A 79-year-old man in the United States with large B-cell lymphoma and chronic obstructive pulmonary disease had disseminated Nocardia ignorata infection involving the brain and spleen. Despite antimicrobial therapy, he died from complications. This rare manifestation highlights the need to consider Nocardia in immunocompromised patients with central nervous system and abdominal lesions.

Both pulmonary tuberculosis (PTB) and sarcoidosis (SA) are chronic, systemic, granulomatous diseases. Due to their similar clinical and radiological features, as well as similar pathological characteristics, it is difficult to distinguish. This study aims to explore the pathological correlation between PTB and SA and the impact of nodules formation on the occurrence of PTB.

We retrospective enrolled 307 patients admitted to the tuberculosis department between January 2022 and March 2024. After applying the inclusion and exclusion criteria, 170 patients were divided into three groups and analyzed: sarcoid tuberculosis group (TB-N, n=59), non-sarcoid tuberculosis group (TB-NoN, n=74), and sarcoidosis group (SA, n=37). Comparative analysis was performed on the clinical characteristics, pathogen profiles, and pulmonary microbial composition differences among the three groups.

Patients in the TB-N and SA group predominantly presented with multiple nodules. Among samples testing positive by both mNGS and conventional microbiological tests (CMT), the proportion of partially matched results was higher in the TB-N group than in the TB-NoN group, with a greater diversity of pathogenic bacteria detected in the TB-N group. ACE index analysis revealed significantly higher microbial richness in the TB-NoN group compared to both SA and TB-N groups. Regarding treatment regimens, combination therapy was more frequently administered in the TB-N group, while single drug treatment predominated in the TB-NoN group. Although the duration of anti-tuberculosis treatment was longer in the TB-N group, this difference did not reach statistical significance.

Significant differences in imaging manifestations were observed between TB-N and SA groups. The presence of nodules was associated with a more complex pathogen profile in PTB patients; however, the pulmonary microbial diversity was lower in TB-N than in TB-NoN. PTB patients with nodules predominantly received combination therapy.

Severe community-acquired pneumonia (SCAP) remains a major cause of mortality in the paediatric population, with current diagnostic and treatment approaches often proving insufficient and contributing to the growing challenge of antibiotic resistance. This study explored the potential of metagenomic next-generation sequencing (mNGS) of bronchoalveolar lavage fluid as a tool to enhance the precision of antibiotic management in children with SCAP.

A retrospective cohort study of 202 paediatric patients with community-acquired pneumonia (aged 1 month-18 years) admitted to the First Affiliated Hospital of Xinxiang Medical University (November 2020-March 2023) was conducted. Patients were grouped by severity (intensive care unit [ICU]/non-ICU) and mNGS timing (early: ≤72 hours post-admission; late: >72 hours). The diagnostic efficacy of mNGS versus conventional microbiological techniques (CMT) was evaluated using sensitivity, specificity, positive/negative predictive values and area under the receiver operating characteristic curve (AUC) analysis. Antibiotic adjustments and clinical outcomes were analysed via survival statistics.

Metagenomic next-generation sequencing showed a higher positive detection rate (98.51%) than CMT (47.52%) (AUC = 0.82, 95%CI: 0.76-0.88). Of the 202 patients, 127 (62.87%) were male, with a median age of 1.88 years (interquartile range: 0.29-7 years). Early mNGS was associated with fewer extrapulmonary complications (69.63% vs 55.22% in the late group, p < 0.05), and shorter hospitalisation (median 13 vs 15 days, p <0.01). Antibiotic escalation occurred in 50 (24.75%) cases, de-escalation in 22 (10.89%) and same-level adjustment in 25 (12.38%).

Metagenomic next-generation sequencing outperforms CMT in pathogen detection. Early mNGS is associated with improved clinical outcomes, suggesting its potential utility in paediatric SCAP management.

