2025年 · 第07卷第06期

抗生素耐药性(AMR)的出现和加速上升在全球范围内构成了严重的公共卫生威胁，需要创新的方法来应对这种传播[1].最近，Y.Wang等人。[2]报告了一份全面的全球AMR地图集沙门氏菌，汇编了来自148个国家/地区的208,233个基因组的广泛数据集，涉及九个主要来源和跨越一百年的主要血清型。他们的分析强调了AMR患病率的显著增加，具有显著的地理、来源特异性和血清型相关差异，为全球AMR缓解工作提供了重要的见解。在这篇评论中，我们旨在强调Y.Wang等人的工作的全球意义，并提出将基因组监测纳入公共卫生战略的未来方向。

Chikungunya virus (CHIKV), a mosquito-borne alphavirus first identified in Tanzania in 1952, has expanded to more than 100 countries posing increasing global health risks. The 2025 epidemic in Réunion Island and local transmission in Foshan City, Guangdong Province, China, illustrated the growing risk of global dissemination. For primary vectors Aedes aegypti and Aedes albopictus, their behavioral traits (such as daytime biting, skip oviposition, and multiple-host feeding) substantially increased transmission potential and complicate control. Two vaccines, IXCHIQ^® (live-attenuated) and VIMKUNYA^TM (virus-like particle), have been licensed in Europe and the United States, and multiple candidates including inactivated, subunit, viral-vectored, and messenger ribonucleic acid (mRNA) vaccines are under development. This review summarized current knowledge on CHIKV virology, epidemiology, evolution, vaccines, and vector control, to provide insights for effective management of this re-emerging arboviral threat.

With the global warming, the expansion of transportation networks, a tremendous increase in international travel and exchange, and the ongoing evolution of viruses, chikungunya virus (CHIKV) has spread beyond its African origins and achieved global distribution, posing a significant threat to public health worldwide. Under such circumstances, animal models serve as indispensable tools for elucidating CHIKV pathogenesis and developing antiviral strategies. The currently established animal models of CHIKV infection can recapitulate various aspects of the clinical disease at different levels of complexity. However, each model possesses distinct advantages and limitations, rendering them suitable for rather specific research applications. Furthermore, the clinical realities of CHIKV infection in patients with comorbidities or coinfections with other viruses, coupled with emerging initiatives to reduce animal model reliance, present substantial challenges for the future development and application of these models. This review summarizes the natural history and susceptible host of CHIKV, recent advances in understanding the pathogenesis and animal model development, and prospects for animal models of CHIKV infection. The aim is to provide a reference for the selection, utilization, and development of appropriate animal models for CHIKV research.

The Rhabdoviridae family comprises a diverse range of negative-sense single-stranded ribonucleic acid (RNA) viruses, including significant human and mammalian viruses transmitted by various arthropod species. Herein, using Aedes albopictus (Ae. albopictus) samples collected in two urban parks during 2023 and 2024, through metagenomics sequencing, 16 sequences were identified as putative novel viruses, showing closest homology to insect-specific viruses, mycoviruses, or plant-associated viruses. Notably, two novel viruses, Aedes albopictus almendravirus GCCDC15 (Aealb-AlmV GCCDC15) and Aedes albopictus almendravirus GCCDC16 (Aealb-AlmV GCCDC16) were identified and successfully isolated. Both of these viruses belong to the genus Almendravirus within the Rhabdoviridae family. Phylogenetic analysis revealed that Aealb-AlmV GCCDC15 and GCCDC16 are distantly related to Coot Bay virus (the United States of America, 2013) and Menghai rhabdovirus (Yunnan Province, China, 2017). The genetic distances between these two viruses and their most similar viruses are marked by 59.85 % and 87.20 % of amino acid identity in the L protein, respectively, supporting their classification as two new species in the Rhabdoviridae family. Cytopathic effects and rod-like virions were observed in mosquito cells (C6/36) after inoculating with supernatants from the Ae. albopictus samples. To investigate the natural distribution and persistence of the novel almendraviruses, we conducted a specific reverse transcription-polymerase chain reaction (RT-PCR) screening of Ae. albopictus mosquitoes collected from two urban parks across different time points. The assays confirmed the presence of both Aealb-AlmV GCCDC15 and GCCDC16 in mosquito populations. Critically, these viruses were detected repeatedly over successive sampling periods and in mosquitoes from geographically distinct sites within the urban environment. In summary, our study delineates the virome characteristics of Aedes mosquitoes in the urban ecosystem and successfully isolated two novel rhabdoviruses. The recurrent detection provides clear evidence for the sustained circulation of Ae. albopictus-derived almendraviruses in urban parks, highlighting their ongoing transmission and establishment in these habitats.

