-
Pathology, Research and Practice Jun 2024Usual Interstitial Pneumonia (UIP) a fibrosing pneumonia is associated with idiopathic pulmonary fibrosis, chronic autoimmune disease (AID), or hypersensitivity...
BACKGROUND
Usual Interstitial Pneumonia (UIP) a fibrosing pneumonia is associated with idiopathic pulmonary fibrosis, chronic autoimmune disease (AID), or hypersensitivity pneumonia. Oxygen radicals, due to tobacco smoke, can damage DNA and might upregulate PARP1. Cytosolic DNA from dying pneumocytes activate cytosolic GMP-AMP-synthase-stimulator of interferon genes (cGAS-STING) pathway and TREX1. Prolonged inflammation induces senescence, which might be inhibited by phagocytosis, eliminating nuclear debris. We aimed to evaluate activation of cGAS-STING-TREX1 pathway in UIP, and if phagocytosis and anti-phagocytosis might counteract inflammation.
METHODS
44 cases of UIP with IPF or AID were studied for the expression of cGAS, pSTING, TREX1 and PARP1. LAMP1 and Rab7 expression served as phagocytosis markers. CD47 protecting phagocytosis and p16 to identify senescent cells were also studied.
RESULTS
Epithelial cells in remodeled areas and macrophages expressed cGAS-pSTING, TREX1; epithelia but not macrophages stained for PARP1. Myofibroblasts, endothelia, and bronchial/bronchiolar epithelial cells were all negative except early myofibroblastic foci expressing cGAS. Type II pneumocytes expressed cGAS and PARP1, but less pSTING. TREX1 although expressed was not activated. Macrophages and many regenerating epithelial cells expressed LAMP1 and Rab7. CD47, the 'don't-eat-me-signal', was expressed by macrophages and epithelial cells including senescence cells within the remodeled areas.
CONCLUSIONS
The cGAS-STING pathway is activated in macrophages and epithelial cells within remodeled areas. LikelyTREX1 because not activated cannot sufficiently degrade DNA fragments. PARP1 activation points to smoking-induced oxygen radical release, prolonging inflammation and leading to fibrosis. By expressing CD47 epithelial cells within remodeled areas protect themselves from being eliminated by phagocytosis.
PubMed: 38944022
DOI: 10.1016/j.prp.2024.155432 -
JAMA Health Forum Jun 2024States resumed Medicaid eligibility redeterminations, which had been paused during the COVID-19 public health emergency, in 2023. This unwinding of the pandemic...
IMPORTANCE
States resumed Medicaid eligibility redeterminations, which had been paused during the COVID-19 public health emergency, in 2023. This unwinding of the pandemic continuous coverage provision raised concerns about the extent to which beneficiaries would lose Medicaid coverage and how that would affect access to care.
OBJECTIVE
To assess early changes in insurance and access to care during Medicaid unwinding among individuals with low incomes in 4 Southern states.
DESIGN, SETTING, AND PARTICIPANTS
This multimodal survey was conducted in Arkansas, Kentucky, Louisiana, and Texas from September to November 2023, used random-digit dialing and probabilistic address-based sampling, and included US citizens aged 19 to 64 years reporting 2022 incomes at or less than 138% of the federal poverty level.
EXPOSURE
Medicaid enrollment at any point since March 2020, when continuous coverage began.
MAIN OUTCOMES AND MEASURES
Self-reported disenrollment from Medicaid, insurance at the time of interview, and self-reported access to care. Using multivariate logistic regression, factors associated with Medicaid loss were evaluated. Access and affordability of care among respondents who exited Medicaid vs those who remained enrolled were compared, after multivariate adjustment.
RESULTS
The sample contained 2210 adults (1282 women [58.0%]; 505 Black non-Hispanic individuals [22.9%], 393 Hispanic individuals [17.8%], and 1133 White non-Hispanic individuals [51.3%]) with 2022 household incomes less than 138% of the federal poverty line. On a survey-weighted basis, 1564 (70.8%) reported that they and/or a dependent child of theirs had Medicaid at some point since March 2020. Among adult respondents who had Medicaid, 179 (12.5%) were no longer enrolled in Medicaid at the time of the survey, with state estimates ranging from 7.0% (n = 19) in Kentucky to 16.2% (n = 82) in Arkansas. Fewer children who had Medicaid lost coverage (42 [5.4%]). Among adult respondents who left Medicaid since 2020 and reported coverage status at time of interview, 47.8% (n = 80) were uninsured, 27.0% (n = 45) had employer-sponsored insurance, and the remainder had other coverage as of fall 2023. Disenrollment was higher among younger adults, employed individuals, and rural residents but lower among non-Hispanic Black respondents (compared with non-Hispanic White respondents) and among those receiving Supplemental Nutrition Assistance Program benefits. Losing Medicaid was significantly associated with delaying care due to cost and worsening affordability of care.