Even with the development of the Pfizer-BioNTech BNT162b2 vaccine, which provides protection against COVID-19 and demonstrates high efficacy in generating immune responses, the complexities of the dynamics linking pro- and anti-inflammatory cytokine profiles with anti-spike IgG production remain unclear. The study aims to elucidate these immune dynamics after vaccination. This prospective cohort research was done at the University of Diyala from January 2022 to January 2023, evaluating the immunological response to the Pfizer-BNT162b2 mRNA vaccine in 180 healthy students. Pro- and anti-inflammatory cytokines and anti-spike IgG antibodies were measured before vaccination, 1 month after the second dose, and 4 months after the second dose. Biomarkers were analyzed via ELISA and CRP assays. The study involved 180 healthy participants (80 males, 100 females; median age, 21 years; BMI, 25.7 kg/m²). After the first Pfizer-BNT162b2 vaccine dose, the level of anti-spike IgG increased by 330-fold, and the levels of pro- and anti-inflammatory markers, such as IL-1β, IL-10, and CRP, increased significantly. Four months after the second dose, anti-spike IgG levels were 136-fold above baseline. Significant correlations emerged between cytokine and IgG levels, with anti-spike IgG/IL-10 ratios elevated and sustained over the long term. Pfizer-BNT162b2 vaccine elicits a significant immune response associated with changes in pro-inflammatory cytokines, and the interaction between these cytokines and anti-spike IgG suggests a potential role for immune regulation in enhancing humoral immunity. Based on these findings, the IgG/IL-17 ratio may serve as a viable exploratory biomarker for assessing short- and medium-term vaccination efficacy.

The aim was to examine which factors contribute to the willingness to pay (WTP) for health insurance in Germany.

Cross-sectional data are taken from a large, population-based study (GESIS panel, wave 50, n = 4,447; November 2022 to January 2023). Willingness to pay for health insurance served as outcome measure. Socioeconomic, health-related, coronavirus-related, and political spectrum-related factors were included as independent variables. Multiple linear regressions with cluster-robust standard errors were used.

Monthly average WTP for health insurance was €258 (SD: €210). A higher WTP for health insurance was associated with being male (female vs. male: β = -0.56.6, 95% CI: -67.7 to -45.5), being older (β = 2.1, 95% CI: 1.6-2.6), higher education (e.g., intermediary school leaving certificate vs. general/subject-specific university entrance qualification: β = -67.3, 95% CI: -80.7 to -53.8), higher income group (e.g., 1,700-2,300 € vs. under 900 €: β = 79.8, 95% CI: 36.1-123.5), not being married and living together with spouse (e.g., single vs. married/partner living together: β = 28.9, 95% CI: 12.4-45.4) as well as being politically more right-wing oriented (e.g., right-wing vs. left-wing: β = 33.4, 95% CI: 4.5-62.3).

In contrast to health- and coronavirus-related factors, socioeconomic and political spectrum-related factors were significantly associated with WTP for health insurance in Germany. Moreover, based on the average WTP, one can conclude that individuals do not fully agree with the present contributions to statutory health insurance in Germany as a whole during the Covid-19 pandemic. Future research could focus on cross-country comparisons (with varying healthcare systems and also between individualistic and collectivistic cultures).

