While the relationship between serum phosphorus concentrations and the prognosis of chronic obstructive pulmonary disease (COPD) has been previously investigated, the prognostic significance of dynamic serum phosphorus fluctuations in patients with COPD admitted to intensive care units (ICUs) remains poorly understood. This study aimed to evaluate the association between longitudinal serum phosphorus trajectories during the early ICU stay and 28-day all-cause mortality, and to explore their potential utility as a prognostic indicator.

This retrospective cohort analysis utilized data from COPD patients registered in the Medical Information Mart for Intensive Care IV database (MIMIC-IV, v3.0). Group-based trajectory modeling (GBTM) was applied to delineate distinct serum phosphorus trajectories over the first seven days following ICU admission. The relationship between these trajectory groups and all-cause mortality was examined using Kaplan-Meier survival curves and multivariable Cox proportional hazards regression. Subgroup analyses were performed to verify the robustness of this association across different patient subgroups.

A total of 3627 ICU-admitted patients with COPD were enrolled. GBTM distinguished two distinct longitudinal patterns: a high-phosphate trajectory (mean phosphate range: approximately 5.0-6.5 mg/dL; n = 320, 8.8%) and a low/normal-phosphate trajectory (mean phosphate range: approximately 3.2-3.8 mg/dL; n = 3307, 91.2%). Patients in the high-phosphate group exhibited markedly lower 28-day cumulative survival. Multivariable Cox regression confirmed that this trajectory was independently associated with elevated 28-day mortality risk. Furthermore, the trajectory-based model demonstrated superior predictive performance compared with single-time-point phosphate measurements.

Longitudinal trends in serum phosphate levels serve as a significant and independent risk factor for short-term mortality in critically ill patients with COPD.

Acute lung injury (ALI) is characterized by a high mortality rates and acute inflammation that compromises the epithelial and endothelial barriers of the respiratory system. Isoalantolactone (IsoA) is a natural compound derived from Inula helenium that has been shown to exhibit anti-inflammatory activity. However, its ability to preserve epithelial barrier integrity or modulate mitogen-activated protein kinase/signal transducer and activator of transcription 3 (MAPK/STAT3) signaling and epithelial-mesenchymal transition (EMT) has not been evaluated in ALI.

Human bronchial epithelial cells (HBEC3-KT and BEAS-2B) were stimulated with lipopolysaccharide (LPS) with or without IsoA to assess cytotoxicity and cytokine release. In vivo, female C57BL/6 mice were pretreated with IsoA (10-20 mg/kg, intraperitoneally) or vehicle 1 h before LPS challenge (1 mg/kg, intratracheally). At 4 h, lung tissues and bronchoalveolar lavage fluid (BALF) were collected for histopathology and to measure total protein, cellular composition, and cytokine levels (tumor necrosis factor-α [TNF-α], interleukin-6 [IL-6], monocyte chemoattractant protein-1 [MCP-1]); leukocyte subsets were quantified by multicolor flow cytometry. Lung homogenates were analyzed using Western blot for phosphorylated/total extracellular signal-regulated kinase 1/2 (ERK1/2), c-Jun N-terminal kinase 1/2 (JNK1/2), p38 MAPKs, STAT3, and EMT markers (E-cadherin, N-cadherin, snail).

In LPS-stimulated HBEC3-KT and BEAS-2B cells, IsoA significantly reduced IL-6 secretion. In the murine model of LPS-induced ALI, IsoA administration alleviated lung tissue damage, decreased inflammatory cell infiltration, and reduced IL-6 and TNF-α concentration in BALF and serum. Mechanistically, IsoA attenuated the phosphorylation of ERK, JNK, and p38 MAPKs and suppressed STAT3 activation in lung tissues. Furthermore, IsoA attenuated EMT, as evidenced by decreased N-cadherin and snail expressions and restored E-cadherin expression.

IsoA alleviates LPS-induced ALI by reducing MAPK/STAT3 activation and EMT-associated epithelial injury. These findings suggest that IsoA may be a promising candidate for the targeted modulation of inflammatory lung injury, and that further preclinical and translational studies are justified.

While microRNA (miR) expression profiling has identified potential biomarkers in patients with COVID-19, the regulatory mechanisms by which miRs modulate disease severity remain poorly characterized. We performed integrated miR-proteome analysis to elucidate mechanistic relationships between miR regulation and COVID-19 severity.

Deidentified plasma samples from 93 participants with acute COVID-19 were categorized by severity using a 12-point symptom scoring system: mild (0-1), moderate (2-4), and severe (5-12). miR and proteomic profiles were analyzed using univariate statistics, pathway analysis, miR-target prediction, and correlation analysis. Differentially expressed miRNAs (DEMs) and differentially expressed proteins (DEPs) between the three severity groups from the original cohort were evaluated in a validation cohort of 94 participants.