CONCLUSIONS AND RELEVANCE
The results of this survey study indicated that 6 months into unwinding, 1 in 8 Medicaid beneficiaries reported exiting the program, with wide state variation. Roughly half who lost Medicaid coverage became uninsured. Among those moving to new coverage, many experienced coverage gaps. Adults exiting Medicaid reported more challenges accessing care than respondents who remained enrolled.
Topics: Humans; Medicaid; United States; Health Services Accessibility; Adult; Female; Male; Insurance Coverage; Middle Aged; COVID-19; Poverty; Young Adult; Arkansas
PubMed: 38943683
DOI: 10.1001/jamahealthforum.2024.2193 -
Critical Care (London, England) Jun 2024In a phase 3 trial (PANAMO, NCT04333420), vilobelimab, a complement 5a (C5a) inhibitor, reduced 28-day mortality in mechanically ventilated COVID-19 patients. This post... (Randomized Controlled Trial)
Randomized Controlled Trial
In a phase 3 trial (PANAMO, NCT04333420), vilobelimab, a complement 5a (C5a) inhibitor, reduced 28-day mortality in mechanically ventilated COVID-19 patients. This post hoc analysis of 368 patients aimed to explore treatment heterogeneity through unsupervised learning. All available clinical variables at baseline were used as input. Treatment heterogeneity was assessed using latent class analysis (LCA), Ward's hierarchical clustering (HC) and the adjudication to previously described clinical sepsis phenotypes. The primary outcome was 28-day mortality. For LCA, a 2-class latent model was deemed most suitable. In the LCA model, 82 (22%) patients were assigned to class 1 and 286 (78%) to class 2. Class 1 was defined by more severely ill patients with significantly higher mortality. In an adjusted logistic regression, no heterogeneity of treatment effect (HTE) between classes was observed (p = 0.998). For HC, no significant classes were found (p = 0.669). Using the previously described clinical sepsis subtypes, 41 patients (11%) were adjudicated subtype alpha (α), 17 (5%) beta (β), 112 (30%) delta (δ) and 198 (54%) gamma (γ). HTE was observed between clinical subtypes (p = 0.001) with improved 28-day mortality after treatment with vilobelimab for the δ subtype (OR = 0.17, 95% CI 0.07-0.40, p < 0.001). No signal for harm of treatment with vilobelimab was observed in any class or clinical subtype. Overall, treatment effect with vilobelimab was consistent across different classes and subtypes, except for the δ subtype, suggesting potential additional benefit for the most severely ill patients.
Topics: Humans; Female; Male; Middle Aged; Aged; COVID-19 Drug Treatment; Antibodies, Monoclonal, Humanized; Treatment Outcome; COVID-19
PubMed: 38943192
DOI: 10.1186/s13054-024-05004-z -
Particle and Fibre Toxicology Jun 2024Today, nanomaterials are broadly used in a wide range of industrial applications. Such large utilization and the limited knowledge on to the possible health effects have...
BACKGROUND
Today, nanomaterials are broadly used in a wide range of industrial applications. Such large utilization and the limited knowledge on to the possible health effects have raised concerns about potential consequences on human health and safety, beyond the environmental burden. Given that inhalation is the main exposure route, workers exposed to nanomaterials might be at risk of occurrence of respiratory morbidity and/or reduced pulmonary function. However, epidemiological evidence regarding the association between cumulative exposure to nanomaterials and respiratory health is still scarce. This study focused on the association between cumulative exposure to nanomaterials and pulmonary function among 136 workers enrolled in the framework of the European multicentric NanoExplore project.
RESULTS
Our findings suggest that, independently of lifelong tobacco smoking, ethnicity, age, sex, body mass index and physical activity habits, 10-year cumulative exposure to nanomaterials is associated to worse FEV and FEF, which might be consistent with the involvement of both large and small airway components and early signs of airflow obstruction. We further explored the hypothesis of a mediating effect via airway inflammation, assessed by interleukin (IL-)10, IL-1β and Tumor Necrosis Factor alpha (TNF-α), all quantified in the Exhaled Breath Condensate of workers. The mediation analysis results suggest that IL-10, TNF-α and their ratio (i.e., anti-pro inflammatory ratio) may fully mediate the negative association between cumulative exposure to nanomaterials and the FEV/FVC ratio. This pattern was not observed for other pulmonary function parameters.
