Herd Immunity Research Articles

The evaluation of SARS-CoV-2 mutations at the early stage of the pandemic in Istanbul population

BackgroundDetermination of SARS-CoV-2 variant is significant to prevent the spreads of COVID-19 disease.MethodsWe aimed to evaluate the variants of SARS-CoV-2 rate in positive patients in Kanuni Sultan Suleyman Training and Research Hospital (KSS-TRH), Istanbul, Türkiye between 1st January and 30th November 2021 by using RT-PCR method.ResultsHerein, 825,169 patients were evaluated (male:58.53% and female:41.47%) whether COVID-19 positive or not [( +):21.3% and (−):78.7%] and 175,367 patient was described as positive (53.2%-female and 46.8%-male) by RT-PCR. COVID-19 positive rate is observed highest in the 6–15- and 66–75-year age range. The frequencies were obtained as SARS-CoV-2 positive (without mutation of B.1.1.7 [B.1.1.7 (U.K), E484K, L452R, B.1.351 (S. Africa/Brazil) spike mutations] as 66.1% (n: 115,899), B.1.1.7 Variant as 23.2% (n:40,686), Delta mutation (L452R) variant as 9.8% (n:17,182), B.1.351 variant as 0.8% (n:1370) and E484K as 0.1% (n: 230). In April 2021, general SARS-CoV-2 and B.1.1.7 variant were dominantly observed. Up to July 2021, B.1.617.2 (Delta variant/ Indian variant) and E484K has been not observed. B.1.351 variant of SARS-CoV-2 has been started in February 2021 at the rarest ratio and March 2021 is the top point. September 2021 is the pick point of E484K. African/Brazil variant of SARS-CoV-2 has been started in February 2021 at the rarest ratio and March 2021 is the top point. September 2021 is the pick point of E484K. When the gender type is compared within the variants, women were found to be more prevalent in all varieties.ConclusionsThe meaning of these mutations is very important to understand the transmission capacity of the COVID-19 disease, pandemic episode, and diagnosis of the virus with mutation types. Understanding the variant type is important for monitoring herd immunity and the spread of the disease.

Hybrid leader corona virus herd optimizer with multilevel thresholding techniques for foreground and background image segmentation

In computer vision and image processing, an important role is given to image segmentation. As it can accurately extract and identify specific regions within an image. One of the key challenges that remain in this is achieving precise recognition and extraction of segmented regions. This can be solved by multilevel optimization techniques and this offers an effective solution. Recently, multilevel thresholding has emerged as an important technique for image segmentation as the separation of image pixels into various classes by the selection of optimal threshold values. However, when the number of thresholds increases, the computational complexity of multilevel thresholding also increases. To address this issue, various optimization algorithms are employed by the researchers. Thus, the Hybrid optimization techniques are integrated to enhance the overall efficacy of image segmentation processes. Thus, this research devised a Multithreshold-Hybrid Leader Coronavirus Herd Optimizer+kernel based Bayesian fuzzy clustering (Multithreshold-HLCHO+kernel-BFC) for the segmentation of foreground and background images. Here, the noise from the input image is removed using the Non-Local Means (NLM) filter and also the region of interest (ROI) extraction is done. The foreground and background image segmentation is done using the multilevel thresholding techniques, wherein threshold values are optimally generated utilizing HLCHO with multiobjectives like Renyi, Masi and Tsallic. Moreover, the integration of Hybrid Leader Based Optimization (HLBO) with Coronavirus Herd Immunity Optimizer (CHIO) forms the HLCHO. Also, the kernel-based BFC is employed for the segmentation of background and foreground images. Finally, the segmented output is achieved by fusing these two outputs using a fusion process. Additionally, Multithreshold-HLCHO+kernel-BFC acquired a maximum value of 0.891 for dice coefficient, 37.174 dB for PSNR, 0.918 for uniformity measure, and 0.297 for Mean Squared error (MSE).

Measures of population immunity can predict the dominant clade of influenza A (H3N2) in the 2017-2018 season and reveal age-associated differences in susceptibility and antibody-binding specificity.

