Pub Date : 2025-10-01DOI: 10.1016/j.bsheal.2025.09.005
Mengjiao Xie , Yang Song , Jing Tao , Mengnan Jiang , Yang Liu , Io Hong Cheong , Zisis Kozlakidis , Zhaorui Chang , Qiang Wei
Diarrhea is currently a prominent global public health issue. This study evaluated recent trends in the global burden of diarrhea and projected future changes over the next decade. Using the diarrhea data from the global burden of disease (GBD) 2021, this study assessed the temporal trends using Joinpoint regression and explored the impact of different factors using age-period-cohort modeling. Decomposition analysis identified drivers of disease burden changes, and the Bayesian age-period-cohort (BAPC) model predicted future trends. Additionally, health inequalities were measured by the inequality slope index and the concentration index. Results show a downward trend in the global burden of diarrhea since 1990. Age-period-cohort analysis suggests that the risk of incidence decreases with age until the age of 20, but increases with age after the age of 60. Risk of death from diarrhea was highest in children aged 0–4 and also increasesd after the age of 60. Decomposition identified population growth as the primary driver of burden changes, and BAPC projections indicated that the burden of diarrhea will continue declining. However, significant inequalities persist, with lower sociodemographic index (SDI) countries bearing a disproportionately high burden, although these gaps have decreased over time. The conclusion highlights that children under 5 and adults over 60 face the highest risks of diarrhea incidence and death. More attention should be paid to these populations, and effective public health policies should be implemented.
{"title":"Global trends, age-period-cohort analysis, and future projections of diarrhea burden: Findings from the Global Burden of Disease Study 2021","authors":"Mengjiao Xie , Yang Song , Jing Tao , Mengnan Jiang , Yang Liu , Io Hong Cheong , Zisis Kozlakidis , Zhaorui Chang , Qiang Wei","doi":"10.1016/j.bsheal.2025.09.005","DOIUrl":"10.1016/j.bsheal.2025.09.005","url":null,"abstract":"<div><div>Diarrhea is currently a prominent global public health issue. This study evaluated recent trends in the global burden of diarrhea and projected future changes over the next decade. Using the diarrhea data from the global burden of disease (GBD) 2021, this study assessed the temporal trends using Joinpoint regression and explored the impact of different factors using age-period-cohort modeling. Decomposition analysis identified drivers of disease burden changes, and the Bayesian age-period-cohort (BAPC) model predicted future trends. Additionally, health inequalities were measured by the inequality slope index and the concentration index. Results show a downward trend in the global burden of diarrhea since 1990. Age-period-cohort analysis suggests that the risk of incidence decreases with age until the age of 20, but increases with age after the age of 60. Risk of death from diarrhea was highest in children aged 0–4 and also increasesd after the age of 60. Decomposition identified population growth as the primary driver of burden changes, and BAPC projections indicated that the burden of diarrhea will continue declining. However, significant inequalities persist, with lower sociodemographic index (SDI) countries bearing a disproportionately high burden, although these gaps have decreased over time. The conclusion highlights that children under 5 and adults over 60 face the highest risks of diarrhea incidence and death. More attention should be paid to these populations, and effective public health policies should be implemented.</div></div>","PeriodicalId":36178,"journal":{"name":"Biosafety and Health","volume":"7 5","pages":"Pages 295-305"},"PeriodicalIF":3.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145374325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-01DOI: 10.1016/j.bsheal.2025.07.008
Naizhe Li , Sunxiao Ruan , Huaiyu Tian
Interactions among zoonotic pathogens play a critical role in shaping disease transmission, severity, and public health responses. However, the mechanisms and population-level consequences of these interactions remain underexplored in current modelling frameworks. This review aims to synthesize emerging evidence and address key scientific challenges in understanding how pathogen interactions influence transmission dynamics and mathematical modelling, with a focus on zoonotic and other cocirculating pathogens. In this review, we synthesize current evidence on synergistic, antagonistic, and neutral interactions between zoonotic and other cocirculating pathogens. We explore the underlying mechanisms of these interactions, such as transmission enhancement, immune modulation, and resource competition, at both the individual and population levels. We further review mathematical models to illustrate how these interaction features, such as transmission pathways, coinfection histories, cross-immunity, and superspreading potential, could be incorporated into epidemiological frameworks to increase our understanding of the community transmission of infections. Particular attention is given to the challenges of parameter estimation, incomplete surveillance data, and the difficulty of modelling interactions across scales and pathogen types. Understanding and modelling these interactions is essential for predicting outbreak trajectories, designing effective vaccination strategies, and improving early-warning systems. We conclude by calling for enhanced integration of empirical data and mechanistic modelling, especially in the context of emerging zoonoses and postpandemic preparedness. This review provides a structured perspective to support future interdisciplinary efforts aimed at managing cocirculating pathogens and mitigating their public health impact.
