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Previously, it was shown that intranasally (i.n.) administered Corynebacterium pseudodiphtheriticum 090104 (Cp) or CP-derived bacterium-like particles (BLPs) improve the immunogenicity of the pneumococcal conjugate vaccine (PCV). This work aimed to deepen the characterization of the adjuvant properties of Cp and CP-derived BLPs for their use in the development of pneumococcal vaccines. The ability of Cp and CP-derived BLPs to improve both the humoral and cellular specific immune responses induced by i.n. administered polysaccharide-based commercial pneumococcal vaccine (Pneumovax 23®) and the chimeric recombinant PSPF (PsaA-Spr1875-PspA-FliC) protein was evaluated, as well as the protection against Streptococcus pneumoniae infection in infant mice. Additionally, whether the immunization protocols, including Cp and CP-derived BLPs, together with the pneumococcal vaccines can enhance the resistance to secondary pneumococcal pneumonia induced after inflammatory lung damage mediated by the activation of Toll-like receptor 3 (TLR3) was assessed. The results showed that both Cp and CP-derived BLPs increased the immunogenicity and protection induced by two pneumococcal vaccines administered through the nasal route. Of note, the nasal priming with the PSPF T-dependent antigen co-administered with Cp or CP-derived BLPs efficiently stimulated humoral and cellular immunity and increased the resistance to primary and secondary pneumococcal infections. The CP-derived BLPs presented a stronger effect than live bacteria. Given safety concerns associated with live bacterium administration, especially in high-risk populations, such as infants, the elderly, and immunocompromised patients, BLPs emerge as an attractive mucosal adjuvant to improve the host response to pneumococcal infections and to enhance the vaccines already in the market or in development.

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BACKGROUND: The rate of pulmonary tuberculosis (TB) recurrence is substantial. Identifying risk factors can support the development of prevention strategies. METHODS: We retrieved studies published between 1 January 1980 and 31 December 2022 that assessed factors associated with undifferentiated TB recurrence, relapse or reinfection. For factors reported in at least four studies, we performed random-effects meta-analysis to estimate a pooled relative risk (RR). We assessed heterogeneity, risk of publication bias and certainty of evidence. RESULTS: We included 85 studies in the review; 81 documented risk factors for undifferentiated recurrence, 17 for relapse and 10 for reinfection. The scope for meta-analyses was limited given the wide variety of factors studied, inconsistency in control for confounding and the fact that only few studies employed molecular genotyping. Factors that significantly contributed to moderately or strongly increased pooled risk and scored at least moderate certainty of evidence were: for undifferentiated recurrence, multidrug resistance (MDR) (RR 3.49; 95% CI 1.86 to 6.53) and fixed-dose combination TB drugs (RR 2.29; 95% CI 1.10 to 4.75) in the previous episode; for relapse, none; and for reinfection, HIV infection (RR 4.65; 95% CI 1.71 to 12.65). Low adherence to treatment increased the pooled risk of recurrence 3.3-fold (95% CI 2.37 to 4.62), but the certainty of evidence was weak. CONCLUSION: This review emphasises the need for standardising methods for TB recurrence research. Actively pursuing MDR prevention, facilitating retention in treatment and providing integrated care for patients with HIV could curb recurrence rates. The use of fixed-dose combinations of TB drugs under field conditions merits further attention. PROSPERO REGISTRATION NUMBER: CRD42018077867.

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BACKGROUND: Respiratory diseases (RDs) cause millions of hospitalisations and deaths worldwide, resulting in economic and social impacts. Strategies for health promotion and disease prevention based on the epidemiological profile of the population may reduce hospital costs. AIM: To characterise hospitalisations and deaths due to RDs in Brazilian adults above 20 years old between 2008 and 2021. METHODS: This ecological study used secondary data of hospitalisations and deaths due to RDs from the Hospital Information System of the Brazilian Unified Health System between 2008 and 2021. Data were grouped according to region, age group and sex. The period was divided into first (2008-2011), second (2012-2015) and third (2016-2019) quadrennia and one biennium (2020-2021), and all data were analysed using the GraphPad Prism; statistical significance was set at p<0.05. RESULTS: A total of 9 502 378 hospitalisations due to RDs were registered between 2008 and 2021. The south and southeast regions presented the highest hospitalisation and fatality rate, respectively, in the age group ≥80 years with no significant differences between sexes. Also, RDs caused 1 170 504 deaths, with a national fatality rate of 12.32%. CONCLUSION: RDs affected the Brazilian population and impaired the health system, especially the hospital environment. The south/southeast regions were the most affected, and the ageing process contributed to the increased incidence of RDs.

