Search Results (733)

Reporting delays are a common challenge in actuarial reserving and infectious disease surveillance, where incomplete development information can distort real-time estimation and decision-making. Classical reserving methods, such as the chain ladder method, assume stable development patterns across event periods, which may be unrealistic when reporting behavior evolves over time. This paper develops a Hoerl-type state-space framework, in which development dynamics evolve as latent stochastic processes within a linear Gaussian state-space model. Estimation is conducted using the Kalman filter and Rauch-Tung-Striebel smoother, allowing recursive estimation under incomplete run-off triangles. The paper further establishes consistency and asymptotic normality for estimators of latent states, ultimate quantities, and the effective reproduction number. Simulation and empirical applications show that the proposed method performs comparably to Mack’s model under stable development patterns while providing substantially more accurate estimates of effective reproduction numbers when reporting behavior varies over time or delays remain unresolved near the boundary of the observation window. These results suggest that the proposed approach provides a flexible and theoretically grounded extension of classical actuarial reserving methods. Full article

Background/Objectives: Krebs von den Lungen-6 (KL-6) is a mucin-like glycoprotein that is elevated in a variety of lung diseases and used as a diagnostic and prognostic biomarker in people with cystic fibrosis (pwCF). Single nucleotide polymorphisms (SNPs) in mucin-1 (MUC1) influence KL-6 serum concentration. This study investigated the relationship between serum KL-6 concentrations in pwCF and a MUC1 SNP and its longitudinal dynamics. Methods: The study included pwCF (n = 174) and healthy controls (n = 30). In pwCF, 365 samples were collected for longitudinal analyses; KL-6 levels were measured and the MUC1 SNP rs4072037 was genotyped in pwCF and controls. Cross-sectional and longitudinal associations between KL-6, genotype, and clinical parameters, such as infectious exacerbation, body mass index, inflammatory values and lung function, were analyzed using linear mixed-effects models. Results: Serum KL-6 was significantly elevated in pwCF compared with controls (458 ± 357 vs. 283 ± 103 U/mL; p < 0.001). Homozygous G/G carriers exhibited higher baseline KL-6 than A/A carriers (627 ± 673 vs. 397 ± 148 U/mL; p < 0.001), while heterozygous individuals showed intermediate levels. Longitudinally, the MUC1 SNP and interindividual differences in vital capacity (ppFVC) primarily determined baseline KL-6 levels, explaining 52.5% of variance. Short-term intraindividual fluctuations were largely driven by infectious exacerbations independent of genotype, accounting for ~10% of within-subject variance. Conclusions: PwCF generally showed elevated serum KL-6 levels and reflected both stable interindividual differences, mainly driven by the MUC1 SNP and ppFVC. Dynamic intraindividualchanges were associated with infectious exacerbations. Given the influence of MUC1 polymorphisms (e.g., rs4072037) on KL-6 concentration, personalized interpretation based on the genotype status may be informative in pwCF. Full article

The cribriform plate (CP) functions as a dynamic neuroimmune interface through which olfactory nerve bundles exit the brain within a specialized perineural microenvironment (cpPME). While traditionally viewed as a passive structural barrier, emerging evidence positions the CP as a central hub for cerebrospinal fluid (CSF) drainage, glymphatic–lymphatic clearance, and antigen presentation. This review provides a comprehensive understanding of recent advances in cpPME research, highlighting the adaptive remodeling of the immune landscape in response to neuroinflammation and aging. We critically evaluate the translational gap between rodent models and human physiology, discussing the implications for neurodegenerative diagnostics, neuroinflammatory conditions, infectious diseases and “nose-to-brain” therapeutic delivery. By integrating anatomical, physiological, and immunological perspectives, we offer a comprehensive framework for understanding the CP’s role in CNS homeostasis and its potential as a transformative diagnostic and therapeutic target. Full article