{"title":"Interactions between zoonotic pathogens and infectious disease spread: Why understanding mechanisms and modelling matters more than ever","authors":"Naizhe Li , Sunxiao Ruan , Huaiyu Tian","doi":"10.1016/j.bsheal.2025.07.008","DOIUrl":"10.1016/j.bsheal.2025.07.008","url":null,"abstract":"<div><div>Interactions among zoonotic pathogens play a critical role in shaping disease transmission, severity, and public health responses. However, the mechanisms and population-level consequences of these interactions remain underexplored in current modelling frameworks. This review aims to synthesize emerging evidence and address key scientific challenges in understanding how pathogen interactions influence transmission dynamics and mathematical modelling, with a focus on zoonotic and other cocirculating pathogens. In this review, we synthesize current evidence on synergistic, antagonistic, and neutral interactions between zoonotic and other cocirculating pathogens. We explore the underlying mechanisms of these interactions, such as transmission enhancement, immune modulation, and resource competition, at both the individual and population levels. We further review mathematical models to illustrate how these interaction features, such as transmission pathways, coinfection histories, cross-immunity, and superspreading potential, could be incorporated into epidemiological frameworks to increase our understanding of the community transmission of infections. Particular attention is given to the challenges of parameter estimation, incomplete surveillance data, and the difficulty of modelling interactions across scales and pathogen types. Understanding and modelling these interactions is essential for predicting outbreak trajectories, designing effective vaccination strategies, and improving early-warning systems. We conclude by calling for enhanced integration of empirical data and mechanistic modelling, especially in the context of emerging zoonoses and postpandemic preparedness. This review provides a structured perspective to support future interdisciplinary efforts aimed at managing cocirculating pathogens and mitigating their public health impact.</div></div>","PeriodicalId":36178,"journal":{"name":"Biosafety and Health","volume":"7 5","pages":"Pages 267-274"},"PeriodicalIF":3.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145374324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-01DOI: 10.1016/j.bsheal.2025.09.006
Weiwei Shen , Yixue Li , Liucun Zhu , Tao Huang
As a single-stranded ribonucleic acid (RNA) virus, the replication, transcription, and interactions with host cells of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) rely on a complex network of RNA-RNA interactions. Investigating local RNA-RNA interactions is crucial for elucidating how viruses regulate their own functions and respond to host immune responses. This study aims to explore the application of machine learning techniques in analyzing and predicting RNA-RNA interactions within the coronavirus genome. Using virion RNA in situ conformation sequencing technology(vRIC-Seq) data and advanced computational models, we evaluated potential interactions between viral RNA fragments. By employing a variety of traditional machine learning algorithms, including traditional One-hot coding, Word2Vec models, a number of different neural network architectures, and the RNAErnie language modeling framework, we achieved significant predictive accuracy in determining the presence or absence of interactions. Furthermore, this approach provides a novel framework for investigating RNA-RNA interactions in other viral systems, thereby opening new avenues for the development of targeted therapeutic strategies against viral infections. The integration of computational models substantially enhances our comprehension of complex biological processes and represents a promising trajectory for future virology research. The source codes and models are freely available at https://github.com/VV1025/RNA-language-models.
