Implant-associated infections pose significant challenges for diagnosis and treatment due to the formation of biofilm on device surfaces. Biofilms are complex microbial communities that adhere to surfaces and are encased in a self-produced extracellular polymeric substance (EPS) matrix. Their presence on implants can lead to persistent infections, implant failure, and increased morbidity and mortality. This review explores the role of biofilms in the pathogenesis of implant-associated infections and examines current strategies for their diagnosis and prevention.

Several wars and pandemics have demonstrated the serious impacts of infectious diseases on the military, highlighting the importance of infection control. As a national defense organiza-tion, the military operates within specific cultural and environmental contexts distinct from civilian organizations. The unique conditions of war necessitate extensive efforts to prevent in-fections. Therefore, a strategic approach based on the understanding of its unique strengths and challenges is essential for proper infection control in the military. As the military is nationally dispersed and in contact with the surrounding communities, the readiness of this organization,along with its personnel, resources, and operational systems, is crucial for the protection ofsoldiers and civilians during infectious disease outbreaks. Therefore, joint efforts and academic and practical exchanges between the military and civilian sectors based on the comprehensiveunderstanding of the military conditions and operations are necessary for prompt response to and control of infectious diseases.

Effective healthcare policies, such as vaccination, decreased the global prevalence of infectious diseases, such as pertussis. However, these diseases have recently re-emerged, posing a serious public health threat. This article discusses the recent pertussis outbreak in Korea, outlining its clinical symptoms and highlighting the relevant diagnostic tools and management strategies to prevent its re-emergence. Incidence of pertussis in South Korea has been increasing since 2015, with over 30,000 cases reported until November 1, 2024, marking the highest number of cases recorded since the 2000s. Although pertussis is fatal in infants, it can be prevented via maternal vaccination. However, in recent years, pertussis vaccination rate during pregnancy has remained at approximately 60%, which is insufficient to prevent neonatal pertussis. Notably, vaccination rates among adolescents and adults are even lower than those among children, leading to the rapid increase in pertussis infection in the post-adolescent and vulnerable populations. Therefore, effective strategies to promote the vaccination of adults, especially pregnant women, are necessary to prevent and control such re-emerging infectious diseases.

Background: This study investigates changes in infection control organizations and the roles of infection control nurses in healthcare institutions during the prolonged COVID-19 pandemic. It aims to provide evidence for preparing effective responses to future emerging infectious diseases. Methods: A survey was conducted from August 9 to 27, 2021, targeting infection control nurses working in infection control departments of general hospitals in South Korea with more than 100 beds. Results: A total of 118 (38.3%) healthcare institutions responded, and 113 questionnaires ultimately analyzed. Changes in infection control organizations observed during the COVID-19 pandemic included alterations in infection control department staffing (34.5%), work arrangements (92.0%), IT systems development (45.1%), facilities and equipment (92.9%), and systems for responding to emerging infectious diseases (99.1%). Over 50% of infection control tasks were performed either frequently or daily during the pandemic, with the most significant workload increase observed in epidemiological investigations (77.9%). The most challenging task for infection control nurses was establishing and operating epidemic management systems (82.3%). Among personal factors hindering their work, a sense of responsibility for their duties scored the highest (mean 4.73/5 points). The primary structural and environmental barrier was insufficient staffing (mean 4.36/5 points). The most critical need for improving infection control efforts was increased staffing in infection control departments and enhanced administrative support (mean 4.63/5 points). Conclusion The COVID-19 pandemic brought significant changes to infection control organizations and the roles of infection control nurses. Based on the lessons learned, healthcare institutions must systematically restructure infection control organizations and enhance the expertise of infection control nurses to respond effectively to future outbreaks of emerging infectious diseases.

Background: Healthcare workers (HCWs) face an increased risk of airborne infections owing to close patient contact and extended shifts. N95 respirators require routine fit testing to ensure effective protection. This study assessed the fit stability of N95 masks among nurses working in airborne infection units, using annual fit tests to observe changes over time. Methods: From May 2020 to January 2023, 37 nurses from the high-risk airborne infection units of a university hospital participated in this study. The fit tests followed the Occupational Safety and Health Administration’s Quantitative Fit Testing protocol, and non-parametric statistical analyses were applied. Nurses received formal annual training on N95 mask use and individualized guidance before each fit test. Results: In the initial test, 46.0% passed; of these, 15 consistently passed the second test (Group 1, Pass-Pass) and 2 failed (Group 2, Pass-Fail). No significant associations were found between demographic or job factors and the fit test outcomes. Average fit scores decreased from 100.79 (±58.22) initially to 32.25 (±66.54) in the second test, showing variability. Conclusion Ongoing training and experience improved fit consistency over time, emphasizing the importance of regular education. Rather than testing annually, targeted testing when selecting a new respirator or performing subsequent facial changes may enhance the protection of HCWs and reduce costs.