East Coast Fever (ECF) causes approximately one million livestock deaths annually in sub-Saharan Africa, posing a significant threat to livestock. The wildlife–livestock interface complicates disease management, as wildlife serve as reservoirs. This study developed a Continuous Time Markov Chain (CTMC) model incorporating the wildlife–livestock interface to analyze ECF dynamics. Using the Galton–Watson approximation, we assessed the probability of disease extinction following the introduction of infected hosts or vectors. The probability of disease extinction calculated from the branching process is shown to be in good agreement with the probability approximated from numerical simulations. The disease dynamics of the deterministic model and the CTMC model are compared to ascertain the effect of demographic stochasticity on ECF dynamics. Differences in model predictions and asymptotic dynamics between stochastic and deterministic models were evident. The deterministic and stochastic formulations should therefore be viewed as complementary modeling frameworks, with the deterministic model characterizing average epidemic dynamics and the CTMC model capturing the probabilistic variability and extinction behavior inherent in real transmission processes. These differences are crucial for intervention strategies earmarked to prevent outbreaks. Our analysis revealed a high probability of ECF extinction if the disease emerges from recovered carrier cattle. Finite time to ECF disease extinction is estimated using 10,000 sample paths, and it is shown that the epidemic duration is shortest if the disease is introduced by infectious cattle. The epidemic duration is longest when the disease is introduced by infectious ticks. Additionally, we observed that host interactions at the wildlife–livestock interface play a critical role in shaping ECF transmission and informing control strategies. Full article

Metabolic dysregulation is increasingly recognized as a central feature linking chronic infection, immune dysfunction, and skeletal deterioration; however, these processes are most often investigated in isolation, limiting the development of integrative mechanistic frameworks. In this review, we propose the Metabolic Reprogramming Interface Model (MRIM) as a systems-level, hypothesis-generating construct that conceptualizes metabolism as a shared regulatory axis bridging host–pathogen interactions and bone microenvironment remodeling. Importantly, MRIM is not presented as a unified or experimentally validated disease model, but rather as a structured framework designed to organize and critically evaluate emerging multidisciplinary evidence. At the molecular level, metformin, a widely used metabolic modulator, has been shown to influence mitochondrial bioenergetics, AMP-activated protein kinase (AMPK) signaling, redox balance, and autophagic pathways, all of which are independently implicated in microbial persistence, immune cell function, and skeletal homeostasis. Within MRIM, these observations are integrated to hypothesize that metabolic perturbation may coordinately influence infection dynamics, inflammatory responses, and bone turnover. Nevertheless, most of the supporting evidence remains indirect, arising from in vitro studies, animal models, and observational clinical datasets, thereby limiting causal inference. To address this, the framework explicitly distinguishes between experimentally validated mechanisms, context-dependent biological interactions, and higher-order theoretical integrations. While preliminary findings suggest that metformin may modulate microbial fitness, attenuate excessive inflammation, and influence bone remodeling, these effects appear to be highly context-dependent and have not yet been substantiated in adequately powered prospective clinical trials evaluating combined infectious and skeletal outcomes. This review therefore provides a critical synthesis of current knowledge, highlights key mechanistic and translational uncertainties, and outlines testable hypotheses for future investigation, positioning MRIM as a conceptual scaffold to guide interdisciplinary research rather than a definitive explanatory model. Full article

Brucellosis remains a significant public health and economic burden in many regions, primarily transmitted from livestock to humans through direct contact and environmental contamination. In this paper, we develop a novel cross-species epidemic model that couples the transmission dynamics of brucellosis among sheep, humans, and the environmental reservoir of Brucella. The sheep population is divided into susceptible, exposed, infectious, and vaccinated compartments, while the human population is stratified into susceptible and infected classes. Environmental brucella load is explicitly modeled, and distributed time delays are incorporated to account for incubation periods and delayed exposure risks in humans. We prove that all solutions are non-negative and ultimately bounded, ensuring biological consistency. The basic reproduction number ${\mathcal{R}}_0$ is derived using the next-generation matrix method. Using Lyapunov functionals and LaSalle’s invariance principle, we establish that the disease-free equilibrium is globally asymptotically stable when ${\mathcal{R}}_0\le 1$, whereas a unique endemic equilibrium exists and is globally asymptotically stable when ${\mathcal{R}}_0>1$. Sensitivity analysis identifies the environmental transmission rate, shedding rate, and disinfection as the most influential parameters. Treatment efficacy is shown to exhibit a critical threshold $p^{\mathrm{cr}}=1-1/{\mathcal{R}}_0$, above which eradication becomes feasible. Numerical simulations validate the theoretical findings and demonstrate that time delays affect outbreak timing but not asymptotic stability. These results provide quantitative guidance for brucellosis control strategies, emphasizing environmental sanitation, culling, and vaccination as key interventions. Full article