{"title":"Predict SARS-CoV-2 genome interactions based on RNA language models","authors":"Weiwei Shen , Yixue Li , Liucun Zhu , Tao Huang","doi":"10.1016/j.bsheal.2025.09.006","DOIUrl":"10.1016/j.bsheal.2025.09.006","url":null,"abstract":"<div><div>As a single-stranded ribonucleic acid (RNA) virus, the replication, transcription, and interactions with host cells of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) rely on a complex network of RNA-RNA interactions. Investigating local RNA-RNA interactions is crucial for elucidating how viruses regulate their own functions and respond to host immune responses. This study aims to explore the application of machine learning techniques in analyzing and predicting RNA-RNA interactions within the coronavirus genome. Using virion RNA in situ conformation sequencing technology(vRIC-Seq) data and advanced computational models, we evaluated potential interactions between viral RNA fragments. By employing a variety of traditional machine learning algorithms, including traditional One-hot coding, Word2Vec models, a number of different neural network architectures, and the RNAErnie language modeling framework, we achieved significant predictive accuracy in determining the presence or absence of interactions. Furthermore, this approach provides a novel framework for investigating RNA-RNA interactions in other viral systems, thereby opening new avenues for the development of targeted therapeutic strategies against viral infections. The integration of computational models substantially enhances our comprehension of complex biological processes and represents a promising trajectory for future virology research. The source codes and models are freely available at <span><span>https://github.com/VV1025/RNA-language-models</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":36178,"journal":{"name":"Biosafety and Health","volume":"7 5","pages":"Pages 333-343"},"PeriodicalIF":3.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145374323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-01DOI: 10.1016/j.bsheal.2025.09.004
Guohao Zhang , Chuanyang Liu , Wenying Li , Jiajie Lu, Ang Li, Lingyun Zhu
Artificial intelligence (AI)-driven de novo protein design is revolutionizing synthetic biology by facilitating the first-principle rational engineering of protein-based functional modules unbound by known structural templates and evolutionary constraints, enabling a diverse range of applications. Expressing these novel, structurally unprecedented proteins within cellular systems inherently adds complexity to their functional unpredictability. Robust biosafety and bioethics evaluations are therefore required to address potential risks such as immune reactions, cellular pathway disruptions, and environmental persistence. We systematically analyse the computational frameworks underpinning this revolution and highlight the capability of de novo proteins to act as a modular toolkit for synthetic biology. Looking forward, we envision integrating closed-loop validation with multi-omics profiling for comprehensive risk assessments along with a hierarchical design framework for advancing the future of synthetic biology – from the creation of tailored de novo functional protein modules and structure-guided rational genetic circuits design to the development of full-synthetic cellular systems, thereby establishing a scalable path from protein design to system-level implementation.
{"title":"Beyond evolution: De novo designed protein toolkit rewriting the rules of synthetic biology","authors":"Guohao Zhang , Chuanyang Liu , Wenying Li , Jiajie Lu, Ang Li, Lingyun Zhu","doi":"10.1016/j.bsheal.2025.09.004","DOIUrl":"10.1016/j.bsheal.2025.09.004","url":null,"abstract":"<div><div>Artificial intelligence (AI)-driven <em>de novo</em> protein design is revolutionizing synthetic biology by facilitating the first-principle rational engineering of protein-based functional modules unbound by known structural templates and evolutionary constraints, enabling a diverse range of applications. Expressing these novel, structurally unprecedented proteins within cellular systems inherently adds complexity to their functional unpredictability. Robust biosafety and bioethics evaluations are therefore required to address potential risks such as immune reactions, cellular pathway disruptions, and environmental persistence. We systematically analyse the computational frameworks underpinning this revolution and highlight the capability of <em>de novo</em> proteins to act as a modular toolkit for synthetic biology. Looking forward, we envision integrating closed-loop validation with multi-omics profiling for comprehensive risk assessments along with a hierarchical design framework for advancing the future of synthetic biology – from the creation of tailored <em>de novo</em> functional protein modules and structure-guided rational genetic circuits design to the development of full-synthetic cellular systems, thereby establishing a scalable path from protein design to system-level implementation.</div></div>","PeriodicalId":36178,"journal":{"name":"Biosafety and Health","volume":"7 5","pages":"Pages 306-311"},"PeriodicalIF":3.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145374321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-01DOI: 10.1016/j.bsheal.2025.09.007
Junyu Luo , Xiyang Cai , Yixue Li
Large language models (LLMs) have emerged as transformative tools in infectious disease research, offering unprecedented capabilities in analyzing biological sequences. This review summarizes three primary types of biological LLMs, including protein language models, genomic language models, and multimodal models, highlighting their architectures and applications. These models are revolutionizing key areas such as pathogen identification, evolutionary surveillance, host-pathogen prediction, and therapeutic development by enabling the interpretation of complex genomic and proteomic data at an unparalleled scale. While recent advancements are remarkable, challenges persist in data quality, long-context processing, model interpretability, and biosafety considerations. Understanding the potential and limitations of LLMs is crucial for leveraging them effectively in infectious disease research while ensuring responsible development and deployment.