The hospital water environment serves as a reservoir for Gram-negative multidrug-resistant organisms. Drains, sinks, drink dispensers, toilets, and shower equipment were identified as sources of the outbreak. Plumbing systems with variable nutrient and microbial loads promote bacterial colonization and biofilm formation. Plumbing traps (P-traps) in sinks are the primary source of sink-related outbreaks. Pathogens are introduced into P-traps through handwashing and disposal of waste or nutrients. Some pathogens can survive and develop biofilms in P-traps. When nutrients are introduced, the biofilm can extend upwards, reaching the sink strainer. During faucet operation, aerosols and drain contents are dispersed into the surrounding environment. Guidelines for handwash basin design generally recommend that large basins contain splashes and taps that are not aligned directly over the drains to minimize aerosol generation. Ensuring that basins are not used for the disposal of patient-related waste or nutrients and prohibiting the storage of clean patient material near the sink is important. Daily cleaning and disinfection of sink surfaces with bleach (sodium hypochlorite) are essential. Additionally, self-disinfecting traps that use heat, vibration, or ultraviolet radiation have been reported to reduce microbial burden and prevent further biofilm formation. Eradicating sink and drain contamination is often difficult and outbreaks can recur. Even after the replacement of the sink, the sink component had only temporary effects, suggesting a persistent reservoir in the retained sink fit. Further research is needed to develop continuous and effective disinfection methods for sinks and drains.

Background: Hospital environments, particularly shared rooms, are vulnerable to the transmission of carbapenem-resistant Enterobacterales (CRE). The incidence of CRE colonization in the Korean homeless population remains unknown. This study aimed to analyze the impact of targeted active surveillance of CRE in hospital wards following two outbreaks. Methods: This retrospective study was conducted in a homeless ward with shared rooms at a municipal hospital in Seoul. The CRE incidence was calculated from October 1, 2023, to May 31, 2024. Active surveillance was initiated on January 22, 2024. Poisson regression analysis was used to compare CRE incidence events at three months before and four months after the intervention. The risk factors for CRE colonization were also analyzed. Results: The CRE colonization rate decreased from 1.149 to 0.815 per 1,000 patient-days post-intervention; however, the change was not statistically significant (rate ratio: 0.986, 95% confidence interval (95% CI): 0.389-2.496, P=0.976). In contrast to the secondary cases, one acquired CRE case was detected after the intervention without an outbreak. The CRE colonization rate was higher in the homeless ward than in the general ward. CRE colonization was significantly associated with age (adjusted odds ratio (aOR), 1.071; 95% CI: 1.014-1.132;P=0.014), previous antibiotic exposure (aOR, 6.796; 95% CI: 1.215-38.029; P=0.029), and co-colonization with other multidrug resistant bacteria (aOR, 7.168; 95% CI: 2.224-23.096;P=0.001). Conclusion A relatively high incidence of CRE colonization was observed in the homeless ward. After active surveillance, no CRE outbreaks occurred following the implementation.

This paper examines the epidemiology and characteristics of avian influenza, with a focus on its potential to cause pandemics. Avian influenza, caused by type A influenza viruses, is an acute viral infection primarily affecting wild birds and rarely transmitting to humans. However, genetic reassortment and human-adaptive mutations can lead to emergence of novel strains with pandemic potential. This paper highlights the evolution and global spread of avian influenza viruses, particularly the H5 and H7 subtypes, which have occasionally infected humans and various mammals. Although predicting pandemics remains impossible, pandemic risk assessment plays a crucial role in prioritizing investments in influenza preparedness, such as vaccine development. It also identifies knowledge gaps, guides further research, ensures transparent decision-making based on scientific evidence, and promotes effective communication among policymakers, experts, and the public. This review provides insights from current pandemic risk assessments for various avian influenza viruses, emphasizing the importance of vigilant monitoring and pre-pandemic vaccine development to mitigate the threat of future influenza pandemic.

Background: The COVID-19 pandemic has disrupted healthcare systems worldwide, with overwhelmed facilities leading to high morbidity and mortality rates. Deep learning models that predict patient severity can aid in optimizing resource allocation and patient monitoring.However, conventional models rely on excessive clinical features, reduce generalizability, and fail to provide real-time severity tracking. This study proposes a sequence-to-sequence (Seq2Seq) deep-learning model for predicting COVID-19 severity using minimal clinical features. Methods: Data from 4,462 patients from two tertiary care hospitals in Daegu, Korea (2020– 2022) were used to train the model, with 442 external validation cases collected from the National Institute of Health in Korea. Seq2SeqAttn inputs the observation of 17 clinical features of at most five days and outputs the predicted severity level of up to three days. Results: The model achieved a 98% recall and 97.6% receiver operating characteristic curve for validation. Seq2SeqAttn correctly identified severe cases, with lactate dehydrogenase (LDH) and neutrophil-lymphocyte ratios significantly differing between the severity groups.Integrated gradients revealed that peripheral oxygen saturation and LDH levels were critical predictors. The model outperformed conventional severity assessment tools, such as the WHO Clinical Progression Scale and National Early Warning Score. Conclusion This study presented a real-time COVID-19 severity prediction model using minimal clinical features. The high accuracy and interpretability of the model demonstrates its potential to improve resource allocation and patient care during pandemics. Future studies should investigate its applicability to other respiratory and infectious diseases.