Molecular Pathogens 2024, Vol.15 http://microbescipublisher.com/index.php/mp © 2024 MicroSci Publisher, an online publishing platform of Sophia Publishing Group. All Rights Reserved. Sophia Publishing Group (SPG), founded in British Columbia of Canada, is a multilingual publisher. Publisher Sophia Publishing Group
Molecular Pathogens 2024, Vol.15 http://microbescipublisher.com/index.php/mp © 2024 MicroSci Publisher, an online publishing platform of Sophia Publishing Group. All Rights Reserved. Sophia Publishing Group (SPG), founded in British Columbia of Canada, is a multilingual publisher. Publisher Sophia Publishing Group Editedby Editorial Team of Molecular Pathogens Email: edit@mp.microbescipublisher.com Website: http://microbescipublisher.com/index.php/mp Address: 11388 Stevenston Hwy, PO Box 96016, Richmond, V7A 5J5, British Columbia Canada Molecular Pathogens (ISSN 1925-1998) is an open access, peer reviewed journal published online by MicroSciPublisher. The journal is committed to publishing and disseminating all the latest and outstanding research articles, letters and reviews in all areas of molecular pathogens. The range of topics including isolation and identification of emerging pathogens viruses, pathogen-host interactions, genetics and evolution, genomics and gene regulation, proteomics and signal transduction, glycomics and signal recognition, virulence factors and vaccine design and other topical advisory subjects. All the articles published in Molecular Pathogens are Open Access, and are distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. MicroSci Publisher uses CrossCheck service to identify academic plagiarism through the world’s leading plagiarism prevention tool, iParadigms, and to protect the original authors’ copyrights. MicroSci Publisher is an international Open Access publisher specializing in microbiology, bacteriology, mycology, molecular and cellular biology and virology registered at the publishing platform that is operated by Sophia Publishing Group (SPG), founded in British Columbia of Canada.
Molecular Pathogens (online), 2024, Vol. 15 ISSN 1925-1998 http://microbescipublisher.com/index.php/mp © 2024 MicroSci Publisher, an online publishing platform of Sophia Publishing Group. All Rights Reserved. Sophia Publishing Group (SPG), founded in British Columbia of Canada, is a multilingual publisher. Latest Content 2024, Vol.15, No.1 【Review Article】 Pathogenic Mechanisms of Marine Pathogens and Outbreak Dynamics 17-29 Hui Xiang, Zhongqi Wu DOI: 10.5376/mp.2024.15.0003 【Research Article】 Application of Artificial Intelligence in Early Diagnosis of Influenza A (H1N1) Virus Infection 1-9 ShaLi DOI: 10.5376/mp.2024.15.0001 【Research Report】 Genomic Approaches to Enhance Disease Resistance in Grapevine Breeding Programs 30-39 Dandan Huang DOI: 10.5376/mp.2024.15.0004 【Feature Review】 Molecular Mechanisms of Tea Plant Resistance to Major Pathogens 40-49 Jie Huang, Meifang Li DOI: 10.5376/mp.2024.15.0005 【Review and Progress】 Relationship Between HIV Mutation and Host Antibody Response 9-16 Wei Zhang DOI: 10.5376/mp.2024.15.0002
Molecular Pathogens 2024, Vol.15, No.1, 1-8 http://microbescipublisher.com/index.php/mp 1 Research Article Open Access Application of Artificial Intelligence in Early Diagnosis of Influenza A (H1N1) Virus Infection ShaLi Ningbo cha microorganism technology co., ltd, Ningbo, 315000, Zhejiang, China Corresponding email: Judyzhouww@163.com Molecular Pathogens, 2024, Vol.15, No.1 doi: 10.5376/mp.2024.15.0001 Received: 27 Dec., 2024 Accepted: 30 Dec., 2024 Published: 01 Jan., 2024 Copyright © 2024 Li, This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Li S., 2024, Application of artificial intelligence in early diagnosis of influenza A (H1N1) virus infection, Molecular Pathogens, 15(1): 1-8 (doi: 10.5376/mp.2024.15.0001) Abstract This review mainly discusses the application and potential value of artificial intelligence in early diagnosis of influenza A (H1N1) virus infection. By comparing the advantages and disadvantages of the commonly used influenza A (H1N1) virus diagnosis methods, the limitations of the diagnosis methods and the wide applicability of artificial intelligence in medical diagnosis, this paper focuses on the specific application of artificial intelligence in the diagnosis of influenza A (H1N1) virus infection, and highlights its special advantages in improving the accuracy and efficiency of early diagnosis. The research also discusses the advantages and challenges of how artificial intelligence can improve the accuracy and efficiency of early diagnosis. In addition, this review also summarizes the future development trend of artificial intelligence in early diagnosis of influenza A (H1N1) virus infection. Through practical application and case study, the effect and influence of artificial intelligence in practical application are evaluated, and suggestions and prospects for future research are put forward. Although artificial intelligence still faces some challenges and limitations in practical application, with the continuous progress of technology and deeper understanding of artificial intelligence, it is believed that the application of artificial intelligence in medical and health fields will be more and more extensive in the future. Keywords Artificial intelligence; Early diagnosis; Influenza A (H1N1) virus; Effect evaluation 1 Introduction The influenza A (H1N1) virus is a highly contagious virus that has become one of the significant global public health threats since its first outbreak in 2009. The disease symptoms caused by the virus are similar to other types of influenza, including fever, cough, sore throat, and body aches. However, it can also lead to more severe complications such as pneumonia and respiratory failure. Therefore, early diagnosis of influenza A (H1N1) virus infection is crucial for controlling the spread of the epidemic and timely treatment of patients. This study will explore the application of artificial intelligence in the early diagnosis of influenza A (H1N1) virus infection, analyzing its feasibility and potential value (María et al., 2021). The application of artificial intelligence in medical diagnosis has become one of the hot topics in research nowadays. It involves various technologies such as machine learning, deep learning, and natural language processing, which can handle large amounts of medical data and improve the accuracy and efficiency of diagnosis. In medical diagnosis, the application of artificial intelligence can support doctors in tasks such as disease analysis, prediction, and treatment plan formulation. Particularly in the face of outbreaks of novel viruses, artificial intelligence can assist doctors in quickly identifying suspected cases, implementing early isolation and treatment measures, and effectively preventing the spread of the virus (Mintz and Brodie, 2018). By studying and analyzing the application of artificial intelligence in the early diagnosis of influenza A (H1N1) virus infection, this research aims to validate the potential of artificial intelligence in improving diagnostic accuracy and efficiency, and explore its practical effects in practice (Lin et al., 2021). It is believed that through the application of artificial intelligence technology, cases of influenza A (H1N1) virus infection can be diagnosed faster and more accurately in the future, thus gaining valuable time for prevention, control, and treatment efforts, and effectively safeguarding public health and safety.