{"title":"Large language models for biological sequence analysis in infectious disease research","authors":"Junyu Luo , Xiyang Cai , Yixue Li","doi":"10.1016/j.bsheal.2025.09.007","DOIUrl":"10.1016/j.bsheal.2025.09.007","url":null,"abstract":"<div><div>Large language models (LLMs) have emerged as transformative tools in infectious disease research, offering unprecedented capabilities in analyzing biological sequences. This review summarizes three primary types of biological LLMs, including protein language models, genomic language models, and multimodal models, highlighting their architectures and applications. These models are revolutionizing key areas such as pathogen identification, evolutionary surveillance, host-pathogen prediction, and therapeutic development by enabling the interpretation of complex genomic and proteomic data at an unparalleled scale. While recent advancements are remarkable, challenges persist in data quality, long-context processing, model interpretability, and biosafety considerations. Understanding the potential and limitations of LLMs is crucial for leveraging them effectively in infectious disease research while ensuring responsible development and deployment.</div></div>","PeriodicalId":36178,"journal":{"name":"Biosafety and Health","volume":"7 5","pages":"Pages 323-332"},"PeriodicalIF":3.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145374322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01DOI: 10.1016/j.bsheal.2025.07.007
Miran Jo , Eunjo Lee , Ho Jung Oh , Jin Tae Hong , Kyung Hee Sohn
Despite the development of messenger ribonucleic acid (mRNA) vaccines for the infectious novel coronavirus 2 (SARS-CoV-2), further research on test methods is required to ensure their quality as well as rapid and effective approval for release to the market. During the current national lot release testing, identity tests cannot be conducted on other products using primers, probes, and in-house reference materials provided by the manufacturer and specific to one vaccine, because their sequences do not match. When key reagents and reference materials are dependent on the manufacturer in this way, difficulties in national lot release approval—which serves as an additional step for the government to verify product quality—arise if the manufacturer does not provide them. In this study, we aimed to develop a quantitative polymerase chain reaction (qPCR) assay by using commercially available nucleic acid amplification test (NAT) reference material and a dye instead of a probe along with primers that were newly designed in this study. It can be applied to both vaccines. This study suggests a test method that can be applied when the in-house reference standard for the identity test, a major step to confirm the quality of vaccines, is not secured.
{"title":"Development of an identity test for COVID-19 mRNA vaccines using SARS-CoV-2 NAT standard","authors":"Miran Jo , Eunjo Lee , Ho Jung Oh , Jin Tae Hong , Kyung Hee Sohn","doi":"10.1016/j.bsheal.2025.07.007","DOIUrl":"10.1016/j.bsheal.2025.07.007","url":null,"abstract":"<div><div>Despite the development of messenger ribonucleic acid (mRNA) vaccines for the infectious novel coronavirus 2 (SARS-CoV-2), further research on test methods is required to ensure their quality as well as rapid and effective approval for release to the market. During the current national lot release testing, identity tests cannot be conducted on other products using primers, probes, and in-house reference materials provided by the manufacturer and specific to one vaccine, because their sequences do not match. When key reagents and reference materials are dependent on the manufacturer in this way, difficulties in national lot release approval—which serves as an additional step for the government to verify product quality—arise if the manufacturer does not provide them. In this study, we aimed to develop a quantitative polymerase chain reaction (qPCR) assay by using commercially available nucleic acid amplification test (NAT) reference material and a dye instead of a probe along with primers that were newly designed in this study. It can be applied to both vaccines. This study suggests a test method that can be applied when the in-house reference standard for the identity test, a major step to confirm the quality of vaccines, is not secured.</div></div>","PeriodicalId":36178,"journal":{"name":"Biosafety and Health","volume":"7 4","pages":"Pages 224-227"},"PeriodicalIF":3.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144896485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01DOI: 10.1016/j.bsheal.2025.07.001
Xiaozhou He , Ran Zhang , Jie Dong , Wei Zhen , Li Zhu , Junkai Ren , Xuejun Ma , Feng Wang , Shuang Zhang , Ke Xu , Feng Qiu , Qiudong Su , Jian’an He , Weimin Zhou , Guizhen Wu
The epidemiological characteristics of emerging infectious disease outbreaks in recent years have underscored the critical importance of controlling imported infectious diseases. In this study, we implemented dynamic tracking of microbial invasions by monitoring environmental microbes at the customs and ports. From July to September 2024, a total of 126 environmental samples were collected from three ports of entry in Shenzhen, China. Metagenomic analysis detected 55 non-viral microbial communities and 12 viral taxa. Among these, 26.8 % of the bacteria, 100 % of the fungi, 71.4 % of the protists, and none of the archaea exhibited potential pathogenic properties. Viruses were the most prevalent, including bacteriophages (100 %), unclassified viruses (96.8 %), giant viruses (27.8 %), fungal viruses (4.8 %), and vertebrate viruses (1.6 %). No statistical differences were observed in viral distribution across areas (χ2 = 18.70, P = 0.541), sites (χ2 = 14.02, P = 0.597), or ports of entry (χ2 = 10.27, P = 0.247). However, viral distribution varied significantly across three sampling months (χ2 = 21.06, P = 0.002), with a higher proportion of giant viruses detected in July. Thirty-nine and forty microorganisms were identified across the six areas and five sites, respectively, with relatively few area/site-specific microorganisms. Four distinct disinfection level zones were categorized: relatively safe zone, less safe zone, general disinfection zone and key disinfection zone. Two strains of viruses with potential pathogenicity were identified: pigeon circovirus and Influenza A virus (H4N2). This study established a metagenomics-based surveillance framework for microbial risk assessment in high-risk port environments and proposed a four-tier disinfection strategy to prioritize high-contact zones. Our findings highlighted environmental metagenomics as a critical complement to traveler screening and provided early warning signals for the prevention and control of imported infectious diseases.