Since the end of 2019, a novel coronavirus known as COVID-19 has caused a severe outbreak worldwide. Due to the complexity of epidemic data, traditional algorithms have struggled to accurately predict the development of the pandemic. The Autoregressive Integrated Moving Average (ARIMA) model is capable of capturing time-based trends in epidemic data, including seasonality, cyclic patterns, and long-term trends, which helps improve the accuracy of forecasting future epidemic trajectories. The Bidirectional Long Short-Term Memory (BiLSTM) network, a variant of the Recurrent Neural Network (RNN), is highly effective in handling sequential data. In epidemic data analysis, BiLSTM models can be applied to forecast future trends or conduct time series predictions. BiLSTM is able to capture temporal relationships and sequential patterns within data, thereby providing more accurate predictions. Genetic Algorithms (GA), inspired by biological evolution through operations such as selection, crossover, and mutation, offer an efficient approach to identifying the best-fit models and parameter configurations. By using GA, we can iteratively optimize epidemic forecasting models and enhance their performance over time. In this study, we proposed a hybrid model called GA-BiLSTM-ARIMA. Using COVID-19 case data from Japan, we calculated the GA-BiLSTM-ARIMA model's evaluation metrics: RMSE, MAE, MAPE, and R ², which were 2,262.42, 1,672.07, 6.81, and 0.9764, respectively. The results demonstrate that the hybrid model outperforms both the standalone BiLSTM and ARIMA models in predictive performance. The GA-BiLSTM-ARIMA model successfully integrates the strengths of different models through a systematic and intelligently optimized hybrid strategy. When forecasting infectious disease time series data, this model achieves higher and more robust predictive accuracy compared to traditional single models or partial hybrid models. This type of analysis supports the development of more effective prevention and control strategies and delivers accurate information and early warnings to the public and policymakers, contributing to a better global response to pandemic challenges.

Acute mountain sickness (AMS) is a common pathological response following rapid ascent to high altitude, and its underlying mechanisms remain incompletely understood. This study focused on investigating the early changes in body composition following acute high-altitude exposure and their association with AMS.

A prospective cohort study was conducted on healthy individuals who flew from plain (500 m) to plateau (3,680 m). Multi-frequency bioelectrical impedance analysis was used to measure body composition indicators within 24 h before and 24 h after ascent. AMS was diagnosed and its severity assessed using the Lake Louise Score System (LLSS). Comparisons of body composition changes before and after high-altitude exposure were performed, and intergroup comparisons were conducted based on the occurrence of AMS. Spearman correlation analysis and multiple linear regression were used to explore the relationships between body composition changes and LLSS score.

A total of 34 participants were included, and the AMS incidence was 61.76%. Compared with the plain baseline, after rapid ascent to high altitude, extracellular-to-intracellular water ratio (ECW/ICW) (t = -2.41, p = 0.022) and phase angle (PhA) (t = -4.78, p < 0.001) decreased significantly, and participants with AMS exhibited greater magnitude decrease in both ΔECW/ICW (Z = -2.14, p = 0.032) and ΔPhA (Z = -3.01, p = 0.003). Spearman correlation analysis showed that both ΔECW/ICW (r = -0.447, p = 0.008) and ΔPhA (r = -0.646, p < 0.001) were negatively correlated with the LLSS score. However, all results of ECW/ICW and ΔECW/ICW shown no statistically significant after Bonferroni correction. Multiple linear regression analysis indicated that only ΔPhA was an independent predictor of the LLSS score (β = -0.507, p = 0.001).

After rapid exposure to high altitude, body water often shifted into cells and pronounced cellular-level dysfunction occurred. Susceptible individuals with AMS exhibited a more pronounced decrease in PhA. The change of PhA (ΔPhA), rather than the change of ECW/ICW (ΔECW/ICW), was an independent predictor of AMS severity. Monitoring ΔPhA may provide an effective, non-invasive method for early risk warning of AMS.

The coronavirus disease 2019 (COVID-19) pandemic has profoundly affected older populations globally. However, limited data are available on the long-term survival and evolving care needs of older patients with COVID-19 in Japan. We evaluated 1-year survival rates and long-term care needs in this vulnerable population.

This population-based cohort study analyzed 2175 hospitalized patients with COVID-19 aged ≥ 65 years using data from the Nara Kokuho Database between February 2020 and August 2022. The database contains administrative claims data from the National Health Insurance and Late Elders' Health Insurance systems, incorporating information on care needs levels. We examined 1-year survival and changes in care needs levels.