Molecular Pathogens 2024, Vol.15, No.1, 1-8 http://microbescipublisher.com/index.php/mp 2 2 Diagnostic Methods for Influenza A (H1N1) Virus 2.1 Currently used diagnostic methods for influenza A (H1N1) virus include The commonly used diagnostic methods for influenza A (H1N1) virus currently mainly include source tracing examination, symptom examination, and laboratory testing. Source tracing examination is a method that assists doctors in diagnosing the disease by investigating patients' contact history and travel history. Since the H1N1 influenza virus is often contracted through contact with infected individuals or in disease hotspots before the onset of symptoms, understanding and analyzing this information allows doctors to make more accurate judgments on whether the patient is infected with influenza A (H1N1) virus (Swine, 2009). Symptom examination is a method based on the observation and evaluation of the patient's symptoms. Although the symptoms of influenza A (H1N1) virus are similar to those of common influenza, they tend to be more severe at the onset of the illness, typically peaking around days 4 to 7 and lasting for approximately one week. Some patients may experience some improvement. Therefore, by observing and evaluating the patient's symptoms, doctors can make a more accurate diagnosis of whether it is an infection with influenza A (H1N1) virus. Laboratory testing is a method that involves collecting samples such as throat swabs and nasal swabs from patients for nucleic acid examination and virus isolation testing. It is a relatively accurate diagnostic method (Figure 1). Nucleic acid examination is one of the important criteria for diagnosing influenza A (H1N1). If the results of throat swab or nasal swab tests are positive, it can be generally confirmed that the patient is infected. Virus isolation testing involves culturing and isolating nasal secretions, pharyngeal gargle fluids, and respiratory epithelial tissue cells from the patient's throat. If H1N1 virus can be clearly isolated, it can confirm the infection. Laboratory testing has high sensitivity and specificity, making it possible to accurately diagnose influenza A (H1N1) virus infection (María et al., 2021). Figure 1 Detection of throat swab and nose swab (Picture source: Sohu) 2.2 Comparison of the advantages and disadvantages of various diagnostic methods Source tracing examination is a method used to determine the possibility of infection with influenza A (H1N1) virus by asking the patient about their travel history and contact history. The method has the advantages of being cost-effective and widely applicable. Source tracing examination does not require additional instruments and equipment, only the inquiry conducted by healthcare professionals or personnel from disease control departments. Therefore, it is a rapid diagnostic method for large-scale outbreak investigations. Furthermore, it is useful for preliminary assessment of the situation and can help determine whether further symptom examination or laboratory testing is needed. Symptom examination is a method used to assess the likelihood of influenza A (H1N1) virus infection by observing the patient's physical symptoms. The method has the advantages of being simple, non-invasive, and easy to perform. Doctors can preliminarily determine the need for further diagnostic measures by asking the patient about flu-like symptoms such as fever, cough, sore throat, etc. Furthermore, symptom examination does not involve any invasive procedures, thus causing no additional harm or injury to the patient's body.