近年来新出现的传染病爆发的流行病学特点突出了控制输入性传染病的极端重要性。在本研究中，我们通过监测海关和港口的环境微生物，实现了微生物入侵的动态跟踪。2024年7月至9月，在中国深圳的三个入境口岸采集了126份环境样本。宏基因组分析检测到55个非病毒微生物群落和12个病毒分类群。其中，26.8%的细菌、100%的真菌、71.4%的原生生物和无一的古细菌表现出潜在的致病特性。病毒最常见，包括噬菌体（100%）、未分类病毒（96.8%）、巨型病毒（27.8%）、真菌病毒（4.8%）和脊椎动物病毒（1.6%）。病毒在地区（χ2 = 18.70, P = 0.541）、地点（χ2 = 14.02, P = 0.597）、入境口岸（χ2 = 10.27, P = 0.247）的分布差异均无统计学意义。但病毒分布在3个采样月份间差异显著（χ2 = 21.06, P = 0.002），其中7月份检测到的巨型病毒比例较高。在6个区域和5个站点中分别鉴定出39种和40种微生物，区域/站点特异性微生物相对较少。将消毒等级划分为相对安全区、较不安全区、一般消毒区和重点消毒区。鉴定出两株具有潜在致病性的病毒：鸽子圆环病毒和甲型流感病毒（H4N2）。本研究建立了基于宏基因组学的高风险港口环境微生物风险评估监测框架，并提出了优先考虑高接触区的四层消毒策略。我们的研究结果强调了环境宏基因组学作为旅行者筛查的重要补充，并为预防和控制输入性传染病提供了早期预警信号。
{"title":"A metagenomic approach for microbial risk assessment and source attribution in high-risk ports of entry environments","authors":"Xiaozhou He , Ran Zhang , Jie Dong , Wei Zhen , Li Zhu , Junkai Ren , Xuejun Ma , Feng Wang , Shuang Zhang , Ke Xu , Feng Qiu , Qiudong Su , Jian’an He , Weimin Zhou , Guizhen Wu","doi":"10.1016/j.bsheal.2025.07.001","DOIUrl":"10.1016/j.bsheal.2025.07.001","url":null,"abstract":"<div><div>The epidemiological characteristics of emerging infectious disease outbreaks in recent years have underscored the critical importance of controlling imported infectious diseases. In this study, we implemented dynamic tracking of microbial invasions by monitoring environmental microbes at the customs and ports. From July to September 2024, a total of 126 environmental samples were collected from three ports of entry in Shenzhen, China. Metagenomic analysis detected 55 non-viral microbial communities and 12 viral taxa. Among these, 26.8 % of the bacteria, 100 % of the fungi, 71.4 % of the protists, and none of the archaea exhibited potential pathogenic properties. Viruses were the most prevalent, including bacteriophages (100 %), unclassified viruses (96.8 %), giant viruses (27.8 %), fungal viruses (4.8 %), and vertebrate viruses (1.6 %). No statistical differences were observed in viral distribution across areas (<em>χ<sup>2</sup></em> = 18.70, <em>P</em> = 0.541), sites (<em>χ<sup>2</sup></em> = 14.02, <em>P</em> = 0.597), or ports of entry (<em>χ<sup>2</sup></em> = 10.27, <em>P</em> = 0.247). However, viral distribution varied significantly across three sampling months (<em>χ<sup>2</sup></em> = 21.06, <em>P</em> = 0.002), with a higher proportion of giant viruses detected in July. Thirty-nine and forty microorganisms were identified across the six areas and five sites, respectively, with relatively few area/site-specific microorganisms. Four distinct disinfection level zones were categorized: relatively safe zone, less safe zone, general disinfection zone and key disinfection zone. Two strains of viruses with potential pathogenicity were identified: pigeon circovirus and Influenza A virus (H4N2). This study established a metagenomics-based surveillance framework for microbial risk assessment in high-risk port environments and proposed a four-tier disinfection strategy to prioritize high-contact zones. Our findings highlighted environmental metagenomics as a critical complement to traveler screening and provided early warning signals for the prevention and control of imported infectious diseases.</div></div>","PeriodicalId":36178,"journal":{"name":"Biosafety and Health","volume":"7 4","pages":"Pages 228-237"},"PeriodicalIF":3.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144895627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01DOI: 10.1016/j.bsheal.2025.07.006
Kabita Adhikari , Elizabeth Zhou , Majid Khan , Shubhasish Goswami , Amir Khazaieli , Blake A. Simmons , Deepika Awasthi , Subhash C. Verma