For antigenically variable pathogens such as influenza, strain fitness is partly determined by the relative availability of hosts susceptible to infection with that strain compared to others. Antibodies to the hemagglutinin (HA) and neuraminidase (NA) confer substantial protection against influenza infection. We asked if a cross-sectional antibody-derived estimate of population susceptibility to different clades of influenza A (H3N2) could predict the success of clades in the following season. We collected sera from 483 healthy individuals aged 1 to 90 years in the summer of 2017 and analyzed neutralizing responses to the HA and NA of representative strains using Focus Reduction Neutralization Tests (FNRT) and Enzyme-Linked Lectin Assays (ELLA). We estimated relative population-average and age-specific susceptibilities to circulating viral clades and compared those estimates to changes in clade frequencies in the following 2017-18 season. The clade to which neutralizing antibody titers were lowest, indicating greater population susceptibility, dominated the next season. Titer correlations between viral strains varied by age, suggesting age-associated differences in epitope targeting driven by shared past exposures. Yet substantial unexplained variation remains within age groups. This study indicates how representative measures of population immunity might improve evolutionary forecasts and inform selective pressures on influenza.

Neutrophil plasticity in liver diseases.

The liver has critical digestive, metabolic, and immunosurveillance roles, which get disrupted during liver diseases such as viral hepatitis, fatty liver disease, and hepatocellular carcinoma. While previous research on the pathological development of these diseases has focused on liver-resident immune populations, such as Kupffer cells, infiltrating immune cells responding to pathogens and disease also play crucial roles. Neutrophils are one such key population contributing to hepatic inflammation and disease progression. Belonging to the initial waves of immune response to threats, neutrophils suppress bacterial and viral spread during acute infections, and have homeostasis-restoring functions. Whereas during chronic insults, they display their plastic nature by responding to the inflammatory environment and develop new phenotypes alongside longer lifespans. This review summarizes the diversity in neutrophil function and subpopulations present at steady state, during liver disease, and during liver cancer.

Persistent anti-vaxxers and pandemic-induced new anti-vaxxers

Persistent anti-vaxxers and pandemic-induced new anti-vaxxers Read this analysis of persistent anti-vaxxers and pandemic-induced new anti-vaxxers by Fujio Toriumi, Professor at the University of Tokyo in Japan. During the COVID-19 pandemic caused by the novel coronavirus, anti-vaccine discourses pose a threat to public health by making it challenging to achieve herd immunity. Although studies describing anti-vaxxers’ characteristics have been accumulating, a lack of understanding prevails regarding the “triggers” that lead individuals to become anti-vaxxers.

Tumor-Associated Macrophages as Major Immunosuppressive Cells in the Tumor Microenvironment

Within the tumor microenvironment, myeloid cells constitute a dynamic immune population characterized by a heterogeneous phenotype and diverse functional activities. In this review, we consider recent literature shedding light on the increasingly complex biology of M2-like immunosuppressive tumor-associated macrophages (TAMs), including their contribution to tumor cell invasion and metastasis, stromal remodeling (fibrosis and matrix degradation), and immune suppressive functions, in the tumor microenvironment (TME). We also review the development of promising therapeutic approaches to target these populations in cancers. The expanding knowledge of distinct subsets of immunosuppressive TAMs, and their contributions to tumorigenesis and metastasis, has sparked significant interest among researchers regarding the therapeutic potential of TAM depletion or phenotypic modulation. This review delineates the involvement of M2-like TAM subsets in cancer development and metastasis, while also delving into the intricate signaling mechanisms underlying the polarization of diverse macrophage phenotypes, their plasticity, and therapeutic implications.