Hantaviruses are emerging zoonotic pathogens responsible for two severe clinical syndromes: (i) haemorrhagic fever with renal syndrome (HFRS) and (ii) hantavirus cardiopulmonary syndrome (HCPS), collectively causing more than 200,000 human cases annually worldwide. Despite their public-health importance, the molecular mechanisms governing the host response and the population-level dynamics of rodent-to-human spillover remain incompletely characterised. The timeliness of this framework is underscored by the April–May 2026 outbreak of Andes orthohantavirus aboard the MV Hondius cruise ship, the first such cluster in a maritime setting, with three deaths reported across multiple countries. This event revealed critical gaps in existing models that treat humans solely as dead-end spillover hosts. Our coupled Susceptible-Exposed-Infectious-Recovered-Dead (SEIRD) model assumes no human-to-human transmission and is therefore designed for hantavirus strains where spillover does not lead to secondary human cases, specifically Hantaan virus (HTNV), Puumala virus (PUUV), Sin Nombre virus (SNV), and Dobrava-Belgrade virus (DOBV). The Andes virus (ANDV) outbreak aboard the MV Hondius is used as a real-world case study to assess the boundaries of our model and to motivate future extensions, not as a direct validation target for its quantitative predictions. Here, we present an integrated computational study combining three complementary analyses. First, we performed a preliminary phylogenetic analysis of the viral sequence, identifying Orthohantavirus andesense as the likely etiological agent responsible for the vessel-associated outbreak. Second, we carried out a downstream transcriptomic analysis of Hantaan virus (HTNV)-infected human umbilical vein endothelial cells (HUVECs), using publicly available RNA-seq data (GEO accession GSE133751, $n=3$ per group). This analysis identified 184 upregulated and 19 downregulated genes, highlighting a transcriptional response dominated by interferon-stimulated genes (ISGs), including CXCL10, CXCL11, MX2, DDX58, IRF7, STAT1, OASL, and CMPK2. We then constructed a protein–protein interaction (PPI) network using STRING, comprising 176 nodes and 3210 edges, and applied a composite network centrality score to rank putative regulatory hubs. This analysis identified ISG15, IRF1, CXCL10, STAT1, and DDX58 as the most central nodes. Pathway enrichment analysis confirmed a strong activation of interferon signalling (Reactome, $p=1.3\times {10}^{-63}$), antiviral defence mechanisms (Gene Ontology, $p=3.8\times {10}^{-58}$), and NF-$\kappa$B-related pathways, together with a concurrent suppression of ribosomal translation. Finally, we developed a coupled SEIRD epidemiological model that explicitly represents rodent-to-rodent and rodent-to-human transmission with logistic rodent population growth. Preliminary simulation analysis demonstrates that reducing human exposure to rodent excreta is substantially more effective than rodent population control alone for reducing human disease burden, and that rodent control in isolation can paradoxically increase human cases through a dilution-like effect. The integrated framework provides molecular and epidemiological insights relevant to hantavirus surveillance, therapeutic target identification, and public-health intervention design. Full article

Background/Objectives: Clinical surveillance of infectious diseases caused by viruses, such as SARS-CoV-2, is important for effective intervention and preventing potential epidemics or pandemics. The development of cost-effective whole genome sequencing technologies has facilitated worldwide efforts to sequence viral genomes. The array of sequence data generated across the globe offers diverse opportunities to study SARS-CoV-2 evolutionary dynamics and serves as a foundation for different research questions in the future. Even though bioinformatics tools are rapidly developed for accessing and analyzing large-scale data from public repositories, surveillance labs lack streamlined pipelines to handle high sample volumes and efficiently identify mutations for variant reporting with minimal computational expertise. Methods: We have developed a SARS-CoV-2 mutational analysis pipeline using Workflow Description Language (WDL), which is open-source and combines various steps in an analysis workflow with human-readable syntax. Thus, users with minimal informatics background can easily adapt the workflow while creating a local data repository within their institution. The pipeline processes input FASTA files and quality control files from Ion Torrent S5, performs clade and variant assignments, integrates patient metadata, and stores the results into a REDCap database. Results: In this framework, REDCap acts as the core data backbone for run-level tracking and result storage. To further enhance the utility of our REDCap-based data capture system, we have developed an intuitive interactive dashboard. This interface seamlessly connects with the REDCap data sources, providing real-time monitoring, interactive visualization, and the ability to create a consolidated variant report. Conclusions: Our overall approach streamlines processes in managing complex genomic data and offers easy adaptation to empower other molecular labs. Full article