To investigate the clinical characteristics of coronavirus disease 2019 (COVID-19) infection in patients with chronic hepatitis B (CHB) during interferon antiviral therapy and to explore the correlation between interferon use and COVID-19 infection and clinical indicators in these patients. A retrospective study was conducted on 477 Patient with CHB who visited the Second Hepatology Department of Ditan Hospital from December 2022 to February 2023. Patients were divided into an interferon group and a nucleoside analogue group based on whether they received interferon treatment. COVID-19 infection and fever duration were the primary indicators, while blood routine and liver function were the secondary indicators. Differences in COVID-19 infection rate, fever duration, and related laboratory tests between the two groups were compared. There were 184 patients in the interferon group and 293 patients in the nucleoside analogue group. The COVID-19 infection rate was 73.91 % (136/184) in the interferon group and 92.15 % (270/293) in the nucleoside analogue group, with a statistically significant difference (χ² = 29.67, P < 0.001). After COVID-19 infection, the fever duration was shorter in the interferon group than in the nucleoside analogue group, with a statistically significant difference (χ² = 130.15, P < 0.001). Logistic regression analysis showed that interferon use was an independent influencing factor for COVID-19 (odds ratio = 0.25, 95 % confidence interval: 0.14–0.43, P < 0.001). Compared with the nucleoside analogue group, the levels of white blood cells, neutrophils, lymphocytes, platelets, and aminotransferases were significantly different in the interferon group (P < 0.05). There were no differences between the two groups in creatinine and cardiac enzymes (P > 0.05). Interferon therapy can reduce the COVID-19 infection rate in patient with CHB and shorten the fever duration to a certain extent.

Understanding the mechanisms of drug resistance in Mycobacterium tuberculosis (MTB) is essential for the rapid detection of resistance and for guiding effective treatment, ultimately contributing to reducing the global burden of tuberculosis (TB). Under anti-TB drugs pressure, MTB continues to accumulate resistance loci. The current repertoire of known resistance-associated mutations requires further refinement, necessitating efficient methods for the timely identification of potential resistance sites. Here, we introduce xAI-MTBDR, an explainable artificial intelligence framework designed to identify potential resistance-associated mutations and predict drug resistance in MTB. It outperforms state-of-the-art methods in predicting drug resistance for all first-line drugs, and scoring each mutation’s contribution to resistance. By leveraging public whole-genome sequencing data from nearly 40,000 MTB isolates, the framework identified 788 candidate resistance-related mutations and revealed 27 potential resistance markers, several of which are positioned closer to their respective drugs in protein structures than known resistance mutations, suggesting a potentially more direct role in mediating resistance. Furthermore, these scores enabled the framework to efficiently subgroup isolates with different resistance mechanisms and reflect varying levels of resistance. The framework serves as a valuable tool for accurate detection of drug-resistant MTB and offers new insights into its underlying mechanisms.

The explosive growth during the early stages and the sustained transmission in the later phases of the coronavirus disease 2019 (COVID-19) pandemic may be closely linked to superspreading events (SSEs), yet in-depth research into their specific mechanisms and quantitative effects remains limited. This study, based on data from 4,519 COVID-19 cases across eight regions in China, reconstructed transmission chains and quantified key parameters such as the basic reproduction number (R₀) and dispersion parameter (k), revealing a high degree of heterogeneity in COVID-19 transmission. The results showed that the majority of COVID-19 cases were mild, with female cases in some regions being significantly older than males. Epidemic curves were highly similar in geographically proximal areas, with the longest transmission chain reaching nine generations. The transmission parameters revealed a serial interval of 1.27–4.71 days, R₀ ranging from 0.87 to 2.65, and k values between 0.50–2.04, demonstrating that super-spreaders serve as critical drivers of epidemic spread. We found that 1.35 % of cases identified as super-spreaders directly responsible for 40.09 % of secondary cases. Occupationally, students and catering staff were identified as high-risk groups for super-spreading. Geographically, household or community transmission served as the main driver of SSEs in six regions, while school-based transmission dominated in one region. These findings provide crucial scientific evidence for advancing our understanding of COVID-19 transmission dynamics and informing precision prevention strategies.

To address the challenge of tracking large-scale viral evolutionary history, this study introduces TempSnap-Trace, a novel computational framework designed for this purpose. The methodology processes genomic data to generate variant-featured haplotype strings. Subsequently, the minimum-cost arborescence network (McAN) algorithm is employed to infer phylogenetic relationships, from which weighted temporal snapshot networks are constructed. Core evolutionary nodes are then identified using community detection approach. This method integrates mutation sites, network topology, and directional information to reconstruct backbone evolutionary pathways based on inter-community similarity over time. Compared to community detection methods based on unweighted graphs, this approach increased modularity by 17.4 % and reduced code length by 30.1 %. This approach was validated on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) data, where it successfully identified the French B.1 lineage as a key transmission hub and accurately traced the backbone evolutionary paths of major variants, including Beta (B.1.351) and Zeta (P.2). Its utility was further demonstrated by delineating the evolutionary trajectory of the Mpox IIb B.1 variant. Furthermore, through extensive parallelization and algorithmic optimizations, the framework exhibits exceptional computational efficiency and scalability: in benchmark tests against the viral genome evolutionary analysis system (VENAS) workflow, our end-to-end pipeline demonstrated a 27-fold speedup (55.1 s vs. 1,492.6 s) and successfully processed a massive dataset in 23.6 h that caused VENAS to fail due to memory limitations. These findings validate the utility of TempSnap-Trace for large-scale viral surveillance, highlighting its distinct advantages in cross-border transmission warning and variant origin tracing. The code of TempSnap-Trace is available at https://github.com/Jiajun0413/TempSnap-Trace.