Molecular Pathogens 2024, Vol.15, No.1, 1-8 http://microbescipublisher.com/index.php/mp 3 Laboratory testing is a method used to confirm whether an individual is infected with influenza A (H1N1) virus by collecting respiratory samples such as throat swabs or nasopharyngeal swabs for virus nucleic acid detection or antigen testing (Michaelis et al., 2009). This method has the advantages of high diagnostic accuracy and sensitivity. Laboratory testing can conclusively detect the presence of the virus and determine its subtypes, aiding doctors in selecting appropriate treatment plans. However, laboratory testing requires specialized laboratory equipment and expertise, which may take some time and incur costs. Therefore, it is not feasible for large-scale screenings to be conducted within a short timeframe. 2.3 The importance of early diagnosis Early diagnosis is of great significance in disease management and prevention. It enables prompt measures to be taken for timely treatment and disease management. For many diseases, early treatment often improves efficacy and reduces the occurrence of complications. For instance, in cases of influenza A (H1N1) virus infection, early use of antiviral medications can alleviate symptoms and shorten recovery time. Therefore, through early diagnosis, healthcare professionals can quickly initiate appropriate treatment measures, aiding in the patient's recovery and disease control (Benjamin et al., 2022). Early diagnosis is equally important for disease prevention and control. In the case of certain infectious diseases, early diagnosis can help isolate patients early and implement corresponding prevention and control measures, effectively reducing disease transmission. For example, in the event of a newly emerging infectious disease outbreak, early diagnosis can assist in identifying and isolating infected individuals, thereby reducing the spread of the disease within the community. In addition, early diagnosis also contributes to a better understanding of the epidemiology and characteristics of diseases. By detecting cases in a timely manner and making diagnoses, relevant data can be collected and analyzed to understand the types of viruses or bacteria, transmission routes, and trends of the disease, providing a basis for formulating targeted prevention and control strategies. Through early diagnosis, prompt treatment measures can be implemented, alleviating symptoms and preventing the occurrence of complications. Furthermore, early diagnosis facilitates timely patient isolation, reducing disease transmission, and providing data support to aid in the development of effective prevention and control strategies. Therefore, strengthening the capacity and means of early diagnosis is crucial. 3 The Specific Application of Artificial Intelligence in the Diagnosis of Influenza A (H1N1) Virus Infection 3.1 Methods and strategies of using artificial intelligence for the diagnosis of influenza A (H1N1) virus infection Utilizing artificial intelligence for the diagnosis of influenza A (H1N1) virus infection is an emerging method and strategy with a broad range of applications. Artificial intelligence can analyze various clinical data of patients, such as medical history, symptoms, and physical signs, to extract key information for disease assessment and diagnostic judgments. Machine learning algorithms can mimic human thinking processes and, through training and learning, establish predictive models to identify features and patterns of influenza A (H1N1) virus infection (Benjamin et al., 2022). Artificial intelligence can also be applied to early screening and warning systems for influenza viruses. By analyzing and modeling a large amount of influenza virus data, artificial intelligence can identify the risk factors and associated features that may indicate the presence of influenza A (H1N1) virus infection. With these information, predictive models can be developed to monitor the spread and epidemiological trends of influenza viruses in real-time, providing early warnings of possible outbreaks. This can help in taking timely measures for prevention and control.
Molecular Pathogens 2024, Vol.15, No.1, 1-8 http://microbescipublisher.com/index.php/mp 4 Artificial intelligence can also be used for drug development and optimization of treatment plans for influenza viruses (Figure 2). By utilizing machine learning and data mining techniques, large-scale drug databases and biological information can be analyzed to identify potential antiviral drugs and therapeutic targets. Artificial intelligence can assist researchers in virtual screening and drug design, accelerating the speed and efficiency of drug discovery. Additionally, by analyzing individual patient characteristics and disease conditions, artificial intelligence can develop personalized treatment plans to improve treatment efficacy and prevention. Figure 2 Using artificial intelligence to diagnose influenza A (H1N1) virus infection (Picture source: Sohu) 3.2 The application of data-driven predictive models in the diagnosis of influenza A (H1N1) virus infection Data-driven predictive models have vast applications in the diagnosis of influenza A (H1N1) virus infection. By analyzing large-scale case data and patient information, these models can assist doctors in making more accurate and efficient diagnoses. Data-driven predictive models utilize machine learning and deep learning algorithms to extract valuable information from multidimensional data, including clinical manifestations, laboratory test results, epidemiological characteristics, and more, to further predict the likelihood of a patient being infected with influenza A (H1N1) virus. The learning and training processes of these models are based on historical case data, continuously optimizing the model parameters to improve prediction accuracy (Winter and Carusi, 2022). Data-driven predictive models can incorporate various external factors such as geographic location, climate change, and socio-economic factors to predict the transmission trends of influenza A (H1N1) virus. These factors can potentially affect the speed and scope of virus transmission, consequently impacting the spread of the epidemic. By analyzing these factors, the models can assist doctors and public health agencies in implementing preventive measures in advance, slowing down the rate of epidemic spread. Data-driven predictive models can also assess the effectiveness of antiviral drugs. By analyzing clinical trial data and real-world data, the models can predict the treatment outcomes of antiviral drugs for different populations, providing doctors with more reference guidance. Meanwhile, these models can personalize the best treatment plan for each patient based on individual variations. As technology advances and data quality improves, the application of these models will become increasingly widespread, offering more assistance and support to prevention, control, and treatment efforts. 3.3 Methods and achievements of using deep learning for the diagnosis of influenza A (H1N1) virus infection Deep learning models can be utilized to predict whether patients are infected with influenza A (H1N1) virus. These predictive models are based on a large volume of case data and learn the features of these cases to diagnose new incoming cases. Deep learning models exhibit high sensitivity and specificity, effectively improving diagnostic accuracy, and can process large amounts of case data in a short amount of time.