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Abstract: Severe acute respiratory infection (SARI) outbreaks occur annually, with seasonal peaks varying among geographic regions. Case notification is important to prepare healthcare networks for patient attendance and hospitalization. Thus, health managers need adequate resource planning tools for SARI seasons. This study aims to predict SARI outbreaks based on models generated with machine learning using SARI hospitalization notification data. In this study, data from the reporting of SARI hospitalization cases in Brazil from 2013 to 2020 were used, excluding SARI cases caused by COVID-19. These data were prepared to feed a neural network configured to generate predictive models for time series. The neural network was implemented with a pipeline tool. Models were generated for the five Brazilian regions and validated for different years of SARI outbreaks. By using neural networks, it was possible to generate predictive models for SARI peaks, volume of cases per season, and for the beginning of the pre-epidemic period, with good weekly incidence correlation (R2 = 0.97; 95%CI: 0.95-0.98, for the 2019 season in the Southeastern Brazil). The predictive models achieved a good prediction of the volume of reported cases of SARI; accordingly, 9,936 cases were observed in 2019 in Southern Brazil, and the prediction made by the models showed a median of 9,405 (95%CI: 9,105-9,738). The identification of the period of occurrence of a SARI outbreak is possible using predictive models generated with neural networks and algorithms that employ time series.

Resumo: Surtos de síndrome respiratória aguda grave (SRAG) ocorrem anualmente, com picos sazonais variando entre regiões geográficas. A notificação dos casos é importante para preparar as redes de atenção à saúde para o atendimento e internação dos pacientes. Portanto, os gestores de saúde precisam ter ferramentas adequadas de planejamento de recursos para as temporadas de SRAG. Este estudo tem como objetivo prever surtos de SRAG com base em modelos gerados com aprendizado de máquina usando dados de internação por SRAG. Foram incluídos dados sobre casos de hospitalização por SRAG no Brasil de 2013 a 2020, excluindo os casos causados pela COVID-19. Estes dados foram preparados para alimentar uma rede neural configurada para gerar modelos preditivos para séries temporais. A rede neural foi implementada com uma ferramenta de pipeline. Os modelos foram gerados para as cinco regiões brasileiras e validados para diferentes anos de surtos de SRAG. Com o uso de redes neurais, foi possível gerar modelos preditivos para picos de SRAG, volume de casos por temporada e para o início do período pré-epidêmico, com boa correlação de incidência semanal (R2 = 0,97; IC95%: 0,95-0,98, para a temporada de 2019 na Região Sudeste). Os modelos preditivos obtiveram uma boa previsão do volume de casos notificados de SRAG; dessa forma, foram observados 9.936 casos em 2019 na Região Sul, e a previsão feita pelos modelos mostrou uma mediana de 9.405 (IC95%: 9.105-9.738). A identificação do período de ocorrência de um surto de SRAG é possível por meio de modelos preditivos gerados com o uso de redes neurais e algoritmos que aplicam séries temporais.

Resumen: Brotes de síndrome respiratorio agudo grave (SRAG) ocurren todos los años, con picos estacionales que varían entre regiones geográficas. La notificación de los casos es importante para preparar las redes de atención a la salud para el cuidado y hospitalización de los pacientes. Por lo tanto, los gestores de salud deben tener herramientas adecuadas de planificación de recursos para las temporadas de SRAG. Este estudio tiene el objetivo de predecir brotes de SRAG con base en modelos generados con aprendizaje automático utilizando datos de hospitalización por SRAG. Se incluyeron datos sobre casos de hospitalización por SRAG en Brasil desde 2013 hasta 2020, salvo los casos causados por la COVID-19. Se prepararon estos datos para alimentar una red neural configurada para generar modelos predictivos para series temporales. Se implementó la red neural con una herramienta de canalización. Se generaron los modelos para las cinco regiones brasileñas y se validaron para diferentes años de brotes de SRAG. Con el uso de redes neurales, se pudo generar modelos predictivos para los picos de SRAG, el volumen de casos por temporada y para el inicio del periodo pre-epidémico, con una buena correlación de incidencia semanal (R2 = 0,97; IC95%: 0,95-0,98, para la temporada de 2019 en la Región Sudeste). Los modelos predictivos tuvieron una buena predicción del volumen de casos notificados de SRAG; así, se observaron 9.936 casos en 2019 en la Región Sur, y la predicción de los modelos mostró una mediana de 9.405 (IC95%: 9.105-9.738). La identificación del periodo de ocurrencia de un brote de SRAG es posible a través de modelos predictivos generados con el uso de redes neurales y algoritmos que aplican series temporales.