Epidemiology of peste des petits ruminants in selected districts of Borena zone, Ethiopia

BackgroundPeste des petits ruminant (PPR) is a contagious disease caused by the peste des petits ruminants virus (PPRV). The disease poses a significant economic threat to small ruminant production in Ethiopia, particularly to the striving pastoral production system. A cross-sectional study was conducted to estimate the seroprevalence and associated risk factors of PPR in the small ruminants of the Borena Zone. A total of 384 serum samples were collected randomly from sheep and goats and examined for the presence of PPRV antibodies using competition enzyme-linked immune sorbent assay (c-ELISA). Additionally, a retrospective analysis of five years of outbreak data was performed to provide insight into the spatial and temporal distribution of the disease.ResultsThe seroprevalence of PPRV antibodies in nonvaccinated, vaccinated, and unknown vaccination status of small ruminants in this study was found to be 32.1%, 68.8%, and 45.5%, respectively. Multivariable logistic analysis revealed a statistically significant association between PPRV seropositivity and several factors, including age, animal origin, flock size, and veterinary services status. A retrospective analysis revealed 53 PPR outbreaks in the Borena Zone from 2018 to 2022, exacerbated by low vaccination coverage relative to the at-risk animal population.ConclusionThe study revealed significant gaps in current vaccination efforts, with herd immunity levels falling below the FAO-WOAH recommended threshold of 80%. Despite Ethiopia’s ambitious goal to eradicate PPR by 2027, the frequent outbreaks and insufficient herd immunity highlight the inadequacy of the existing strategies. To effectively move toward eradication, Ethiopia must align its approach with the global PPR eradication framework, which emphasizes a comprehensive strategy that includes diagnostics, surveillance, prevention, and the establishment of a robust veterinary regulatory system, rather than relying solely on vaccination. Overcoming logistical challenges, improving vaccination coverage, and optimizing the timing of vaccination campaigns, especially in hard-to-reach areas, will be crucial for reducing outbreaks and making progress toward eradication.

Estimating the force of infection of four dengue serotypes from serological studies in two regions of Vietnam.

Dengue is endemic in Vietnam with circulation of all four serotypes (DENV1-4) all year-round. It is hard to estimate the disease's true serotype-specific transmission patterns from cases due to its high asymptomatic rate, low reporting rate and complex immunity and transmission dynamics. Seroprevalence studies have been used to great effect for understanding patterns of dengue transmission. We tested 991 population serum samples (ages 1-30 years, collected 2013 to 2017), 531 from Ho Chi Minh City and 460 from Khanh Hoa in Vietnam, using a flavivirus protein microarray assay. By applying our previously developed inference framework to the antibody profiles from this assay, we can (1) determine proportions of a population that have not been infected or infected, once, or more than once, and (2) infer the infecting serotype in those infected once. With these data, we then use mathematical models to estimate the force of infection (FOI) for all four DENV serotypes in HCMC and KH over 35 years up to 2017. Models with time-varying or serotype-specific DENV FOI assumptions fit the data better than constant FOI. Annual dengue FOI ranged from 0.005 (95%CI: 0.003-0.008) to 0.201 (95%CI: 0.174-0.228). FOI varied across serotypes, higher for DENV1 (95%CI: 0.033-0.048) and DENV2 (95%CI: 0.018-0.039) than DENV3 (95%CI: 0.007-0.010) and DENV4 (95%CI: 0.010-0.016). The use of the PMA on serial age-stratified cross-sectional samples increases the amount of information on transmission and population immunity, and should be considered for future dengue serological surveys, particularly to understand population immunity given vaccines with differential efficacy against serotypes, however, there remains limits to what can be inferred even using this assay.

Classification of an Individual's Vaccination Status Using Ensemble Hard Voting Classifier

Vaccination is a proactive medical immunization procedure where an inactivated form of a disease-causing agent (such as a virus) is administered to boost the body's defense systems. Efficient management of vaccination status is crucial in healthcare management, disease eradication, community immunity ("herd immunity"), disease prevention, and global health security. Ensuring precise monitoring and validation of an individual's vaccination status is indispensable, especially in the context of emerging diseases and epidemics. This study evaluates the likelihood of individuals obtaining vaccination for the H1N1 virus and the seasonal flu vaccine. Ensemble methods combine the predictions of multiple base classifiers to enhance overall performance. One such method, the hard voting classifier, aggregates the votes from each base classifier and selects the class with the majority vote as the final prediction. This approach leverages the strengths of different classifiers, reducing the risk of individual model biases and improving generalization using metrics such as precision, recall, accuracy, and F1-score are employed to assess the system's effectiveness. The results demonstrate how data-driven methods can address population wellness and improve vaccination rates using an ensemble method. The proposed ensemble hard voting classifier achieved accuracies of 0.905 and 0.907 on the H1N1 and seasonal vaccine datasets, respectively. Using an ensemble approach like the hard voting classifier enhances prediction accuracy and robustness, ultimately leading to better decision making in public health initiatives.

