Volume 08 · Issue 02 · 2026

ESKAPE, including Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp. pose a significant global health threat due to their extensive drug resistance and rapid evolutionary capacity. This article advocates a paradigm shift from conventional antibiotic warfare to "ecological co-management" , integrating four innovative strategies plus an overarching framework: (1) microbiome-based interventions using probiotics, phages, and niche modulation; (2) a One Health 2.0 framework that incorporates wastewater surveillance and artificial intelligence (AI)-driven stewardship; (3) evolutionary constraint methods including anti-virulence agents and clustered regularly interspaced short palindromic repeats (CRISPR)-based targeting; (4) metabolic pathway intervention and antimetabolite therapy such as biotin biosynthesis inhibition; and (5) the establishment of a clinical translation and risk management system for innovative antimicrobial strategies. These multidimensional efforts aim to disrupt resistance transmission, restore antibiotic susceptibility, and promote sustainable pathogen control through ecological and systems-level integration.

Seasonal influenza activity was disrupted due to the emergence of coronavirus disease 2019 (COVID-19) and the implementation of non-pharmaceutical interventions. In this study, we analyzed weekly influenza-like illness (ILI) cases and influenza positivity rate by subtype and age group from the Shanghai influenza surveillance system from 2015 to 2023. A wavelet analysis was conducted to explore the seasonal pattern of influenza. The average weekly ILI consultation rate was 20.28 ILI cases per 1,000 consultations, with 40,778 of 173,155 specimens (22.63 %) testing positive for influenza. During the COVID-19 pandemic, nearly no influenza viruses were detected until January 2021. Only B/Victoria was circulated for about eight months and peaked in January 2022, with a positivity rate of 47.48 %, which was lower than the peaks in each year before the COVID-19 pandemic. In 2023, the first epidemic, dominated by A (H1N1), began in week 8 (one month later than the 8-year average) and peaked at 76.89 %. The second epidemic, which was unusually not summer-based and driven by A(H3N2), started in week 38 and peaked in week 48 at 75.31 %, advancing the winter-spring epidemic by approximately one month. The age preference of influenza A changed during the era of normalized COVID-19 transmission. The seasonal patterns of influenza varied significantly across different stages of the COVID-19 pandemic, including differences in epidemic onset, peak timing, duration, periodicity, and dominant strains. Stability of seasonal changes in influenza requires longer-term surveillance, as well as further virologic and epidemiologic studies.

Reconstructing the early spatiotemporal dynamics of emerging infectious diseases (EIDs) is essential for effective public health response but remains difficult due to reporting delays, heterogeneous surveillance systems, and cryptic transmission chains. This study proposes a systems-oriented computational framework that tackles these challenges through three key innovations. First, we develop a stochastic infectious disease model tailored to limited early-stage case counts, grounded in a simplified metapopulation structure that enables accurate reconstruction of initial outbreak conditions while maintaining computational efficiency comparable to existing methods. Second, we introduce a matrix-based algorithm for calibrating reporting delays in spatial metapopulation models. By leveraging matrix operations to synchronize case-report updates across multiple regions, the method eliminates the need for traditional iterative traversal, thereby achieving substantial gains in computational efficiency and improving its practical utility in engineering applications. Third, leveraging complex network theory, we develop a parameter estimation framework using open-source algorithm libraries from the Medical Research Council Centre for Global Infectious Disease Analysis (MRC GIDA), achieving more than a tenfold increase in estimation efficiency for individual cities with populations exceeding one million. Validation using both simulated networks and empirical Chinese urban mobility networks covering early coronavirus disease 2019 (COVID-19) transmission scenarios demonstrates that the proposed approach substantially improves parameter estimation efficiency while ensuring robustness and accuracy. This framework provides a powerful tool for rapid, high-fidelity reconstruction of epidemic dynamics, enabling more informed responses to future public health emergencies.

Nocardiosis is an uncommon infection that can cause severe damage or be life-threatening. However, the epidemiology and strain distribution of nocardiosis in China remain poorly characterized. In this study, a comprehensive literature search was conducted using the keyword "('Nocardia’ OR 'Nocardiosis’) AND ('China’ OR 'Chinese’)" in PubMed and Web of Science, and "(Nuokajun) OR (Nukajun)" in the China National Knowledge Infrastructure (CNKI) database. All published cases of nocardiosis between April 1, 1990, and September 13, 2024, were included. Geographic analysis identified a concentration of cases in eastern coastal China. The mean patient age was 52.6 ± 17.4 years, with a male predominance (61.0%) and the highest burden in the 50–59 age group (24.4%). The primary infection sites were the lungs (69.9%), skin (30.3%), and brain/central nervous system (CNS) (19.3%). Disseminated infections accounted for 29.0% of all cases and were associated with a markedly higher mortality rate (16.1%) compared with the overall rate (9.0%), underscoring the importance of early diagnosis and treatment. The predominant species were Nocardia farcinica, Nocardia cyriacigeorgica, and Nocardia brasiliensis. Most isolates remained highly susceptible to trimethoprim-sulfamethoxazole, amikacin, and linezolid. This study has established a national epidemiological pattern of Nocardia infections and strain distribution in China. The elevated risk and mortality associated with disseminated infections warrant vigilance. This underscores the need for heightened clinical awareness of Nocardia infections and ensure timely screening and treatment.