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The COVID-19 pandemic caused by the SARS-CoV-2 (ß-CoV) betacoronavirus has posed a significant threat to global health. Despite the availability of vaccines, the virus continues to spread, and there is a need for alternative strategies to alleviate its impact. Vitamin D, a secosteroid hormone best known for its role in bone health, exhibits immunomodulatory effects in certain viral infections. Here, we have shown that bioactive vitamin D (calcitriol) limits in vitro replication of SARS-CoV-2 and murine coronaviruses MHV-3 and MHV-A59. Comparative studies involving wild-type mice intranasally infected with MHV-3, a model for studying ß-CoV respiratory infections, confirmed the protective effect of vitamin D in vivo. Accordingly, mice fed a standard diet rapidly succumbed to MHV-3 infection, whereas those on a vitamin D-rich diet (10,000 IU of Vitamin D3/kg) displayed increased resistance to acute respiratory damage and systemic complications. Consistent with these findings, the vitamin D-supplemented group exhibited lower viral titers in their lungs and reduced levels of TNF, IL-6, IL-1ß, and IFN-γ, alongside an enhanced type I interferon response. Altogether, our findings suggest vitamin D supplementation ameliorates ß-CoV-triggered respiratory illness and systemic complications in mice, likely via modulation of the host's immune response to the virus.

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Parechovirus A (PeV-A, Parechovirus, Picornaviridae) are human pathogens associated with mild to severe gastrointestinal and respiratory diseases in young children. While several studies have investigated the association of PeV-A with human disease, little is known about its epidemiology or detection in Latin America. Between the years 2014 and 2015, a total of 200 samples were collected from Panamanian pediatric patients aged < 16 years old exhibiting symptoms associated with respiratory (n = 64), gastrointestinal (n = 68), or neurological (n = 68) diseases. These samples were gathered from patients who had previously received negative diagnoses for the main respiratory viruses, rotavirus, and neurological viruses like herpes virus, enterovirus, and cytomegalovirus. The presence of PeV-A was analyzed by real time RT-PCR.Eight positive PeV-A infections (4.0%, 95% CI: 1.7 to 7.7) were detected: two in respiratory samples (3.0%, 95% CI: 0.3 to 10.8), five in gastrointestinal samples (7.3%, 95% CI: 2.4 to 16.3), and one in cerebrospinal fluid (1.5%, 95% CI: 1.4 to 7.9). The study provides evidence of PeV-A circulation in Panama and the data collectively, remarked on the importance of considering PeV-A in the Panamanian pediatric diagnostic landscape, especially when conventional testing for more common viruses yields negative results.

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OBJECTIVES: To investigate the association between socioeconomic and nutritional factors with respiratory morbidity in the first year of life in different regions of Brazil. METHODOLOGY: A nested case-control study within a randomized field trial was conducted in three capital cities (Porto Alegre, Manaus, and Salvador), representing different macro-regions of the country. Cases were defined as children with a reported previous diagnosis of asthma, bronchiolitis, or pneumonia. Corresponding controls were matched by age and sex in a 2:1 ratio, selected consecutively from the original cohort, resulting in a sample of 222 children. Bivariate analyses were performed to assess the association between sociodemographic and nutritional variables with respiratory morbidity outcomes, calculating odds ratios (OR) and their respective confidence intervals (95% CI). Values of p < 0.05 were considered significant. Potential confounding factors were adjusted through multivariate analysis (logistic regression). RESULTS: Maternal smoking and breastfeeding for less than six months showed a significant association and increased risk of respiratory disease (OR=2.12 and 2.05, respectively). Children born in the Southern region of Brazil also demonstrated a higher association and risk of respiratory morbidity. The consumption of ultra-processed foods did not show a significant association or increased risk of respiratory disease. CONCLUSIONS: Maternal smoking, breastfeeding for less than six months, and being born in the Southern region of Brazil are risk factors for the development of respiratory morbidity in the first year of life. The consumption of ultra-processed foods does not appear to pose a risk, but it was prevalent in more than 80% of the population, limiting its discriminatory power of analysis.