Assessing measles risk transmission in Iran: a utilization of the World Health Organization’s programmatic risk assessment tool,2022

BackgroundDespite successful efforts to eliminate measles in Iran, imported measles cases continue to be reported. Because measles is endemic in neighboring countries. This research aims to evaluate the risk of measles transmission in different regions of Iran.MethodsMeasles case-based surveillance data of the Expanded Program of Immunization containing 31 provinces and 463 districts from 2019 to 2021 were assessed. The WHO Measles Programmatic Risk Assessment tool was used to evaluate the risk of disease transmission in four domains: population immunity, surveillance quality, program delivery performance, and threat assessment. scores were categorized as low, medium, high, or very high risk.ResultsDuring 2019–2021, the average incidence of measles was 1.9 per 1 million. Chabahar and Mashhad with 76 and ./6per million reported the highest and lowest incidence respectively. All 463 districts were categorized as low risk in risk assessment. Andimeshk, Chabahar, and Bojnurd obtained the highest risk scores with 27, 24, and 25 respectively. All districts were classified as low risk for population immunity. The average coverage of (MMR1) and (MMR2) was 95% or higher. All districts received the minimum points for surveillance quality.ConclusionAll regions are placed at a low level of disease transmission risk. However, the tool is not able to assess the risk at the rural or peripheral sectors level. The indicators used in this tool are the same for all countries with different epidemiological features (elimination, endemic). Sensitivity analysis can optimize the use of this tool for countries with different disease conditions.

8698 Unique populations of pituitary macrophages regulate gonadotropin secretion

Abstract Disclosure: Z. Del Mundo: None. J. Ha: None. A. Zhang: None. K.D. Wiggins: None. G. De Robles: None. C. Garcia: None. N. Ujagar: None. D. Nicholas: None. Chronic inflammation disrupts hormonal balance in the Hypothalamic-Pituitary-Gonadal (HPG) axis, contributing to reproductive disorders. Despite extensive studies on immune cells in the hypothalamus and ovaries, their impact on the pituitary is largely unexplored. Our research reveals that macrophages are the primary immune population in the pituitary during homeostasis. In response to in vivo chronic lipopolysaccharide exposure in mice, a transient macrophage population emerges, associated with reproductive changes, including elevated serum testosterone, prolonged estrous cycle stage, and reduced ovarian antral follicle count. This study aims to characterize pituitary macrophages and elucidate their impact on the HPG axis. We hypothesize that pituitary macrophages change inflammatory status as a response to stimuli and, as a result, modulate gonadotropin secretion. We pinpointed differentially expressed genes (DEGs) in pituitary macrophages using scRNAseq, distinguishing them from other tissue-resident macrophages, including microglia. These DEGs include markers for macrophage activation, phagocytosis, migration, hormone synthesis, and lipid metabolism. Immunohistochemistry validated the distinctive morphology of pituitary macrophages compared to microglia. In vitro depletion of macrophages from mouse pituitaries resulted in decreased pituitary hormone secretion and decreased secretion of G-CSF, IL-9, and CXCL5. These results highlight the role of pituitary macrophages in hormonal regulation, setting the stage for future immunotherapeutic strategies to restore hormonal balance in reproductive disorders. Presentation: 6/3/2024