The concurrent circulation of SARS-CoV-2 and influenza presents a complex immunological landscape. While biological evidence suggests that prior or current infection with one virus can alter susceptibility to the other, conventional epidemiological models often obscure these effects by aggregating co-infected populations into a single compartment. This structural simplification limits our ability to quantify how infection history shapes population-level transmission dynamics. We developed a stratified, deterministic co-infection model that explicitly distinguishes between single, concurrent, and sequential infections by accounting for sequence-dependent heterogeneity in susceptibility and transmissibility. Our primary innovation is a transmission source–pathway decomposition framework that mathematically attributes the rate of new infections to its specific transmission source (i.e., which infectious subpopulation is generating the transmission: the singly-infected, co-infected, or sequentially-infected class) and transmission pathway (i.e., which susceptibility class is receiving new infections: fully susceptible individuals, or those with prior immunity, or those with active co-infection). This framework accounts for altered susceptibility and transmissibility dependent on infection history. Our model-based analysis reveals a profound, sequence-driven asymmetry in transmission. In a baseline co-epidemic scenario, COVID-19 is predominantly driven by a sequential source: individuals who contracted COVID-19 after recovering from influenza are estimated to account for approximately 73% of new COVID-19 cases and approximately 76% of the disease burden, as predicted by our model. Conversely, influenza transmission remains driven by singly infected individuals (approximately 96% of new influenza cases inferred using our model). This sequence-driven asymmetry was robust to changes in model structure (especially, the inclusion of an influenza latent period in a sensitivity analysis) and across scenarios of varying relative transmissibility for the two viruses. Interventions exhibit pathway-specific effects: COVID-19 vaccination, for instance, disproportionately disrupts this dominant sequential transmission engine by protecting the most immunologically vulnerable hosts. Our model-based findings suggest that infection history may be a primary driver of co-epidemic dynamics. Our framework reveals a plausible, asymmetric interaction where an initial influenza wave can fundamentally reshape the transmission landscape for COVID-19, and demonstrates how a prior COVID-19 wave may fuel subsequent influenza transmission under specific temporal conditions. These findings generate the testable hypothesis that cross-viral susceptibility is a key control point and underscore the importance of pathway-aware intervention strategies that account for infection history. Full article

This study examines how urban transportation systems influence the spatial spread of infectious diseases by developing a modified Susceptible–Exposed–Infected–Recovered (SEIR) model with explicit intercity travel dynamics. The model distinguishes between two mobility mechanisms: travel volume, represented by the departure rate g, and travel speed, represented by the arrival rate $\alpha$. Using the next-generation matrix (NGM) approach, we derive the basic reproduction number $R_0$ and analyse how within-city and transit-phase transmission contribute to epidemic spread. The results show that travel volume and travel speed affect mobility-driven transmission through distinct mechanisms. Increasing g increases the number of travelers entering the transit system and therefore amplifies the aggregate number of transit-mediated infections, although the per-capita transit reproduction expression is governed primarily by $\alpha$ and ${\beta }_d^T$ under the reduced next generation matrix formulation formulation. By contrast, increasing $\alpha$ shortens the time spent in transit, reduces the exposure window during travel, and lowers the per-capita contribution of transit-based infection to $R_0$. Numerical simulations illustrate these effects and support the conclusion that reducing travel volume can mitigate intercity epidemic spread by decreasing the number of potentially exposed travelers. Comparative case studies for Brazil, New Zealand, China, and Algeria are used to evaluate the model under different epidemiological settings and socioeconomic contexts. These socioeconomic indicators are treated as contextual background rather than as direct inputs to the mathematical model. The qualitative predictions of the ordinary differential equation (ODE) model are further cross-validated using an agent-based simulation implemented in NetLogo. Overall, the study shows that separating travel volume from travel speed provides a more precise understanding of mobility-driven disease transmission and can support the design of targeted travel-related control measures. Full article

