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Helicobacter pylori (H. pylori) is an infectious bacterium that colonizes the stomach of approximately half of the global population. It has been classified as a Group I carcinogen by the World Health Organization due to its strong association with an increased incidence of gastric cancer and exacerbation of stomach diseases. The primary treatment for H. pylori infection currently involves triple or quadruple therapy, primarily consisting of antibiotics and proton pump inhibitors. However, the increasing prevalence of antibiotic resistance poses significant challenges to this approach, underscoring the urgent need for an effective vaccine. In this study, a novel multi-epitope H. pylori vaccine was designed using immunoinformatics. The vaccine contains epitopes derived from nine essential proteins. Software tools and online servers were utilized to predict, evaluate, and analyze the physiochemical properties, secondary and tertiary structures, and immunogenicity of the candidate vaccine. These comprehensive assessments ultimately led to the formulation of an optimal design scheme for the vaccine. Through constructing a novel multi-epitope vaccine based on immunoinformatics, this study offers promising prospects and great potential for the prevention of H. pylori infection. This study also provides a reference strategy to develop multi-epitope vaccines for other pathogens.

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BACKGROUND: Infection with Helicobacter pylori (Hp) mostly occurs during childhood, and persistent infection may lead to severe gastric diseases and even gastric cancer. Currently, the primary method for eradicating Hp is through antibiotic treatment. However, the increasing multidrug resistance in Hp strains has diminished the effectiveness of antibiotic treatments. Vaccination could potentially serve as an effective intervention to resolve this issue. AIMS: Through extensive research and analysis of the vital protein characteristics involved in Hp infection, we aim to provide references for subsequent vaccine antigen selection. Additionally, we summarize the current research and development of Hp vaccines in order to provide assistance for future research. MATERIALS AND METHODS: Utilizing the databases PubMed and the Web of Science, a comprehensive search was conducted to compile articles pertaining to Hp antigens and vaccines. The salient aspects of these articles were then summarized to provide a detailed overview of the current research landscape in this field. RESULTS: Several potential antigens have been identified and introduced through a thorough understanding of the infection process and pathogenic mechanisms of Hp. The conserved and widely distributed candidate antigens in Hp, such as UreB, HpaA, GGT, and NAP, are discussed. Proteins such as CagA and VacA, which have significant virulence effects but relatively poor conservatism, require further evaluation. Emerging antigens like HtrA and dupA have significant research value. In addition, vaccines based on these candidate antigens have been compiled and summarized. CONCLUSIONS: Vaccines are a promising method for preventing and treating Hp. While some Hp vaccines have achieved promising results, mature products are not yet available on the market. Great efforts have been directed toward developing various types of vaccines, underscoring the need for developers to select appropriate antigens and vaccine formulations to improve success rates.

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Tuberculosis (TB) remains one of the gravest global health challenges. Mycobacterium tuberculosis (M. tuberculosis), the causative agent, employs sophisticated immune evasion and pathogenesis strategies. Its capability to thrive within immune cells and incite robust inflammatory responses prolongs infection and dissemination. Mycobacterial advanced adaptations facilitate navigation through the human immune system and present a variable antigenic profile throughout different infection stages. Investigating these strategies unfolds targeted approaches to effective vaccine development against TB. This review delves into the most advanced and exhaustive insights into the immune evasion tactics and pathogenic processes of M. tuberculosis across various infection stages. The knowledge distilled from this analysis holds the promise of guiding the creation of innovative TB vaccines and translating theoretical groundwork into practical immunological defenses.

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Millimeter-wave (MMW) radar is essential in roadside traffic perception scenarios and traffic safety control. For traffic risk assessment and early warning systems, MMW radar provides real-time position and velocity measurements as a crucial source of dynamic risk information. However, due to MMW radar's measuring principle and hardware limitations, vehicle positioning errors are unavoidable, potentially causing misperception of the vehicle motion and interaction behavior. This paper analyzes the factors influencing the MMW radar positioning accuracy that are of major concern in the application of transportation systems. An analysis of the radar measuring principle and the distributions of the radar point cloud on the vehicle body under different scenarios are provided to determine the causes of the positioning error. Qualitative analyses of the radar positioning accuracy regarding radar installation height, radar sampling frequency, vehicle location, posture, and size are performed. The analyses are verified through simulated experiments. Based on the results, a general guideline for radar data processing in traffic risk assessment and early warning systems is proposed.

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Background: Hepatocellular carcinoma (HCC), a major type of cancer affecting the lives of people across the world, is one of many diseases whose advancement is thought to be influenced by autophagy dysfunction. Here, the prognostic significance of autophagy in HCC will be investigated. Methods: The Cancer Genome Atlas (TCGA) database was employed in this work to identify 62 differentially-expressed Autophagy-Related Genes (ARGs) in HCC patients. Functional enrichment studies revealed that autophagy played a tumor-promoting role in the advancement of HCC. Based on RNA sequencing of 116 tumor samples and 114 paracancerous tissue samples acquired from HCC patients at the Guangxi Medical University Cancer Hospital, 866 differentially expressed prognosis-related genes were identified. Using lasso regression analysis, two ARGs (BIRC5 and BAK1) linked to prognosis were discovered after intersecting the differential genes derived from the prognosis-related groups. A risk score based on ARGs was developed using a Cox proportional hazards regression model. RNA sequencing data were used to construct this model. Finally, the TCGA and The Human Protein Atlas Databases (THPA) were used to validate the clinical data of 116 HCC patients. Results: Elevated expression level of the BAK1 and BIRC5 genes is linked to poor prognosis. The two ARGs were used to calculate the risk score as follows: (0.0253*BAK1) + (0.0051*BIRC5). Risk score served as the independent prognostic factor as per the multivariate analysis. TCGA, THPA, and data from the Guangxi Medical University Cancer Hospital were used to confirm the predictive validity of risk scores for the prognosis of HCC patients. Conclusion: This study offers molecular insights regarding the involvement of autophagy in HCC patients, along with a probable prognostic signature for determining the outcome (prognosis) of HCC patients.