The overall 1-year survival rate was 80.9%. Age significantly influenced survival, with rates of 93.6% for patients aged 65-69 years and 56.6% for those ≥ 90 years. Disease severity had a substantial impact: patients not requiring oxygen therapy had a survival rate of 87.5%, compared to 74.0% for those with supplemental oxygen and 49.9% for those receiving invasive mechanical ventilation. Despite a decline in severe cases following the Delta and Omicron-predominant waves, survival among severely ill patients remained lower. While most patients without prior care needs retained independence, approximately 23% developed new care requirements after COVID-19. Higher care needs levels were associated with significantly higher mortality, with patients at care needs level ≥ 2 experiencing mortality rates exceeding 35%.

This study highlights the importance of comprehensive management of the long-term survival and care needs of older patients with COVID-19.

ObjectivesTreatable traits are identifiable and treatable features of disease. The primary objective was to evaluate whether a bronchiectasis clinic in a regional setting, implementing a treatable traits approach, would lead to improvements in quality of life. Secondary objectives were to evaluate clinic implementation outcomes, annual exacerbations and hospitalisation.MethodsAn implementation study. Adults with bronchiectasis attending an outpatient clinic in Rockhampton, Australia between 2021 and 2023 were recruited. Clinical care was implemented by a physiotherapist and nurses according to traits identified. Quality of life (QoL-Bronchiectasis respiratory symptom score (QoL-B rs), Leicester Cough Questionnaire (LCQ) total score) was evaluated at baseline, three and 12-months from first attendance. Implementation outcomes were determined by clinic appointment uptake and attendance and pulmonary rehabilitation uptake and completion at 12-months. Exacerbation frequency and hospitalisation for the 12-months before and after enrolment were compared.Results50 participants were recruited [mean (SD) age 71 (12) years; Bronchiectasis Severity Index (%) mild (22) moderate (44) severe (34)]. There was a significant improvement in QoL at three and 12-months from baseline (mean difference, 95% CI) [3 months: QoL-B rs 9.2 (3.2-15.2), LCQ 1.7 (0.6-2.8); 12-months: QoL-B rs 10.1 (3.9-16.3), LCQ 1.8 (0.8-2.8)]. Clinic uptake (91%), attendance (97%) and pulmonary rehabilitation uptake (74%) and completion (84%) were high. Exacerbation frequency [median (IQR) 1.0 (3.0) to 0.0 (1.0), p = 0.007], and hospitalisation decreased (18% vs 0%, p = 0.005).DiscussionA treatable traits approach improved quality of life, was feasible, including the achievement of high pulmonary rehabilitation uptake and completion, and reduced exacerbation frequency and hospitalisation.

Preeclampsia (PE) is characterized by antenatal hypoxia and inflammation, which may impair neonatal respiratory control. However, its association with apnea of prematurity (AOP) in late preterm infants remains unclear. This study aimed to test the hypothesis that maternal PE is an independent risk factor for AOP in late preterm infants.

This retrospective cohort study was conducted at Yamanashi Central Hospital between January 2017 and June 2025. Singleton pregnancies delivered between 34^ + 0 and 36^ + 6 weeks of gestation were included. Early-onset PE was defined as diagnosis before 34 weeks of gestation, and late-onset PE as diagnosis at or after 34 weeks. Multivariable logistic regression analyses were performed to evaluate the association between PE and AOP, adjusting for potential confounders. In Model 1, PE was analyzed as a binary variable. In Model 2, subjects were categorized into three groups: early-onset, late-onset, and no PE.

A total of 470 mother-infant pairs were analyzed. Among them, 23 women experienced early-onset PE, and 34 experienced late-onset PE. The overall incidence of AOP was 27.9% (131/470). In Model 1, PE was associated with a higher AOP risk (adjusted odds ratio [aOR], 2.44; 95% confidence interval [CI]: 1.21-4.90). In Model 2, early-onset PE was significantly associated with AOP (aOR: 3.42; 95% CI: 1.20-9.72), whereas late-onset PE was not (aOR: 1.88; 95% CI: 0.75-4.73).

PE, particularly early-onset, was significantly associated with AOP. Incorporating maternal PE status into perinatal risk stratification may help identify late preterm infants at elevated AOP risk and guide timely intervention.