9218 Highly Multiplexed Mapping of the Ductal Carcinoma In Situ Ecosystem to Predict Disease Outcome

Abstract Disclosure: G.M. Wilson: None. T.B. Doan: None. B.J. Guild: None. N. Pathmanathan: None. J.D. Graham: None. Mammographic screening programs have resulted in remarkable improvements in breast cancer survival due to early-stage detection. However, this has resulted in a dramatic increase in the detection of non-invasive ductal carcinoma in situ (DCIS), a non-obligate precursor of invasive ductal carcinoma (IDC), which is confined to the ducts and surrounded by a normal myoepithelial cell layer. Many DCIS cases will not progress to IDC within the lifetime of the patient. However, there are limited tools to accurately predict progression, thus virtually all DCIS is surgically removed, and most patients receive adjuvant radiotherapy as it halves recurrence rates. This likely represents overtreatment for a majority of patients. Emerging evidence has revealed that the adjacent microenvironment becomes progressively altered in DCIS and IDC compared to normal tissue, and this is correlated with disease outcome. The objective of this study was to investigate changes to the DCIS microenvironment during disease progression and identify disease states that predict patient outcome. We used immunohistochemistry and imaging mass cytometry with a well-annotated cohort of DCIS, with or without adjacent IDC, to measure a panel of 40 phenotypic and functional markers that allowed us to distinguish the diverse cell populations present in the tissues. We observed striking changes to almost all cell types within the DCIS tissue when comparing pure DCIS cases to DCIS adjacent to IDC. In particular, there were considerable changes to tissue-resident immune populations, with differences observed in the type, location and population size of a variety of immune cell subsets, in IDC-associated DCIS compared with DCIS-only cases. Computational mapping of cell type communities in pure and IDC-adjacent DCIS tissues revealed specific differences that could be used to predict the emergence of invasive disease, informing clinical management of this good prognosis malignancy and potentially avoiding over-treatment. Presentation: 6/3/2024

9218 Highly Multiplexed Mapping Of The Ductal Carcinoma In Situ Ecosystem To Predict Disease Outcome

Abstract Disclosure: G.M. Wilson: None. T.B. Doan: None. B.J. Guild: None. N. Pathmanathan: None. J.D. Graham: None. Mammographic screening programs have resulted in remarkable improvements in breast cancer survival due to early-stage detection. However, this has resulted in a dramatic increase in the detection of non-invasive ductal carcinoma in situ (DCIS), a non-obligate precursor of invasive ductal carcinoma (IDC), which is confined to the ducts and surrounded by a normal myoepithelial cell layer. Many DCIS cases will not progress to IDC within the lifetime of the patient. However, there are limited tools to accurately predict progression, thus virtually all DCIS is surgically removed, and most patients receive adjuvant radiotherapy as it halves recurrence rates. This likely represents overtreatment for a majority of patients. Emerging evidence has revealed that the adjacent microenvironment becomes progressively altered in DCIS and IDC compared to normal tissue, and this is correlated with disease outcome. The objective of this study was to investigate changes to the DCIS microenvironment during disease progression and identify disease states that predict patient outcome. We used immunohistochemistry and imaging mass cytometry with a well-annotated cohort of DCIS, with or without adjacent IDC, to measure a panel of 40 phenotypic and functional markers that allowed us to distinguish the diverse cell populations present in the tissues. We observed striking changes to almost all cell types within the DCIS tissue when comparing pure DCIS cases to DCIS adjacent to IDC. In particular, there were considerable changes to tissue-resident immune populations, with differences observed in the type, location and population size of a variety of immune cell subsets, in IDC-associated DCIS compared with DCIS-only cases. Computational mapping of cell type communities in pure and IDC-adjacent DCIS tissues revealed specific differences that could be used to predict the emergence of invasive disease, informing clinical management of this good prognosis malignancy and potentially avoiding over-treatment. Presentation: 6/3/2024