Molecular Pathogens 2024, Vol.15, No.1, 1-8 http://microbescipublisher.com/index.php/mp 5 Deep learning models can also be applied to the detection and classification of viral genomes. By analyzing viral genome data, deep learning models can automatically identify subtypes and variations of the virus, providing more accurate information for prevention, control, and treatment efforts. Deep learning models can also be used for studying disease mechanisms. By analyzing and mining case data, deep learning models can uncover key factors and mechanisms hidden within the data, contributing to a deeper understanding of the pathogenesis and transmission pathways of influenza A (H1N1) virus. Furthermore, deep learning models can be applied to epidemic prevention and control. By analyzing and predicting case data, deep learning models can identify focal points and challenges in prevention and control efforts, forecast the trends and scale of virus transmission, provide scientific evidence for formulating prevention and control measures, and offer more assistance and support to prevention and treatment efforts. 4 Artificial Intelligence in the Diagnosis of Influenza A (H1N1) Virus Case Studies 4.1 Artificial intelligence-assisted diagnosis of influenza A (H1N1) virus infection Researchers Utilizing artificial intelligence techniques, researchers have trained a model that assists doctors in diagnosis by analyzing and learning from case data. The model can automatically recognize patient symptoms and signs, and incorporate information such as age, gender, and medical history to predict the likelihood of influenza A (H1N1) virus infection. Compared to traditional diagnostic methods that heavily rely on the experience of doctors and laboratory testing, which have their limitations, the use of artificial intelligence as an aid can enhance diagnostic accuracy and efficiency (Lee and Ahn, 2020). Artificial intelligence, by automatically recognizing patient symptoms and signs, can reduce subjective errors made by doctors and improve diagnostic accuracy. Furthermore, by quickly analyzing large volumes of case data, it can enhance diagnostic efficiency and reduce patient waiting time. AI-assisted diagnosis can also provide doctors with more reference information to aid in formulating treatment plans and preventive measures, thereby reducing the patient's treatment duration and lowering the incidence of complications. 4.2 Instances of artificial intelligence in early diagnosis of influenza A (H1N1) virus infection Artificial intelligence has been widely applied in the early diagnosis of influenza A (H1N1) virus infection, showcasing a diverse range of examples. For instance, by analyzing patient medical records and clinical symptoms, artificial intelligence can quickly screen whether patients are infected with the flu virus. It can automatically analyze a large volume of patient data and identify key features associated with influenza virus infection, such as coughing, fever, and respiratory distress. This aids doctors in early detection and identification of infection cases, enabling prompt measures for isolation and treatment (Lee and Ahn, 2020). On the other hand, artificial intelligence also finds applications in medical imaging. By studying and analyzing imaging data such as X-rays, CT scans, and magnetic resonance imaging (MRI) related to influenza virus infection, artificial intelligence can identify features associated with the infection. For example, it can assist doctors in detecting signs of lung infection, such as lesions and infiltrations. It helps provide earlier and more accurate diagnostic results and aids doctors in formulating appropriate treatment plans. Furthermore, AI-based virus detection methods have been widely applied. Traditional virus detection typically requires complex laboratory equipment and techniques, taking a considerable amount of time to produce results. With the support of AI, machine learning techniques can be employed to rapidly analyze and identify viruses in patient samples such as blood, saliva, or nasopharyngeal swabs. This significantly reduces the time required for virus detection, accelerates the diagnostic process, and improves treatment efficacy. 4.3 Using artificial intelligence to predict the transmission trend of influenza A (H1N1) virus Researchers utilize artificial intelligence techniques to analyze and predict the transmission trend and scale of influenza A (H1N1) virus by examining historical case data and external factors. The model takes into account
Molecular Pathogens 2024, Vol.15, No.1, 1-8 http://microbescipublisher.com/index.php/mp 6 various factors such as geographic location, climate changes, and socio-economic factors that influence virus transmission, providing a scientific basis for formulating prevention and control measures. Through this approach, it can better guide vaccination strategies, allocation of healthcare resources, and the implementation of prevention and control measures (Huang et al., 2020). To predict the transmission trend of influenza A (H1N1) virus using artificial intelligence, a large amount of historical case data needs to be collected. This data includes information such as patient symptoms, signs, laboratory test results, and treatment history. By analyzing and learning from this data, the AI model can identify patterns and influencing factors related to the transmission of influenza A (H1N1) virus. The AI model utilizes machine learning and deep learning algorithms for prediction. By analyzing historical case data and external factors, the model can build a predictive model and validate and adjust it using new data. This prediction method enables rapid and accurate forecasting of the virus's transmission trend and scale. 5 The Potential of Artificial Intelligence in Early Diagnosis of Influenza A (H1N1) Virus Infection 5.1 How artificial intelligence improves the accuracy and efficiency of early diagnosis Artificial intelligence has significant potential to improve the accuracy and efficiency of early diagnosis in the medical field. AI utilizes big data analytics to analyze vast amounts of medical data, including patient medical records, laboratory test results, and medical imaging, to identify potential disease patterns and risk factors. This can assist healthcare professionals in swiftly and accurately diagnosing early-stage conditions, thereby enhancing diagnostic accuracy (Yan et al., 2021). Artificial intelligence harnesses machine learning and deep learning algorithms to construct intelligent diagnostic models. These models are trained based on existing medical knowledge and data, continuously optimizing their diagnostic capabilities. Compared to traditional clinical decision-making methods, AI models can consider a wide range of factors comprehensively and make comprehensive judgments on patients' conditions. By assisting physicians in preliminary diagnosis, AI can help improve diagnostic efficiency and reduce the risks of misdiagnosis and missed diagnosis. Artificial intelligence can also be applied to medical image analysis. Through deep learning algorithms, AI can automatically identify and label areas of pathology in medical images, assisting physicians in assessing the disease. This not only improves the detection rate of early-stage diseases but also speeds up the work pace of physicians and enhances efficiency. The application of artificial intelligence in medical image analysis has already achieved significant success in the early diagnosis of certain diseases. 5.2 Special advantages and challenges of artificial intelligence in early diagnosis Artificial intelligence has unique advantages in early diagnosis, including the ability to process large amounts of data and provide rapid diagnoses. However, it also needs to address challenges such as data quality, privacy and security, and interpretability to further develop and apply its potential in the medical field. AI performs exceptionally well in handling large volumes of medical data. It can analyze various types of medical images, laboratory results, and medical records to provide fast and accurate diagnostic results. Through machine learning and deep learning algorithms, AI can learn and recognize complex disease patterns and features, aiding in the identification of early signs of diseases (El Khatib and Ahmed, 2019). AI can also provide fast diagnostics and decision support. It can analyze large amounts of data within a short period of time and provide immediate diagnostic suggestions to healthcare professionals. In complex cases, AI can uncover subtle features that doctors may overlook or find difficult to detect. It helps doctors improve accuracy and efficiency and enables timely implementation of appropriate treatment measures.