Background/Objectives: The airway epithelium plays a central role in host defense, inflammatory signaling, and disease progression across infectious, inflammatory, and genetic respiratory disorders. Human nasal epithelial organoids have emerged as accessible and patient-specific in vitro platforms with increasing translational relevance. This systematic review aimed to critically evaluate the current evidence on nasal epithelial organoid models, focusing on donor characteristics, culture methodologies, differentiation strategies, and translational applications. Methods: A systematic search of PubMed/MEDLINE, Embase, Scopus, Ovid MEDLINE, and Cochrane Library was conducted for studies published between 1990 and April 2026. The review followed PRISMA guidelines and was structured according to the PICOTS framework. Eligible studies included in vitro experimental investigations using human-derived nasal epithelial organoids in infectious, inflammatory, or precision medicine contexts. Risk of bias was assessed using the QUIN tool. Results: Seventeen studies met the inclusion criteria. Applications clustered into three principal domains: infectious disease modeling, inflammatory and epithelial remodeling research, and cystic fibrosis precision medicine. Most studies employed expandable three-dimensional Matrigel-embedded organoids or organoid-derived air–liquid interface systems. Infection-focused studies demonstrated variant-specific viral replication dynamics and epithelial immune responses, while inflammatory models reproduced disease-associated differentiation and remodeling phenotypes. Cystic fibrosis oriented studies showed that organoid swelling and electrophysiological assays correlate with CFTR functional rescue and, in selected cases, clinical response. Methodological heterogeneity across protocols and outcome reporting precluded quantitative synthesis. Conclusions: Human nasal epithelial organoids represent versatile translational platforms bridging accessible patient-derived tissue and advanced airway disease modeling. Although variability in culture protocols and functional benchmarks limits standardization, these models hold significant promise for mechanistic investigation, therapeutic stratification, and precision medicine applications. Full article

Background and Objectives: New Caledonia, an archipelago in the South Pacific, experienced an unprecedented conjunction of prolonged border closure during the COVID-19 pandemic (2020 to 2022) and marked influence of the El Niño/Southern Oscillation (ENSO). This context provided a unique opportunity to explore how environmental drivers, island isolation, and socio-demographic factors interact to shape infectious disease dynamics. This study aimed to assess the respective and combined effects of climatic variability, travel restrictions, and socio-demographic factors on the dynamics of four priority infectious diseases. Materials and Methods: We retrospectively analysed data from 2017 to 2023 on four infectious diseases: leptospirosis, dengue, influenza, and hepatitis A (HAV). Satellite precipitation data and the Multivariate El Niño/Southern Oscillation Index (MEI) were used. Socio-demographic and economic variables were gathered. Statistical analyses employed descriptive analysis and Generalized Additive Mixed Models to evaluate the associations between climatic events, travel restrictions, and disease circulation using the communal level as a random effect and time (daily) as a spline effect. Results: We analysed 878 cases of leptospirosis, 165 of HAV, 6607 of influenza, and 7377 dengue cases. Influenza was associated with rainfall before lockdown (Odds Ratio (OR) 0.7, Confidence interval 95%, (CI95%), (0.6–0.8)) and disappeared during lockdown but resurged post-reopening losing its meteorological association. Dengue epidemics declined, coinciding with the Wolbachia program and border closure, and were associated with lower MEI (OR 0.78, CI95% (0.6–1) during the 2017 to 2020 period. HAV cases were correlated with the MEI (OR: 1.8, CI95% (1–3.3)). Leptospirosis cases were associated with cumulative rainfall (OR 1.12 (1.1–1.2)) and lower education (OR 1.04, CI95% (1–1.1)) and decreased with water supply (OR 0.7, CI95% (0.5–0.8)). Conclusions: Our findings highlight how climatic conditions, mobility restrictions, and socio-environmental inequities differentially shape infectious disease risks in island ecosystems. These results reinforce the need for integrated One Health surveillance that jointly addresses environmental change, social vulnerability, and infectious disease prevention. Full article