Tuberculosis (TB) vaccine design relies on the selection of optimal antigens. However, evidence on candidate antigens is scattered across the literature and rarely linked to critical vaccine parameters, such as immune responses, antigen combinations, and adjuvants. Here, we introduce Mycobacterium tuberculosis (MTB)-ImmunogenKG, a provenance-linked, antigen-centric knowledge graph for MTB, that is constructed via an optimized information-extraction pipeline and integrated with a large language model (LLM) system to enable knowledge-augmented reasoning. From over 77,000 publications indexed in PubMed (as of July 2024), this graph consolidates 1.48 million sentence-level statements spanning 14 entity types. In practical applications, MTB-ImmunogenKG enables contradiction-aware antigen profiling for 3,154 proteins (representing about 77% of the total annotated proteins) and improves protective-efficacy prediction with a Matthews correlation coefficient (MCC) gain of 0.19 over sequence-based tools and 0.45 over an LLM-only baseline. By delivering a traceable synthesis of fragmented findings, MTB-ImmunogenKG facilitates the construction of antigen panels and the pairing of adjuvants, thereby streamlining the experimental design cycle for next-generation TB vaccines.

The World Health Organization declared mpox a public health emergency of international concern in both 2022 and 2024, highlighting the critical need for rapid and reliable diagnostic solutions. To address this challenge, we developed monoclonal antibodies against four mpox virus (MPXV) antigens (A29L, A35R, H3L, and E8L) using hybridoma technology. Epitope binning analyses, performed using competitive enzyme-linked immunosorbent assay (ELISA) and biolayer interferometry, identified non-overlapping antibody pairs (e.g., 18F7–27C3 for A29L and 4E7–6C11 for A35R), which served as the foundation for sandwich ELISA assays exhibiting nanogram-level sensitivity. These antibody pairs demonstrated high specificity, effectively distinguishing MPXV antigens from homologous proteins of cowpox virus, vaccinia virus, and variola virus, while maintaining reactivity toward cultured MPXV. Collectively, this work establishes a robust immunodiagnostic platform with strong translational potential for point-of-care applications during mpox outbreaks.

Since the 2022 global mpox outbreak, the lack of specific antibodies for mpox virus (MPXV) detection has hindered precise immunoassays due to cross-reactivity among Orthopoxviruses (OPXVs). This study developed and characterized two monoclonal antibodies (mAbs), CML01 and CML02, targeting the MPXV A35 protein, a conserved surface antigen of extracellular virions. Cross-reactivity assessments via enzyme-linked immunosorbent assay (ELISA), western blot, and indirect immunofluorescence assays (IFA) confirmed that both mAbs bound exclusively to MPXV A35, showing no reactivity with homologous proteins from cowpox (A34), vaccinia (A33), or variola viruses (A36) despite 92.3 %–96.1 % sequence homology. IFA showed recognition of MPXV-infected cells with half-maximal effective concentrations (EC50) of 0.15 and 0.17 μg/mL, respectively. Notably, an IFA-based microneutralization assay using the mAb CML02 exhibited strong correlation (r = 0.93, P < 0.0001) with the traditional plaque reduction neutralization test (PRNT) while enabling higher throughput. Plasma from convalescent mpox patients validated the assay’s utility in testing neutralizing antibody titers. These mAbs address critical gaps in MPXV-specific immunological testing by virtue of their high specificity, which prevents cross-reactivity with other OPXVs and eliminates interference from immunity induced by smallpox vaccination. This work underscores A35 as a key epitope for MPXV-specific immunity and provides essential tools for combating the ongoing mpox threat.

Yellow fever (YF) is an epidemic disease caused by the yellow fever virus (YFV). Historically, it has caused several epidemics and continues to result in fatalities in South Sudan and other regions today. Due to its limited therapeutic options, high mortality rate, and high transmissibility, YFV is classified as a biosafety level-3 (BSL-3) pathogen. The development of a rapid and sensitive YFV detection method is therefore important for epidemic prevention and response. Herein, we combined the clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated proteins (Cas) 13a system, reverse transcription recombinase-aided amplification (RT-RAA), and the easy-readout, sensitive enhanced lateral flow strip (ERASE LFS) to establish a new detection method for YFV. YFV ribonucleic acid and infectious YFV 17D particles were effectively detected using this approach. The RT-RAA-CRISPR-ERASE LFS (RCE) assay for YFV RNA demonstrated a detection sensitivity of 100 copies/μL with no cross-reactivity observed with five other common flaviviruses. The lyophilized RCE kit successfully detected YFV 17D in spiked human serum samples at a titer of 102 plaque-forming units (PFU)/mL. Furthermore, the optimized RCE reaction required only 35 min with a portable heat block. The RCE assay we established enabled rapid and sensitive on-site detection of YFV, holding substantial biosecurity significance for resource-limited regions with inadequate healthcare infrastructure.