8770 Highly Multiplexed Mapping of the Ductal Carcinoma In Situ Ecosystem to Predict Disease Outcome

Abstract Disclosure: G.M. Wilson: None. T.B. Doan: None. B.J. Guild: None. N. Pathmanathan: None. J.D. Graham: None. Mammographic screening programs have resulted in remarkable improvements in breast cancer survival due to early-stage detection. However, this has resulted in a dramatic increase in the detection of non-invasive ductal carcinoma in situ (DCIS), a non-obligate precursor of invasive ductal carcinoma (IDC), which is confined to the ducts and surrounded by a normal myoepithelial cell layer. Many DCIS cases will not progress to IDC within the lifetime of the patient. However, there are limited tools to accurately predict progression, thus virtually all DCIS is surgically removed, and most patients receive adjuvant radiotherapy as it halves recurrence rates. This likely represents overtreatment for a majority of patients. Emerging evidence has revealed that the adjacent microenvironment becomes progressively altered in DCIS and IDC compared to normal tissue, and this is correlated with disease outcome. The objective of this study was to investigate changes to the DCIS microenvironment during disease progression and identify disease states that predict patient outcome. We used immunohistochemistry and imaging mass cytometry with a well-annotated cohort of DCIS, with or without adjacent IDC, to measure a panel of 40 phenotypic and functional markers that allowed us to distinguish the diverse cell populations present in the tissues. We observed striking changes to almost all cell types within the DCIS tissue when comparing pure DCIS cases to DCIS adjacent to IDC. In particular, there were considerable changes to tissue-resident immune populations, with differences observed in the type, location and population size of a variety of immune cell subsets, in IDC-associated DCIS compared with DCIS-only cases. Computational mapping of cell type communities in pure and IDC-adjacent DCIS tissues revealed specific differences that could be used to predict the emergence of invasive disease, informing clinical management of this good prognosis malignancy and potentially avoiding over-treatment. Presentation: 6/3/2024

8770 Highly Multiplexed Mapping Of The Ductal Carcinoma In Situ Ecosystem To Predict Disease Outcome

Abstract Disclosure: G.M. Wilson: None. T.B. Doan: None. B.J. Guild: None. N. Pathmanathan: None. J.D. Graham: None. Mammographic screening programs have resulted in remarkable improvements in breast cancer survival due to early-stage detection. However, this has resulted in a dramatic increase in the detection of non-invasive ductal carcinoma in situ (DCIS), a non-obligate precursor of invasive ductal carcinoma (IDC), which is confined to the ducts and surrounded by a normal myoepithelial cell layer. Many DCIS cases will not progress to IDC within the lifetime of the patient. However, there are limited tools to accurately predict progression, thus virtually all DCIS is surgically removed, and most patients receive adjuvant radiotherapy as it halves recurrence rates. This likely represents overtreatment for a majority of patients. Emerging evidence has revealed that the adjacent microenvironment becomes progressively altered in DCIS and IDC compared to normal tissue, and this is correlated with disease outcome. The objective of this study was to investigate changes to the DCIS microenvironment during disease progression and identify disease states that predict patient outcome. We used immunohistochemistry and imaging mass cytometry with a well-annotated cohort of DCIS, with or without adjacent IDC, to measure a panel of 40 phenotypic and functional markers that allowed us to distinguish the diverse cell populations present in the tissues. We observed striking changes to almost all cell types within the DCIS tissue when comparing pure DCIS cases to DCIS adjacent to IDC. In particular, there were considerable changes to tissue-resident immune populations, with differences observed in the type, location and population size of a variety of immune cell subsets, in IDC-associated DCIS compared with DCIS-only cases. Computational mapping of cell type communities in pure and IDC-adjacent DCIS tissues revealed specific differences that could be used to predict the emergence of invasive disease, informing clinical management of this good prognosis malignancy and potentially avoiding over-treatment. Presentation: 6/3/2024

The Peculiar Emergence of Mpox (Monkeypox): Directions for the Search for the Natural Reservoir and Vaccination Strategies