The role of personal protective equipment (PPE) in protecting against exposure to infectious agents and toxic chemicals is well-established. However, the global surge in PPE demand during the pandemic exposed challenges, including shortages and environmental impacts from disposable waste. Developing effective, scalable, and sustainable decontamination methods for the reuse of PPE is essential. Ozone has emerged as a promising, eco-friendly disinfectant due to its strong oxidative properties, rapid action, and residue-free breakdown into oxygen. This study evaluates the effectiveness of the FATHHOME Trinion Disinfector, an innovative ozone-based dry sterilization device, for inactivating pathogens on PPE materials, such as not resistant to oil 95 (N95) masks and face shields. The device’s bactericidal performance was tested against Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, Salmonella typhimurium, Enterococcus durans, Enterococcus faecalis, and Saccharomyces cerevisiae, achieving a 1- to 2-log reduction in these bacterial and fungal pathogens. A 30-minute ozone exposure cycle was found to attain maximum sterilization efficiency. We also demonstrated the disinfector’s efficacy against viral pathogens, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), adeno-associated virus (AAV), herpes simplex virus type 1 (HSV-1), and hepatitis B virus (HBV) on PPE surfaces. SARS-CoV-2 contamination on face shields and N95 masks decreased by 99.9 %, and AAV infectivity was nearly eliminated. Similar reductions were observed for HSV-1 and HBV. Overall, the findings confirm that ozone-based disinfection offers a rapid, scalable, and sustainable method for decontaminating PPE. These results support the establishment of standardized ozone disinfection protocols to enhance infection control, address PPE shortages, and minimize environmental waste.
{"title":"Inactivation of BSL-2 and BSL-3 human pathogens using FATHHOME’s Trinion Disinfector: A rapid and eco-friendly ozone-based dry disinfection approach","authors":"Kabita Adhikari , Elizabeth Zhou , Majid Khan , Shubhasish Goswami , Amir Khazaieli , Blake A. Simmons , Deepika Awasthi , Subhash C. Verma","doi":"10.1016/j.bsheal.2025.07.006","DOIUrl":"10.1016/j.bsheal.2025.07.006","url":null,"abstract":"<div><div>The role of personal protective equipment (PPE) in protecting against exposure to infectious agents and toxic chemicals is well-established. However, the global surge in PPE demand during the pandemic exposed challenges, including shortages and environmental impacts from disposable waste. Developing effective, scalable, and sustainable decontamination methods for the reuse of PPE is essential. Ozone has emerged as a promising, eco-friendly disinfectant due to its strong oxidative properties, rapid action, and residue-free breakdown into oxygen. This study evaluates the effectiveness of the FATHHOME Trinion Disinfector, an innovative ozone-based dry sterilization device, for inactivating pathogens on PPE materials, such as not resistant to oil 95 (N95) masks and face shields. The device’s bactericidal performance was tested against <em>Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, Salmonella typhimurium, Enterococcus durans, Enterococcus faecalis</em>, and <em>Saccharomyces cerevisiae</em>, achieving a 1- to 2-log reduction in these bacterial and fungal pathogens. A 30-minute ozone exposure cycle was found to attain maximum sterilization efficiency. We also demonstrated the disinfector’s efficacy against viral pathogens, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), adeno-associated virus (AAV), herpes simplex virus type 1 (HSV-1), and hepatitis B virus (HBV) on PPE surfaces. SARS-CoV-2 contamination on face shields and N95 masks decreased by 99.9 %, and AAV infectivity was nearly eliminated. Similar reductions were observed for HSV-1 and HBV. Overall, the findings confirm that ozone-based disinfection offers a rapid, scalable, and sustainable method for decontaminating PPE. These results support the establishment of standardized ozone disinfection protocols to enhance infection control, address PPE shortages, and minimize environmental waste.</div></div>","PeriodicalId":36178,"journal":{"name":"Biosafety and Health","volume":"7 4","pages":"Pages 245-256"},"PeriodicalIF":3.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144896229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01DOI: 10.1016/j.bsheal.2025.07.004
Yunshao Xu , Yuping Duan , Jule Yang , Mingyue Jiang , Yanxia Sun , Yanlin Cao , Li Qi , Zeni Wu , Luzhao Feng