CONCLUSIONS
Safeguarding the respiratory health of workers exposed to nanomaterials should be of primary importance. The observed association between cumulative exposure to nanomaterials and worse pulmonary function parameters underscores the importance of implementing adequate protective measures in the nanocomposite sector. The mitigation of harmful exposures may ensure that workers can continue to contribute productively to their workplaces while preserving their respiratory health over time.
Topics: Humans; Male; Nanostructures; Female; Occupational Exposure; Adult; Inhalation Exposure; Middle Aged; Lung; Pneumonia; Forced Expiratory Volume; Respiratory Function Tests; Cytokines; Air Pollutants, Occupational; Europe
PubMed: 38943182
DOI: 10.1186/s12989-024-00589-3 -
BMC Public Health Jun 2024There are limited population-representative data that describe the potential burden of Post-COVID conditions (PCC) in Mexico. We estimated the prevalence of PCC overall...
BACKGROUND
There are limited population-representative data that describe the potential burden of Post-COVID conditions (PCC) in Mexico. We estimated the prevalence of PCC overall and by sociodemographic characteristics among a representative sample of adults previously diagnosed with COVID-19 in Mexico. We additionally, characterized the PCC symptoms, and estimated the association between diagnosed type-2 diabetes and hypertension with PCC.
METHODS
We used data from the 2021 National Health and Nutrition Survey in Mexico, a nationally and regionally representative survey, from August 1st to October 31st, 2021. Using the WHO definition, we estimated the prevalence of PCC by sociodemographics and prevalence of PCC symptoms. We fit multivariable log-binomial regression models to estimate the associations.
RESULTS
The prevalence of PCC was 37.0%. The most common persistent symptoms were fatigue (56.8%), myalgia or arthralgia (47.5%), respiratory distress and dyspnea (42.7%), headache (34.0%), and cough (25.7%). The prevalence was higher in older people, women, and individuals with low socioeconomic status. There was no significant association between hypertension and PCC or diabetes and PCC prevalence.
CONCLUSIONS
About one-third of the adult Mexican population who had COVID-19 in 2021 had Post-COVID conditions. Our population-based estimates can help assess potential priorities for PCC-related health services, which is critical in light of our weak health system and limited funding.
Topics: Humans; COVID-19; Mexico; Male; Female; Cross-Sectional Studies; Middle Aged; Adult; Prevalence; Aged; Survivors; Young Adult; Hypertension; Adolescent; Diabetes Mellitus, Type 2; Sociodemographic Factors; SARS-CoV-2
PubMed: 38943168
DOI: 10.1186/s12889-024-19274-3 -
BMC Public Health Jun 2024In Haiti, reported incidence and mortality rates for COVID-19 were lower than expected. We aimed to analyze factors at communal and individual level that might lead to...
BACKGROUND
In Haiti, reported incidence and mortality rates for COVID-19 were lower than expected. We aimed to analyze factors at communal and individual level that might lead to an underestimation of the true burden of the COVID-19 epidemic in Haiti during its first two years.
METHODS
We analyzed national COVID-19 surveillance data from March 2020 to December 2021, to describe the epidemic using cluster detection, time series, and cartographic approach. We performed multivariate Quasi-Poisson regression models to determine socioeconomic factors associated with incidence and mortality. We performed a mixed-effect logistic regression model to determine individual factors associated with the infection.
RESULTS
Among the 140 communes of Haiti, 57 (40.7%) had a COVID-19 screening center, and the incidence was six times higher in these than in those without. Only 22 (15.7%) communes had a COVID-19 care center, and the mortality was five times higher in these than in those without. All the richest communes had a COVID-19 screening center while only 30.8% of the poorest had one. And 75% of the richest communes had a COVID-19 care center while only 15.4% of the poorest had one. Having more than three healthcare workers per 1000 population in the commune was positively associated with the incidence (SIR: 3.31; IC95%: 2.50, 3.93) and the mortality (SMR: 2.73; IC95%: 2.03, 3.66). At the individual level, male gender (adjusted OR: 1.11; IC95%: 1.01, 1.22), age with a progressive increase of the risk compared to youngers, and having Haitian nationality only (adjusted OR:2.07; IC95%: 1.53, 2.82) were associated with the infection.
CONCLUSIONS
This study highlights the weakness of SARS-CoV-2 screening and care system in Haiti, particularly in the poorest communes, suggesting that the number of COVID-19 cases and deaths were probably greatly underestimated.