Background/Objectives: Mpox (monkeypox) is a zoonosis with origins in a currently unknown African reservoir. The first epidemiological accounts of mpox date back to the early 1980s, yet mpox only emerged as a pandemic threat in 2022–2023, more than 40 years later. This scenario is very different from those of other emerging diseases such as HIV and SARS, which immediately spread globally, in fully susceptible populations, starting from patients zero. Methods: We use mathematical modeling to illustrate the dynamics of mpox herd immunity in small communities in touch with the mpox natural reservoir. In particular, we employ an SEIR stochastic model. Results: The peculiar emergence of mpox can be explained by its relationship with smallpox, which was eradicated through universal mass vaccination in 1980. Mpox first emerged in small rural communities in touch with mpox’s animal reservoir and then spread globally. The relative isolation of these communities and their herd-immunity dynamics against mpox worked to delay the introduction of mpox in large urban centers. Conclusions: Mathematical modeling suggests that the search for the mpox animal reservoir would be most fruitful in communities with high mpox seroprevalence and small outbreaks. These are communities is tight contact with the mpox natural reservoir. We propose vaccinating individuals in communities in these communities to severely reduce the importation of cases elsewhere.

Human and hamster sera correlate well in identifying antigenic drift among SARS-CoV-2 variants, including JN.1.

Antigenic assessments of SARS-CoV-2 variants inform decisions to update COVID-19 vaccines. Primary infection sera are often used for assessments, but such sera are rare due to population immunity from SARS-CoV-2 infections and COVID-19 vaccinations. Here, we show that neutralization titers and breadth of matched human and hamster pre-Omicron variant primary infection sera correlate well and generate similar antigenic maps. The hamster antigenic map shows modest antigenic drift among XBB sub-lineage variants, with JN.1 and BA.4/BA.5 variants within the XBB cluster, but with fivefold to sixfold antigenic differences between these variants and XBB.1.5. Compared to sera following only ancestral or bivalent COVID-19 vaccinations, or with post-vaccination infections, XBB.1.5 booster sera had the broadest neutralization against XBB sub-lineage variants, although a fivefold titer difference was still observed between JN.1 and XBB.1.5 variants. These findings suggest that antibody coverage of antigenically divergent JN.1 could be improved with a matched vaccine antigen.IMPORTANCEUpdates to COVID-19 vaccine antigens depend on assessing how much vaccine antigens differ antigenically from newer SARS-CoV-2 variants. Human sera from single variant infections are ideal for discriminating antigenic differences among variants, but such primary infection sera are now rare due to high population immunity. It remains unclear whether sera from experimentally infected animals could substitute for human sera for antigenic assessments. This report shows that neutralization titers of variant-matched human and hamster primary infection sera correlate well and recognize variants similarly, indicating that hamster sera can be a proxy for human sera for antigenic assessments. We further show that human sera following an XBB.1.5 booster vaccine broadly neutralized XBB sub-lineage variants but titers were fivefold lower against the more recent JN.1 variant. These findings support updating the current COVID-19 vaccine variant composition and developing a framework for assessing antigenic differences in future variants using hamster primary infection sera.

Exploring pathogen population density as a metric for understanding post-COVID infectious disease surges.

After the easing of COVID-19 restrictions, peaks of common infectious diseases surpassed pre-pandemic levels, raising questions about causes and ways to monitor these changes. A proposed measure, the Pathogen Population Density (PPD) score, could help track these shifts. PPD refers to the concentration of infectious agents within a population at a given time and location, serving as a potential indicator of infection levels in susceptible individuals at the population level. It is likely that PPD remains relatively stable within a specific community, as an equilibrium forms between infections and susceptibility. During the pandemic, nonpharmaceutical interventions (NPIs) led to a reduction in infectious diseases, possibly lowering population immunity and decreasing the PPD score. Once NPIs were lifted, the PPD score likely increased sharply due to a larger pool of susceptible individuals, causing more primary infections and stronger recurrent infections, faster transmission, and more severe pathogenic outcomes at the individual level. Monitoring the PPD score over time could help predict when infection peaks will occur. PPD is influenced by factors such as public health strategies, vaccination programs, and the behavior of high-risk individuals. As a quantitative measure, PPD has the potential to serve as a valuable predictive and monitoring tool, helping public health officials anticipate and track changes in infectious disease dynamics. It could be an effective tool for managing future outbreaks or pandemics and serve as a communication tool between scientists and the public to understand the emergence of new disease peaks.