Molecular Pathogens 2024, Vol.15, No.1, 1-8 http://microbescipublisher.com/index.php/mp 7 However, AI also faces challenges in early diagnosis. Data quality and availability are important issues. AI requires a large amount of high-quality data for training and validation, but in some medical fields, data collection and sharing still have limitations. Moreover, the privacy and security of medical data need to be carefully considered to avoid the leakage of sensitive information. The interpretability of AI models is also a challenge. While AI can provide accurate diagnostic results, explaining the reasons and process behind them may not be intuitive. Understanding and accepting the decision-making processes of AI for physicians and patients is an important issue. Therefore, researchers need to propose effective methods to explain and visualize the decision-making processes of AI models to increase human trust and acceptance. 5.3 Future development trends of artificial intelligence in early diagnosis With the continuous advancement of technology, the application of artificial intelligence in medical imaging will become more precise and efficient. New algorithms and deep learning techniques will enable AI to accurately identify and interpret abnormalities in medical images, helping doctors detect disease signs earlier. The application of artificial intelligence in early diagnosis will also expand to other fields such as genomics and molecular medicine. By analyzing genetic data and cellular signals, AI can assist doctors in gaining a better understanding of disease mechanisms and provide personalized diagnosis and treatment plans based on individual genetic variations. In the future, the development of artificial intelligence in early diagnosis will also involve the integration and analysis of multimodal data. Medical data often involves multiple types of information, such as medical images, laboratory results, and medical records. Integrating and analyzing these data will help doctors form more comprehensive and accurate early diagnosis results. The development of artificial intelligence in early diagnosis also requires close integration with clinical practice. Through collaboration and feedback from doctors, AI can continuously optimize and improve diagnostic algorithms, enhancing their practicality and usability in clinical settings. 6 Summary and Prospect Artificial intelligence has made important contributions and value in the early diagnosis of A (H1N1) influenza virus infection. By analyzing patients' medical records and clinical symptoms, artificial intelligence can quickly and accurately screen whether patients may be infected with the influenza virus. It helps doctors to promptly detect infection cases and implement appropriate isolation and treatment measures, thereby reducing the spread and severity of the epidemic. The application of artificial intelligence in medical image analysis has provided new breakthroughs for diagnosis. Through deep learning and image recognition techniques, artificial intelligence can automatically analyze and identify lung manifestations related to influenza virus infection, such as lesions and infiltrates. This not only enables doctors to obtain accurate diagnostic results quickly but also reduces dependence on experts, improving diagnostic accuracy and efficiency (Hegde et al., 2022). Given the successful application of artificial intelligence in the early diagnosis of A (H1N1) influenza virus infection, future research can focus on the following aspects. First, ongoing data collection and model optimization should be continued. In the early diagnosis of influenza virus infection, collecting more clinical and imaging data is crucial for improving the accuracy and robustness of artificial intelligence models. Simultaneously, algorithms and models need to be further optimized to enhance sensitivity and specificity for influenza virus infection. Future research can explore the application of artificial intelligence in influenza outbreak prediction and monitoring. By collecting a large amount of influenza-related data, such as symptoms, population movements, and social media data, artificial intelligence can help predict and monitor the spread trends and high-risk regions of influenza outbreaks. This will contribute to the formulation of more effective intervention measures and resource allocation to tackle the challenges of influenza outbreaks.
Molecular Pathogens 2024, Vol.15, No.1, 1-8 http://microbescipublisher.com/index.php/mp 8 The successful experience of artificial intelligence in the early diagnosis of influenza virus infection can provide inspiration for research on early diagnosis of other infectious diseases. Applying similar methods and techniques to the early diagnosis of other infectious diseases such as pneumonia, tuberculosis, etc., is expected to make greater progress in the prevention and control of infectious diseases. Conflict of Interest Disclosure The author affirms that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Benjamin H., Sumeet H., and Richard W. L., 2022, The role of artificial intelligence in early cancer diagnosis, Cancers, 14(6): 1524. https://doi.org/10.3390/cancers14061524 El Khatib M.M., and Ahmed G., 2019, Management of artificial intelligence enabled smart wearable devices for early diagnosis and continuous monitoring of CVDS, International Journal of Innovative Technology and Exploring Engineering, 9(1):1211-1215. https://doi.org/10.35940/ijitee.L3108.119119 Hegde S., Ajila V., Zhu W., and Zeng C.H., 2022, Artificial intelligence in early diagnosis and prevention of oral cancer, Asia-Pacific Journal of Oncology Nursing, 9(12): 100133. https://doi.org/10.1016/j.apjon.2022.100133 Huang S.G., Yang J., Fong S., and Zhao Q., 2020, Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges, Cancer Letters, 471: 61-71. https://doi.org/10.1016/j.canlet.2019.12.007 Lee K.S., and Ahn K.H., 2020, Application of artificial intelligence in early diagnosis of spontaneous preterm labor and birth, Diagnostics, 10(9): 733. https://doi.org/10.3390/diagnostics10090733 Lin C., Lin C.S., Lee D.J., Lee C.C., Chen S.J., Tsai S.H. Kuo F.C., Chau T., and Lin S.H., 2021, Artificial intelligence-Assisted electrocardiography for early diagnosis of thyrotoxic periodic paralysis, Journal of the Endocrine Society, 5(9): 120. https://doi.org/10.1210/jendso/bvab120 María G.P., Eduardo P.F., Carlota S.F., Juan S.R., Amparo R.M., and Pia L.J., 2021, Role of artificial intelligence in the early diagnosis of oral cancer. A scoping review, Cancers, 13(18): 4600. https://doi.org/10.3390/cancers13184600 Michaelis M., Doerr H.W., and Cinatl Jr J., 2009, An influenza A H1N1 virus reviva l- pandemic H1N1/09 virus, Infection, 37: 381-389. https://doi.org/10.1007/s15010-009-9181-5 Mintz Y., and Brodie R., 2018, Introduction to artificial intelligence in medicine, Minimally Invasive Therapy & Allied Technologies, 28(2): 73-81. https://doi.org/10.1080/13645706.2019.1575882 Swine O., 2009, Emergence of a novel swine-origin influenza A (H1N1) virus in humans, N Engl J Med., 360: 2605-2615. https://doi.org/10.1056/NEJMoa0903810 Winter P., and Carusi A., 2022, Validation and the Co-Constitution of Trust in Developing Artificial Intelligence (AI) for the Early Diagnosis of Pulmonary Hypertension (PH), Science & Technology Studies, 35(4): 58-77. Yan W., Shi H., He T., Chen J., Wang C., Liao A.J., Yang W., and Wang H.H., 2021, Employment of artificial intelligence based on routine laboratory results for the early diagnosis of multiple myeloma, Front. Oncol., 11. https://doi.org/10.3389/fonc.2021.608191