Topics: Humans; Haiti; COVID-19; Male; Female; Adult; Middle Aged; Incidence; Mass Screening; Young Adult; SARS-CoV-2; Adolescent; Aged; Socioeconomic Factors; COVID-19 Testing
PubMed: 38943127
DOI: 10.1186/s12889-024-19262-7 -
BMC Public Health Jun 2024Public health events (PHEs) have emerged as significant threats to human life, health, and economic growth. PHEs, such as COVID-19, have prompted a reevaluation for...
Public health events (PHEs) have emerged as significant threats to human life, health, and economic growth. PHEs, such as COVID-19, have prompted a reevaluation for enhanced regular prevention and control (RPC). In this study, we focus on the core concept of prevention and control intensity (PCI), and establish a neoclassical economic growth model from the long-term and macro perspective to balance life protection and economic growth. The model construct the mechanism of PCI on economic growth through population dynamics and capital accumulation under the backdrop of RPC for PHEs. We find the conditions for PCI when the economy achieves steady state, and provides an algorithm establishing the optimal strategy that maximises per capita disposable income based on the optimal PCI and consumption. Simulation result quantifies an inverted U-shaped relationship between PCI and capital per capita, output per capita and consumption per capita in the steady state. The model suggests that, given the PHEs of inducing potential unemployment shock, it is worthwhile to combine the implementation of moderate PCI with coordinated policies of income distribution.
Topics: Humans; Economic Development; Public Health; COVID-19; Models, Economic
PubMed: 38943103
DOI: 10.1186/s12889-024-19106-4 -
BMC Public Health Jun 2024Coronavirus disease 2019 (COVID-19), a global public health crisis, continues to pose challenges despite preventive measures. The daily rise in COVID-19 cases is...
BACKGROUND
Coronavirus disease 2019 (COVID-19), a global public health crisis, continues to pose challenges despite preventive measures. The daily rise in COVID-19 cases is concerning, and the testing process is both time-consuming and costly. While several models have been created to predict mortality in COVID-19 patients, only a few have shown sufficient accuracy. Machine learning algorithms offer a promising approach to data-driven prediction of clinical outcomes, surpassing traditional statistical modeling. Leveraging machine learning (ML) algorithms could potentially provide a solution for predicting mortality in hospitalized COVID-19 patients in Ethiopia. Therefore, the aim of this study is to develop and validate machine-learning models for accurately predicting mortality in COVID-19 hospitalized patients in Ethiopia.
METHODS
Our study involved analyzing electronic medical records of COVID-19 patients who were admitted to public hospitals in Ethiopia. Specifically, we developed seven different machine learning models to predict COVID-19 patient mortality. These models included J48 decision tree, random forest (RF), k-nearest neighborhood (k-NN), multi-layer perceptron (MLP), Naïve Bayes (NB), eXtreme gradient boosting (XGBoost), and logistic regression (LR). We then compared the performance of these models using data from a cohort of 696 patients through statistical analysis. To evaluate the effectiveness of the models, we utilized metrics derived from the confusion matrix such as sensitivity, specificity, precision, and receiver operating characteristic (ROC).
RESULTS
The study included a total of 696 patients, with a higher number of females (440 patients, accounting for 63.2%) compared to males. The median age of the participants was 35.0 years old, with an interquartile range of 18-79. After conducting different feature selection procedures, 23 features were examined, and identified as predictors of mortality, and it was determined that gender, Intensive care unit (ICU) admission, and alcohol drinking/addiction were the top three predictors of COVID-19 mortality. On the other hand, loss of smell, loss of taste, and hypertension were identified as the three lowest predictors of COVID-19 mortality. The experimental results revealed that the k-nearest neighbor (k-NN) algorithm outperformed than other machine learning algorithms, achieving an accuracy of 95.25%, sensitivity of 95.30%, precision of 92.7%, specificity of 93.30%, F1 score 93.98% and a receiver operating characteristic (ROC) score of 96.90%. These findings highlight the effectiveness of the k-NN algorithm in predicting COVID-19 outcomes based on the selected features.
CONCLUSION
Our study has developed an innovative model that utilizes hospital data to accurately predict the mortality risk of COVID-19 patients. The main objective of this model is to prioritize early treatment for high-risk patients and optimize strained healthcare systems during the ongoing pandemic. By integrating machine learning with comprehensive hospital databases, our model effectively classifies patients' mortality risk, enabling targeted medical interventions and improved resource management. Among the various methods tested, the K-nearest neighbors (KNN) algorithm demonstrated the highest accuracy, allowing for early identification of high-risk patients. Through KNN feature identification, we identified 23 predictors that significantly contribute to predicting COVID-19 mortality. The top five predictors are gender (female), intensive care unit (ICU) admission, alcohol drinking, smoking, and symptoms of headache and chills. This advancement holds great promise in enhancing healthcare outcomes and decision-making during the pandemic. By providing services and prioritizing patients based on the identified predictors, healthcare facilities and providers can improve the chances of survival for individuals. This model provides valuable insights that can guide healthcare professionals in allocating resources and delivering appropriate care to those at highest risk.
