons, George Sledge, Fauzia Riaz. NVIGEN X® Comprehensive Liquid Biopsy for Sensitive ctDNA, Circulating Tumor Cells (CTC), and Protein Detection in Breast Cancer: Next-Generation Sequencing, Patient Case Studies, and Clinical Implications [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-15-06.