Progression of acute respiratory infection (ARI) to pneumonia increases severity and healthcare burden. Limited evidence exists on using machine learning to identify predictors from demographics, clinical, and pathogen detection data. This study aimed to identify pneumonia predictors in ARI patients using machine learning methods. This observational study was conducted in Chongqing, China, from September 2023 to April 2024. Outpatients and inpatients with ARI were recruited weekly. A random forest algorithm was used for predictor selection, followed by a logistic regression-based nomogram to analyze the probability of pneumonia. Among the 1,638 patients with ARI, those with pneumonia had higher rates of influenza A virus (IFV-A) (49.2 % vs. 39.6 %), influenza B virus (26.3 % vs. 18.6 %), and respiratory syncytial virus (6.1 % vs. 1.9 %) infection than those without pneumonia. In the subgroup of 79 patients with comprehensive blood tests, pneumonia was positively associated with hemoglobin (130.00 g/L vs. 124.00 g/L), blood urea nitrogen (5.73 mmol/L vs. 4.85 mmol/L), C-reactive protein (36.10 mg/L vs. 25.25 mg/L), procalcitonin (0.11 μg/L vs. 0.07 μg/L), and D-dimer (0.95 μg/L vs. 0.80 μg/L) levels, whereas pneumonia was inversely associated with neutrophils (4.20 × 109/L vs. 4.76 × 109/L), aspartate aminotransferase (22.50 U/L vs. 24.00 U/L), and uric acid (280.90 μmol/L vs. 330.00 μmol/L) levels. Elevated D-dimer levels (adjusted odds ratio [aOR] = 1.002, 95 % confidence interval [CI]: 1.001–1.004) and IFV-A infection (aOR = 9.308, 95 % CI: 2.433–35.606) were significantly associated with increased pneumonia probability. In future clinical practice, particular attention should be given to ARI patients with elevated D-dimer levels and IFV-A infections.
急性呼吸道感染（ARI）发展为肺炎会增加严重程度和医疗负担。使用机器学习从人口统计、临床和病原体检测数据中识别预测因子的证据有限。本研究旨在使用机器学习方法确定ARI患者的肺炎预测因素。该观察性研究于2023年9月至2024年4月在中国重庆进行。每周招募ARI门诊和住院患者。使用随机森林算法选择预测因子，然后使用基于逻辑回归的nomogram来分析肺炎的概率。在1,638例ARI患者中，肺炎患者感染甲型流感病毒（IFV-A）（49.2%对39.6%）、乙型流感病毒（26.3%对18.6%）和呼吸道合胞病毒（6.1%对1.9%）的比例高于无肺炎患者。在79例综合血液检查患者亚组中，肺炎与血红蛋白（130.00 g/L vs. 124.00 g/L）、血尿素氮（5.73 mmol/L vs. 4.85 mmol/L）、c反应蛋白（36.10 mg/L vs. 25.25 mg/L）、降钙素原（0.11 μg/L vs. 0.07 μg/L）、d -二聚体（0.95 μg/L vs. 0.80 μg/L）水平呈正相关，与中性粒细胞（4.20 × 109/L vs. 4.76 × 109/L）、天冬氨酸转氨酶（22.50 U/L vs. 24.00 U/L）水平呈负相关。尿酸水平（280.90 μmol/L vs. 330.00 μmol/L）。d -二聚体水平升高（调整优势比[aOR] = 1.002, 95%可信区间[CI]: 1.001 ~ 1.004）和IFV-A感染（aOR = 9.308, 95% CI: 2.433 ~ 35.606）与肺炎发生概率增加显著相关。在未来的临床实践中，应特别注意伴有d -二聚体水平升高和IFV-A感染的ARI患者。
{"title":"Random forest-based predictor selection and pneumonia risk probability assessment in acute respiratory infections: A cross-sectional study in Chongqing, China, 2023–2024","authors":"Yunshao Xu , Yuping Duan , Jule Yang , Mingyue Jiang , Yanxia Sun , Yanlin Cao , Li Qi , Zeni Wu , Luzhao Feng","doi":"10.1016/j.bsheal.2025.07.004","DOIUrl":"10.1016/j.bsheal.2025.07.004","url":null,"abstract":"<div><div>Progression of acute respiratory infection (ARI) to pneumonia increases severity and healthcare burden. Limited evidence exists on using machine learning to identify predictors from demographics, clinical, and pathogen detection data. This study aimed to identify pneumonia predictors in ARI patients using machine learning methods. This observational study was conducted in Chongqing, China, from September 2023 to April 2024. Outpatients and inpatients with ARI were recruited weekly. A random forest algorithm was used for predictor selection, followed by a logistic regression-based nomogram to analyze the probability of pneumonia. Among the 1,638 patients with ARI, those with pneumonia had higher rates of influenza A virus (IFV-A) (49.2 % vs. 39.6 %), influenza B virus (26.3 % vs. 18.6 %), and respiratory syncytial virus (6.1 % vs. 1.9 %) infection than those without pneumonia. In the subgroup of 79 patients with comprehensive blood tests, pneumonia was positively associated with hemoglobin (130.00 g/L vs. 124.00 g/L), blood urea nitrogen (5.73 mmol/L vs. 4.85 mmol/L), C-reactive protein (36.10 mg/L vs. 25.25 mg/L), procalcitonin (0.11 μg/L vs. 0.07 μg/L), and D-dimer (0.95 μg/L vs. 0.80 μg/L) levels, whereas pneumonia was inversely associated with neutrophils (4.20 × 10<sup>9</sup>/L vs. 4.76 × 10<sup>9</sup>/L), aspartate aminotransferase (22.50 U/L vs. 24.00 U/L), and uric acid (280.90 μmol/L vs. 330.00 μmol/L) levels. Elevated D-dimer levels (adjusted odds ratio [aOR] = 1.002, 95 % confidence interval [CI]: 1.001–1.004) and IFV-A infection (aOR = 9.308, 95 % CI: 2.433–35.606) were significantly associated with increased pneumonia probability. In future clinical practice, particular attention should be given to ARI patients with elevated D-dimer levels and IFV-A infections.