Topics: Humans; COVID-19; Machine Learning; Ethiopia; Male; Female; Middle Aged; Adult; Algorithms; Aged; SARS-CoV-2; Hospitalization; Electronic Health Records; Young Adult; Adolescent
PubMed: 38943093
DOI: 10.1186/s12889-024-19196-0 -
BMC Genomics Jun 2024At a global scale, the SARS-CoV-2 virus did not remain in its initial genotype for a long period of time, with the first global reports of variants of concern (VOCs) in...
BACKGROUND
At a global scale, the SARS-CoV-2 virus did not remain in its initial genotype for a long period of time, with the first global reports of variants of concern (VOCs) in late 2020. Subsequently, genome sequencing has become an indispensable tool for characterizing the ongoing pandemic, particularly for typing SARS-CoV-2 samples obtained from patients or environmental surveillance. For such SARS-CoV-2 typing, various in vitro and in silico workflows exist, yet to date, no systematic cross-platform validation has been reported.
RESULTS
In this work, we present the first comprehensive cross-platform evaluation and validation of in silico SARS-CoV-2 typing workflows. The evaluation relies on a dataset of 54 patient-derived samples sequenced with several different in vitro approaches on all relevant state-of-the-art sequencing platforms. Moreover, we present UnCoVar, a robust, production-grade reproducible SARS-CoV-2 typing workflow that outperforms all other tested approaches in terms of precision and recall.
CONCLUSIONS
In many ways, the SARS-CoV-2 pandemic has accelerated the development of techniques and analytical approaches. We believe that this can serve as a blueprint for dealing with future pandemics. Accordingly, UnCoVar is easily generalizable towards other viral pathogens and future pandemics. The fully automated workflow assembles virus genomes from patient samples, identifies existing lineages, and provides high-resolution insights into individual mutations. UnCoVar includes extensive quality control and automatically generates interactive visual reports. UnCoVar is implemented as a Snakemake workflow. The open-source code is available under a BSD 2-clause license at github.com/IKIM-Essen/uncovar.
Topics: SARS-CoV-2; Workflow; Humans; COVID-19; Genome, Viral; Software; Reproducibility of Results
PubMed: 38943066
DOI: 10.1186/s12864-024-10539-0 -
BMC Immunology Jun 2024Variations in the innate and adaptive immune response systems are linked to variations in the severity of COVID-19. Natural killer cell (NK) function is regulated by...
BACKGROUND
Variations in the innate and adaptive immune response systems are linked to variations in the severity of COVID-19. Natural killer cell (NK) function is regulated by sophisticated receptor system including Killer-cell immunoglobulin-like receptor (KIR) family. We aimed to investigate the impact of possessing certain KIR genes and genotypes on COVID19 severity in Iranians. KIR genotyping was performed on 394 age/sex matched Iranians with no underlying conditions who developed mild and severe COVID- 19. The presence and/or absence of 11 KIR genes were determined using the PCR with sequence specific primers (PCR-SSP).
RESULTS
Patients with mild symptoms had higher frequency ofKIR2DS1 (p = 0.004) and KIR2DS2 (p = 0.017) genes compared to those with severe disease. While KIR3DL3 and deleted variant of KIR2DS4 occurred more frequently in patients who developed a severe form of the disease. In this study, a significant increase of and B haplotype was observed in the Mild group compared to the Severe group (respectively, p = 0.002 and p = 0.02). Also, the prevalence of haplotype A was significantly higher in the Severe group than in the Mild group (p = 0.02).
CONCLUSIONS
These results suggest that the KIR2DS1, KIR2DS, and B haplotype maybe have a protective effect against COVID-19 severity. The results also suggest the inhibitory gene KIR2DL3 and haplotype A are risk factors for the severity of COVID-19.
Topics: Humans; COVID-19; Receptors, KIR; Iran; Male; Female; Genetic Predisposition to Disease; SARS-CoV-2; Middle Aged; Adult; Severity of Illness Index; Haplotypes; Genotype; Gene Frequency; Killer Cells, Natural; Aged
PubMed: 38943065
DOI: 10.1186/s12865-024-00631-1