Infectious disease transmission is a complex dynamic process governed by intrinsic causal mechanisms rather than simple statistical correlations. Although deep learning paradigms have demonstrated powerful nonlinear representation capabilities, their “black-box” and purely data-driven nature often lead to a severe lack of causal consistency and logical transparency. To bridge this gap, this paper proposes CCSANet (Causally Constrained SEIR-Aware Network), an interpretable forecasting framework that seamlessly embeds epidemiological priors directly into the neural architecture. The model integrates SEIR dynamics into a temporal causal discovery framework, utilizing a mechanism-aware prior loss to guide a CausalFormer in learning a global temporal causal graph from multi-source heterogeneous data. This ensures that the identified relationships strictly adhere to the fundamental evolutionary logic of contagion. Subsequently, the extracted causal subgraphs are encoded as structural priors within a Causal-SCI-Block via a specialized masking mechanism, effectively forcing information to propagate exclusively along epidemiologically legitimate pathways. To ensure deep alignment between neural representations and physical reality, a causal strength alignment loss is introduced to synchronize the network’s attention weights with actual transmission intensities. Experimental evaluations on real-world multi-city datasets demonstrate that this integrated approach significantly outperforms baselines such as LSTM, Informer, and its predecessor, ESASNet. Under a 7-day sliding window configuration, the model maintains a Coefficient of Determination $R^2$ stably above 0.97, achieving an accuracy improvement of 5.5% to 6.2% and an 8% to 10% reduction in SMAPE, thereby demonstrating that coupling causal discovery with SEIR constraints substantially enhances both predictive precision and physical interpretability. Full article

Background/Objectives: Sepsis is a dynamic clinical syndrome characterized by a rapidly evolving inflammatory response, where early identification of patients at risk for adverse outcomes remains a major challenge. While inflammatory biomarkers are widely used, their prognostic value at baseline is limited. This study aimed to evaluate whether early changes in inflammatory biomarkers, particularly the neutrophil-to-lymphocyte ratio (ΔNLR), provide additional prognostic value in predicting short-term outcomes in patients with sepsis. Methods: A retrospective longitudinal observational study was conducted, including 168 adult patients admitted with sepsis at a tertiary infectious diseases hospital. Inflammatory biomarkers (CRP, procalcitonin, leukocyte subpopulations, and NLR) were assessed at admission and at 48–72 h. Early changes (Δ values) were calculated and analyzed in relation to a composite adverse outcome, including ICU admission, vasopressor requirement, mechanical ventilation, or in-hospital mortality. Logistic regression and ROC curve analyses were used to evaluate predictive performance. Results: Patients with adverse outcomes had significantly higher baseline inflammatory markers and severity scores. Early reductions in CRP and NLR were more pronounced in survivors, whereas non-survivors showed persistently elevated or minimally decreasing values. In multivariate analysis, ΔNLR remained independently associated with in-hospital mortality (OR 0.91, 95% CI 0.84–0.98, p = 0.015), alongside Sequential Organ Failure Assessment (SOFA) score and septic shock. ΔNLR demonstrated better discriminative performance (AUC 0.74) compared to baseline markers and improved predictive accuracy when combined with SOFA score (AUC 0.81). Higher baseline NLR quartiles were associated with a stepwise increase in adverse outcomes. Conclusions: Early changes in inflammatory biomarkers, particularly ΔNLR, provide clinically relevant prognostic information beyond baseline measurements and severity scores in sepsis. Dynamic assessment of immune response may improve early risk stratification and support more individualized clinical decision-making. Full article

Microorganisms can aggregate and organise into structured communities embedded within an exopolysaccharide-based matrsix, which serves as a protective barrier and a functional environment around microbial cells. The formation of biofilms is widely recognised as a pivotal factor in bacterial virulence, impeding the efficacy of antimicrobial agents and hindering immune responses, whilst concomitantly contributing to the development of antimicrobial resistance and the onset of persistent infections. Biofilm formation is a tightly regulated and dynamic process, controlled by quorum-sensing mechanisms and profoundly influenced by environmental factors and nutrient availability. The objective of this review is to elucidate the significance of biofilms in clinical settings, with a particular focus on their role in the pathogenesis of infectious diseases. Particular attention is devoted to biofilm-associated infections and infections related to invasive medical devices, with a particular emphasis on the most prevalent microbial pathogens, which include S. aureus, S. epidermidis, P. aeruginosa, E. coli, K. pneumoniae, A. baumannii and various species of Candida. Furthermore, the present review encompasses biofilm-associated chronic infections, conditions manifesting in predisposed patients, including individuals affected by cystic fibrosis. This review further examines the most recent strategies for combating antibiotic resistance in bacterial biofilms. This review focuses on recent biofilm pathogenesis advancements, with a focus on diagnosis challenges and the need for new ways to disrupt biofilm integrity. Full article