Molecular Pathogens 2024, Vol.15, No.1, 9-16 http://microbescipublisher.com/index.php/mp 9 Review and Progress Open Access Relationship between HIV Mutation and Host Antibody Response Wei Zhang Institute of Life Science, Jiyang College of Zhejiang A&F University, Zhuji, 311800, Zhejiang, China Corresponding email: 2013478397@qq.com Molecular Pathogens, 2024, Vol.15, No.1 doi: 10.5376/mp.2024.15.0002 Received: 20 Nov., 2023 Accepted: 25 Dec., 2023 Published: 20 Jan., 2024 Copyright © 2024 Zhang, This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Zhang W., 2024, Relationship between HIV mutation and host antibody response, Molecular Pathogens, 15(1): 9-16 (doi: 10.5376/mp.2024.15.0002) Abstract The variation of the HIV virus is caused by its high mutability and the accumulation of errors during replication. Mutation leads to the production of different subtypes and variants of the virus, which in turn affects the antibody response of the host immune system. This study reviews the mutual relationship between HIV virus variation and host antibody response. Antibodies, as a major component of the immune system, play a crucial role in the process of infection. This study provides a systematic overview of the mechanisms underlying HIV-1 viral variation and the different types of mutations. It also discusses the mechanisms of host antibody response, the structure and function of antibodies, and the impact of viral variation on antibody recognition and binding, particularly the challenges posed by antibody escape mutations. The study comprehensively analyzes existing research methods and technologies, such as genetic sequencing and monoclonal antibody techniques, and emphasizes their importance in studying HIV-1 viral variation and antibody responses. The study concludes by summarizing the challenges and future directions in research, including strategies for antibody vaccine development, dynamic modeling of virus evolution and antibody responses, and prospects for new therapeutic strategies and drug development. Keywords HIV virus variation; Host antibody response; Mutant strains; Antibody escape; Viral evolution 1 Introduction Since the discovery of the human immunodeficiency virus (HIV) in the early 1980s, it has been a significant global public health concern. This terrible virus has claimed millions of lives and continues to affect individuals across the globe. HIV belongs to the retrovirus family and specifically the lentivirus sub-group, attacking the immune system and leading to acquired immunodeficiency syndrome (AIDS). The virus is renowned for its high variability, posing significant challenges to treatment and prevention through evolution (Campestrini et al., 2018). It is estimated that approximately 38 million people are infected with HIV globally, making the epidemic a pressing global health concern. The virus is primarily transmitted through unprotected sexual behavior, shared contaminated needles, and mother-to-child during pregnancy, delivery, or breastfeeding (Ishay et al., 2020). Despite significant advancements in antiretroviral therapy (ART) in recent years, there is still no cure for HIV. Management of HIV infection focuses on suppressing viral replication, delaying disease progression, and reducing transmission risks (Kesby et al., 2018). However, the emergence of drug resistance and an increase in viral strain diversity pose significant challenges to effective treatment and prevention. It is evident that a deeper understanding of the interplay between HIV variants and host antibody responses is crucial. Understanding how virus variants impact the immune system and how host antibodies respond to these variants can provide a better understanding of HIV pathogenesis and viral escape mechanisms. In-depth research on virus variants and antibody responses can provide novel insights and targets for vaccine and antiviral treatment strategies (Cassandra et al., 2019). The primary aim of this study is to review the interplay between HIV variants and host antibody responses, exploring its significance in virus control and disease prevention. The study will analyze the mechanisms and influencing factors of HIV variants, describe the interactive mechanisms of virus escape and antibody responses,
Molecular Pathogens 2024, Vol.15, No.1, 9-16 http://microbescipublisher.com/index.php/mp 10 and discuss the application of existing research methods and technologies in addressing this issue. The findings will contribute to enhancing our understanding of HIV infection, its treatment, and prevention, providing scientific evidence for future research and intervention development. 2 Important Variant Types of HIV Virus 2.1 Basic concepts of mutation and evolution HIV is an RNA virus whose genome consists of RNA molecules instead of the more common DNA (Figure 1). It is the causative agent of AIDS. The virus is transmitted through blood, sexual contact, or mother-to-child during pregnancy, delivery, or breastfeeding. HIV infection mainly attacks the body's immune system, particularly CD4+ T lymphocytes, disrupting the function of the immune system. As a result, the patient's immune system becomes fragile and vulnerable to other infections and diseases. HIV is classified into multiple subtypes and serotypes based on differences between different regions and individuals. The most common ones are HIV-1 and HIV-2. HIV-1 is the most prevalent subtype globally, while HIV-2 is prevalent mainly in West Africa. HIV has a high degree of variability, which means it can produce different mutations in infected individuals and between populations (Robert et al., 2019). This variability can affect the effectiveness of vaccine development and antiviral treatment strategies. HIV mutation and evolution refers to the changes and accumulation of genetic information produced by the HIV genome