Atypical Mumps; are We Heading Towards an Outbreak?

Mumps, caused by the mumps virus, is a contagious disease primarily affecting children and young adults. While typically presenting with salivary gland swelling and systemic symptoms, mumps can lead to various complications including SNHL, orchitis/ oophoritis, aseptic meningitis. Recent observations suggest atypical features in mumps cases, raising concerns of a potential outbreak in India. To discuss the etiopathogenesis and clinical presentation in cases of atypical mumps with increasing number of cases, a prospective multicentric study was conducted across five major centers - SMS Medical College Jaipur, RDBP Jaipuria Hospital, Jaipur, Shri Ashwini Saxena ENT Hospital Rewari, AIIMS Bhubaneswar and SP Medical College Bikaner, in India to evaluate patients with acute salivary gland swellings. Clinical and laboratory data were collected, including demographics, presenting symptoms, history of vaccination, imaging findings, and treatment outcomes. Patients were followed for four weeks post-treatment to monitor for delayed complications. Among 53 patients, a bimodal age distribution was observed, with peaks in early adolescents and middle-aged individuals. Vaccination status was recorded based on recall. Fever and salivary gland swelling were predominant symptoms, with a significant proportion experiencing submandibular gland involvement. Elevated serum amylase and CRP levels correlated with disease severity and prolonged symptomatic resolution. Notably, cases of sensorineural hearing loss (SNHL) and airway complications emerged as significant concerns. The study highlights a shift in mumps demographics, with higher age groups affected and increased incidence of complications like SNHL and airway compromise. International trends also suggest periodic outbreaks and evolving clinical manifestations post-COVID-19 pandemic. Factors contributing to mumps resurgence include lack of vaccination or vaccine efficacy, population immunity, and seasonal variations. India appears to be facing a potential mumps outbreak, characterized by atypical features and increased risk of complications like SNHL and airway compromise. Serum amylase and CRP serve as valuable markers for disease severity. Early recognition and management of complications are crucial, emphasizing the importance of mumps immunization to mitigate the impact of the disease. ENT specialists should remain vigilant for emerging complications, particularly SNHL, advocating for comprehensive immunization strategies.

Seroprevalence of antibodies against diphtheria, tetanus, and pertussis across various age groups during the post-COVID-19 pandemic period in Chonburi Province, Thailand.

BackgroundLimited data exists regarding population immunity against diphtheria, tetanus, and pertussis in Thailand during the post-COVID-19 pandemic period. This study aimed to evaluate the age-specific seroprevalence of anti-diphtheria toxoid (anti-DT) IgG, anti-tetanus toxoid (anti-TT) IgG, and anti-pertussis toxin (anti-PT) IgG in individuals across diverse age groups in Chonburi province, Thailand following the COVID-19 pandemic. MethodsBetween October 2022 and January 2023, a total of 657 participants from Chonburi Province, Thailand, were included in this study. The participants were categorized into 9 age groups: < 5, 5-10, 11-20, 21-30, 31-40, 41-50, 51-60, 61-70, and > 70 years. Analysis of anti-DT, anti-TT, and anti-PT IgG levels was conducted using commercial ELISA kits (EUROIMMUN, Lübeck, Germany). ResultsOverall, 65.4% of the population had seroprotection against diphtheria (antibody level ≥0.1 IU/mL), while 95.1% had seroprotection against tetanus (antibody level ≥0.1 IU/mL). The 31-40 years age group exhibited the lowest seroprotection for diphtheria (48.9%), and the > 70 years age group had the lowest seroprotection for tetanus (73.3%). The < 5 years age group showed the highest seropositive rate and highest geometric mean titers for anti-PT IgG. On the contrary, the majority of individuals over 11 years of age displayed anti-PT IgG levels below 40 IU/mL. ConclusionsTo prevent diphtheria and pertussis outbreaks in Chonburi province, implementing catch-up vaccination is necessary. Targeted interventions should be deployed to enhance vaccination coverage among the susceptible population.