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Despite the importance of the respiratory route for Brucella transmission, the lung immune response to this pathogen is scarcely characterized. We investigated the role of the cGAS/STING pathway of microbial DNA recognition in the control of respiratory Brucella infection. After in vitro B. abortus infection, CFU numbers were significantly higher in alveolar macrophages (AM) and lung explants from STING KO mice than in samples from wild type (WT) mice, but no difference was observed for cGAS KO samples. CFU were also increased in WT AM and lung epithelial cells preincubated with the STING inhibitor H151. Several proinflammatory cytokines (TNF-α, IL-1ß, IL-6, IP-10/CXCL10) were diminished in Brucella-infected lung explants and/or AM from STING KO mice and cGAS KO mice. These cytokines were also reduced in infected AM and lung epithelial cells pretreated with H151. After intratracheal infection with B. abortus, STING KO mice exhibited increased CFU in lungs, spleen and liver, a reduced expression of IFN-ß mRNA in lungs and spleen, and reduced levels of proinflammatory cytokines and chemokines in bronchoalveolar lavage fluid (BALF) and lung homogenates. Increased lung CFU and reduced BALF cytokines were also observed in cGAS KO mice. In summary, the cGAS/STING pathway induces the production of proinflammatory cytokines after respiratory Brucella infection, which may contribute to the STING-dependent control of airborne brucellosis.

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OBJECTIVE: To compare demographic characteristics, clinical features, and outcomes of children hospitalized with respiratory syncytial virus (RSV), influenza, or severe acute respiratory syndrome coronavirus 2 during their cocirculation 2021-2022 respiratory virus season. METHODS: We conducted a retrospective cohort study using Colorado's hospital respiratory surveillance data comparing coronavirus disease 2019 (COVID-19)-, influenza-, and RSV-hospitalized cases < 18 years of age admitted and undergoing standardized molecular testing between October 1, 2021, and April 30, 2022. Multivariable log-binomial regression modeling evaluated associations between pathogen type and diagnosis, intensive care unit admission, hospital length of stay, and highest level of respiratory support received. RESULTS: Among 847 hospitalized cases, 490 (57.9%) were RSV associated, 306 (36.1%) were COVID-19 associated, and 51 (6%) were influenza associated. Most RSV cases were <4 years of age (92.9%), whereas influenza hospitalizations were observed in older children. RSV cases were more likely to require oxygen support higher than nasal cannula compared with COVID-19 and influenza cases (P < .0001), although COVID-19 cases were more likely to require invasive mechanical ventilation than influenza and RSV cases (P < .0001). Using multivariable log-binomial regression analyses, compared with children with COVID-19, the risk of intensive care unit admission was highest among children with influenza (relative risk, 1.97; 95% CI, 1.22-3.19), whereas the risk of pneumonia, bronchiolitis, longer hospital length of stay, and need for oxygen were more likely among children with RSV. CONCLUSIONS: In a season with respiratory pathogen cocirculation, children were hospitalized most commonly for RSV, were younger, and required higher oxygen support and non-invasive ventilation compared with children with influenza and COVID-19.

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Influenza A virus (IAV) circulation patterns differ in North America and South America, with influenza seasons often characterized by different subtypes and strains. However, South America is relatively undersampled considering the size of its population. To address this gap, we sequenced the complete genomes of 220 IAVs collected between 2009 and 2016 from hospitalized patients in southern Brazil. New genetic drift variants were introduced into southern Brazil each season from a global gene pool, including four H3N2 clades (3c, 3c2, 3c3, and 3c2a) and five H1N1pdm clades (clades 6, 7, 6b, 6c, and 6b1). In 2016, H1N1pdm viruses belonging to a new 6b1 clade caused a severe influenza epidemic in southern Brazil that arrived early and spread rapidly, peaking mid-autumn. Inhibition assays showed that the A/California/07/2009(H1N1) vaccine strain did not protect well against 6b1 viruses. Phylogenetically, most 6b1 sequences that circulated in southern Brazil belong to a single transmission cluster that rapidly diffused across susceptible populations, leading to the highest levels of influenza hospitalization and mortality seen since the 2009 pandemic. Continuous genomic surveillance is needed to monitor rapidly evolving IAVs for vaccine strain selection and understand their epidemiological impact in understudied regions.