during replication. Due to the high error rate during HIV replication, new mutant strains are generated. This high variability is one of the important reasons why HIV can evade the host immune system and antiviral drugs (Han et al., 2021). Figure 1 The biological characteristics of the human immunodeficiency virus (HIV) (Overview of HIV, 2016) HIV mutations are mainly caused by the error replication of its reverse transcriptase. Reverse transcriptase is an enzyme that converts the virus's RNA into DNA and integrates it into the host cell's chromosomes. However, reverse transcriptase is prone to making mistakes during replication, leading to mutations in the newborn virus genome (Figure 1). These mutations can be point mutations, where a single nucleotide changes, or insertions or deletions, where nucleotides are added or deleted. These mutations lead to changes in the HIV genome and the emergence of various subtypes and strains (Inciarte et al., 2020). HIV evolution occurs when mutations accumulate and selection pressures shape new virus strains in different environments and hosts. Selection pressures can come from the host immune system and antiviral drug applications. When the host immune system produces antibodies against the virus, pressure urges the virus to change the structure of its surface proteins to evade antibody recognition. This antibody escape mechanism leads to mutations in the virus and confers immune evasion. Similarly, antiviral drug use selects for virus strains with
Molecular Pathogens 2024, Vol.15, No.1, 9-16 http://microbescipublisher.com/index.php/mp 11 reduced drug sensitivity, leading to drug resistance. 2.2 Cause and mechanism of virus mutation The reasons and mechanisms of HIV mutation include the high error rate of reverse transcriptase, rapid reproduction and replication, as well as selection pressure and cross-infection. Reverse transcriptase is a key enzyme in the replication process of HIV, responsible for converting the virus's RNA into DNA. However, reverse transcriptase has a high error rate, which means mutations can occur during each replication process. These mutations can lead to errors or deletions in the genome, thereby affecting the survival and replication of the virus. HIV has the ability to rapidly reproduce and replicate, producing billions of virus particles every day. During this rapid replication process, viruses often experience replication errors. The accumulation of these errors due to the large number of viruses leads to frequent mutations. These mutations can be point mutations (changes in a single base pair), insertions or deletions (insertion or deletion of DNA fragments), or gene recombination (exchange of genes between different HIV strains). Selection pressure and cross-infection are also important factors in HIV mutation. The attack by the host immune system on the virus creates selection pressure, selecting for those mutant strains that can evade immune responses. These mutant strains gradually become dominant in the population, ultimately leading to immune escape. When different HIV strains infect the same host, their genomes can undergo recombination, forming new mutant virus strains. These mutations and variations make HIV complex and diverse, with the ability to evade the immune system and antiviral drugs. This poses a significant challenge to the treatment and prevention of the disease, and requires research and efforts to find effective strategies to combat HIV. 2.3 The impact of mutations on viruses Mutations have various impacts on HIV. Mutations make HIV highly variable, which means there is a large genetic difference between virus strains. This variability poses difficulties for antiviral treatment and vaccine development, as a specific drug or vaccine may be effective against one virus strain but not another. Mutations also lead to the development of drug resistance, rendering previously effective drugs ineffective. Mutations allow HIV to evade recognition and attack by the host immune system. Viral mutations can lead to changes in the structure of surface proteins (such as the HIV envelope protein gp120), enabling the virus to evade antibodies produced by the host. This antibody evasion mutation makes it difficult for the immune system to effectively fight the virus, increasing the severity and progression of the disease. Mutations can also affect the virulence and transmissibility of HIV. Some mutations may increase the infectiousness of the virus, making it more likely to spread through sexual or blood transmission to others. Mutations can also affect the replication rate and efficiency of the virus, thereby affecting its pathogenic potential and course of the disease. 3 Interaction between Host Antibody Response and AIDS Virus 3.1 Response mechanism of host immune system to AIDS virus infection The host immune system plays a crucial role in the response to HIV infection, and it employs multiple mechanisms to counteract the virus's attack. Firstly, the host immune system identifies and attacks cells infected with HIV. After infection, the virus releases some viral proteins that can be recognized as "foreign invaders" by immune system cells. The immune system tags these infected cells and destroys them through immune cells such as cytotoxic T cells (Mueller et al., 2018) (Figure 2). To combat HIV, the host immune system produces specific antibodies. After infection, the immune system activates B cells and prompts them to produce specific antibodies. These antibodies can recognize and bind to the surface proteins of the virus, thereby preventing further infection of host cells and prompting immune cells to clear the virus that is marked by antibodies. The host immune system can regulate and control immune responses