</div></div>","PeriodicalId":36178,"journal":{"name":"Biosafety and Health","volume":"7 4","pages":"Pages 238-244"},"PeriodicalIF":3.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144896228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01DOI: 10.1016/j.bsheal.2025.07.005
Xindi Wang , Junyu Luo , Xiyang Cai , Ruibin Liu , Yixue Li , Chitin Hon
Understanding human-virus protein-protein interactions is critical for studying molecular mechanisms driving viral infection, immune evasion, and propagation, thereby informing strategies for public health. Here, we introduce a novel multimodal deep learning framework that integrates high-confidence experimental datasets to systematically predict putative interactions between human and viral proteins. Our approach incorporates two complementary tasks: binary classification for interaction prediction and conditional sequence generation to identify interacting protein partners. By leveraging protein language models and multimodal fusion, the framework demonstrates improved accuracy in identifying biologically relevant interactions. For empirical validation, we applied this method to predict severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-human interactions, identifying candidate proteins absent from training data, several of which were corroborated by independent studies. These predictions offer critical insights into potential therapeutic targets, facilitating the design of antiviral drugs and vaccines. By enabling rapid, cost-effective discovery pipelines, our study contributes to pandemic preparedness and public health interventions, underscoring its value in combating emerging infectious diseases.
{"title":"DeepHVI: A multimodal deep learning framework for predicting human-virus protein-protein interactions using protein language models","authors":"Xindi Wang , Junyu Luo , Xiyang Cai , Ruibin Liu , Yixue Li , Chitin Hon","doi":"10.1016/j.bsheal.2025.07.005","DOIUrl":"10.1016/j.bsheal.2025.07.005","url":null,"abstract":"<div><div>Understanding human-virus protein-protein interactions is critical for studying molecular mechanisms driving viral infection, immune evasion, and propagation, thereby informing strategies for public health. Here, we introduce a novel multimodal deep learning framework that integrates high-confidence experimental datasets to systematically predict putative interactions between human and viral proteins. Our approach incorporates two complementary tasks: binary classification for interaction prediction and conditional sequence generation to identify interacting protein partners. By leveraging protein language models and multimodal fusion, the framework demonstrates improved accuracy in identifying biologically relevant interactions. For empirical validation, we applied this method to predict severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-human interactions, identifying candidate proteins absent from training data, several of which were corroborated by independent studies. These predictions offer critical insights into potential therapeutic targets, facilitating the design of antiviral drugs and vaccines. By enabling rapid, cost-effective discovery pipelines, our study contributes to pandemic preparedness and public health interventions, underscoring its value in combating emerging infectious diseases.</div></div>","PeriodicalId":36178,"journal":{"name":"Biosafety and Health","volume":"7 4","pages":"Pages 257-266"},"PeriodicalIF":3.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144896486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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