Molecular Pathogens 2024, Vol.15, No.1, 9-16 http://microbescipublisher.com/index.php/mp 12 through the production of cytokines. Cytokines are secreted proteins that serve as signals and regulators between immune cells. In HIV infection, the production of certain cytokines is modulated, thereby affecting the activity and efficacy of immune cells (van Zyl et al., 2018) (Figure 2). Figure 2 Interaction between HIV and immune system (van Zyl et al., 2018) However, HIV has multiple mechanisms to evade the host immune response. The virus can change the structure of its surface proteins through diversity mutations and recombination, thereby avoiding recognition and attack by antibodies. Additionally, the virus can suppress the activity of the host immune system, disrupt the function of immune cells, or inhibit cytokine production. These evasion mechanisms make the host immune response to HIV infection complex and difficult, providing opportunities for sustained infection and immune escape by the virus. 3.2 Antibody Structure and Function Antibodies are a type of protein produced by the immune system, also known as immunoglobulins. Antibodies have a specific structure and function that play an important role in defending against infections and protecting the body from pathogenic invasion. The structure of antibodies is highly unique, typically consisting of two heavy chains and two light chains that form a Y-shaped molecule. They are connected together by disulfide bonds at their C-terminal ends. Each antibody has specificity due to the distinct amino acid sequence of its variable region (hypervariable region), which allows it to recognize and bind to a specific antigen. The variable region of antibodies is generated by genetic recombination and mutation, providing diversity that enables recognition and binding to various antigens (Sanchez et al., 2018). The function of antibodies is mainly manifested in two aspects. First, antibodies can directly neutralize the toxins or antigens of pathogens, preventing them from entering or invading host cells. When antibodies bind to a pathogen, they can block its attachment to host cells, thereby preventing invasion and damage. Second, antibodies can enhance the immune response of the body by activating other components of the immune system. This includes activating the complement system, inducing inflammatory responses through cytotoxic functions, and promoting the destruction and clearance of antigens by other immune cells. Antibodies also have other functions, such as regulating immune responses and mediating the activity of immune cells. They can interact with specific immune cells, such as macrophages, NK cells, and other immune cells, thereby regulating the activity and response of the host immune system. These functions make antibodies play a crucial role in various aspects of immune responses. 3.3 The impact of virus mutation on the recognition and binding ability of antibodies The high variability of HIV has a significant impact on the recognition and binding ability of antibodies, posing a significant challenge in the development of broad-spectrum, high-affinity antibody-based drugs and vaccines. The
Molecular Pathogens 2024, Vol.15, No.1, 9-16 http://microbescipublisher.com/index.php/mp 13 genetic material of HIV, RNA, undergoes frequent mutations and recombinations, leading to the emergence of a wide range of subtypes and variant strains within the virus population. This variability enables the virus to evade attack by the host immune system, including antibody recognition and binding (Teresa et al., 2019). Antibodies recognize antigens on the surface of pathogens through their specific structure, with variable regions that have specific amino acid sequences capable of binding to specific areas on the surface of the pathogen. However, due to the variability of HIV, the antigenic epitopes on the surface of the virus can undergo changes, leading to mutations or the disappearance of regions that were previously bound by antibodies. These changes in antigenic epitopes can render antibodies unable to recognize and bind to the virus as the antigenic targets have been altered. This variability can occur in surface proteins of the virus (such as the Env protein of HIV) or other key proteins that play a critical role in the binding process of antibodies. The variability of HIV also leads to the emergence of multiple subtypes and variant strains with distinct epitope and structural characteristics. This makes it challenging for a single antibody to cover all subtypes and variant strains as antibodies can only bind to specific antigens. Therefore, an antibody may have strong binding capacity for one subtype or variant strain but may be ineffective against other subtypes or variant strains. 4 The Consequences of Antibody Escape Mutation and Viral Infection 4.1 The definition and mechanism of antibody escape mutation Antibody escape mutation refers to the ability of HIV, due to its high degree of variability and high replication rate, to produce mutations that allow the virus to evade attack by the host immune system's antibodies during infection. This mutation enables the virus to avoid antibody recognition and binding, thereby protecting itself from immune system attacks (Liu et al., 2019). The mechanisms of antibody escape mutation mainly include point mutations and structural changes. Point mutations refer to changes in individual nucleotides in the viral genome, leading to changes in the antigenic epitopes recognized by antibodies. These mutations may alter the structure, charge, or affinity of epitopes, making it difficult for antibodies to effectively bind. The virus can also weaken or prevent antibody binding by changing the amino acid sequence surrounding the antibody binding site (Montoya et al., 2018). Structural changes refer to the structural modifications of epitopes through mechanisms such as deletion, insertion, or rearrangement during infection. These changes can alter the structure and affinity of antibodies for the virus, making antibodies lose their specificity for the epitopes recognized by the original antibodies. Antibody escape mutation plays a crucial role in HIV transmission and viral replication. This mutation allows the virus to evade host immune system surveillance, establish persistent infection in hosts, and lead to further virus transmission and development. Given the antigenic variability of HIV, developing broad-spectrum antibody-based drugs and vaccines that target different subtypes and variant strains remains a challenge. 4.2 The impact of antibody escape mutation on viral infection Due to the high degree of virus variability and the existence of escape mechanisms, antibody escape mutation enables the virus to effectively evade the host immune system's antibody attack, thereby increasing the persistence and replicative capacity of the virus infection. Antibody escape mutation allows the virus to avoid being quickly cleared by antibodies. When the virus enters the host, the immune system produces specific antibodies to recognize and bind to virus particles, thereby neutralizing or marking the virus for clearance by the host immune system. However, HIV has developed new antigenic epitopes through mutation, avoiding specific antibody binding and making it difficult for the host immune system to effectively clear the infection. This allows the virus to persist and replicate within the host. Antibody escape mutation also leads to the development of drug resistance in the virus. During antiviral treatment, selective pressure can guide the virus to develop drug-resistant mutations, making previously sensitive antiviral
RkJQdWJsaXNoZXIy MjQ4ODYzNA==