We identified 365 unique miRs and 801 unique proteins that were significantly associated with COVID-19 severity in any of the three comparisons. Ingenuity pathway analysis revealed neutrophil degranulation, cytokine storm, interleukin-10 (IL-10) signaling, and wound healing signaling as top dysregulated pathways in severe versus mild cases, with IL-6 involved in 9 of the 10 most significant pathways. Correlation analysis between miRs and proteins from 93 participants identified 6,559 miR-protein pairs with |r| > 0.5, of which 83.6% were negative correlations, suggesting widespread miR-mediated downregulation of protein expression. A significant correlation (r = 0.43, p < 0.0001) was found between 122 predicted miR-protein pairs found in the discovery cohort and the same miR-protein pairs in the validation cohort.

To our knowledge, this represents the first integrated analysis of circulating miRs and proteins from the same COVID-19 participants and separately in a validation cohort. The high frequency of negative miR-protein correlations combined with target prediction analysis suggests that miRs play regulatory roles in COVID-19 severity-associated pathways. These findings provide mechanistic insights into miR regulation of host immune responses and identify potential biomarkers that could inform therapy of COVID-19.

This study adopted both variable-centered and person-centered approaches to examine the internal relationships between self-compassion and rhinitis control assessment in patients with allergic rhinitis, and to explore heterogeneity in self-compassion using latent profile analysis.

From February 2026 to April 2026, 419 patients with allergic rhinitis were recruited from three tertiary Grade A hospitals in Sichuan Province, China. Data were collected using the Self-Compassion Scale, the Rhinitis Control Assessment Test, the Allergic Rhinitis Self-Care Scale, and a demographic information questionnaire.

Self-compassion was significantly and positively associated with rhinitis control (r = 0.472) and self-care (r = 0.592), and self-care was positively associated with rhinitis control (r = 0.693; all p < 0.01). In cross-sectional mediation analysis, the indirect association of self-compassion on rhinitis control through self-care was 0.542 [95% confidence interval (CI): (0.431, 0.655)], accounting for 79.09% of the total association [0.685, 95% CI: (0.563, 0.807)]; the direct association was 0.143 [95% CI (0.020, 0.266), 20.91%]. Latent profile analysis identified four self-compassion profiles: the self-kind but low-mindfulness group (10.5%, n = 44), the low self-compassion group (9.3%, n = 39), the high self-compassion with elevated self-coldness group (70.6%, n = 296), and the mindful-but-isolated group (9.5%, n = 40). The four profiles did not differ significantly on self-care (F = 0.389, p = 0.761) or rhinitis control (F = 0.915, p = 0.434).

In this cross-sectional study of patients with allergic rhinitis, self-compassion and self-care were associated with rhinitis control, and self-care accounted for a substantial proportion of the association between self-compassion and rhinitis control. Four latent self-compassion profiles were identified but did not differ on self-care or rhinitis control. These findings may inform the future longitudinal and intervention research; however, the cross-sectional design does not establish causal pathways, and the effectiveness of profile-based interventions remains to be tested.

To compare documented patterns of healthcare-system disruption and response across WHO-declared Public Health Emergencies of International Concern (PHEICs) using the established 4S surge-capacity framework (Staff, Stuff, Space, Systems) and an author-developed 12-indicator operationalization.

We conducted a structured comparative case analysis, not a systematic review of intervention effects, of all eight PHEIC events declared by WHO through 31 March 2026. The unit of analysis was the event, and the coded observations were operational occurrences documented in official sources. Twelve binary, unweighted author-developed indicators, three per 4S domain, were mapped to the four established domains. Activation required either one primary official source or two independent secondary sources meeting a prespecified threshold. Two reviewers independently assigned indicator-level codes, with disagreements resolved by consensus; indicators classified as Not Reported were combined with Not Activated. Activated indicators were summed within domains (range 0-3), and each observed configuration was assigned to one of four profiles using a fixed hierarchical rule sequence.

All eight events activated at least one indicator, and external workforce mobilization (S1) and diagnostic bottleneck (T3) were documented in every event. COVID-19 was the only event activating all 12 indicators (systemic convergence). The two Ebola virus disease events shared an identical configuration of complete Staff, Stuff, and Systems activation with Space limited to temporary or repurposed facilities (workforce-cascade). H1N1 influenza, poliomyelitis, and mpox clade I formed the residual partial multi-domain category and did not share a common domain signature. Zika virus disease and mpox clade II met the limited-domain rule, with no Space-domain activation.

Public Health Emergencies of International Concern designation was not sufficient to identify a common pattern of documented health-system disruption. The typology is a provisional descriptive partition rather than a severity ranking, causal model, or predictive tool, and its residual category is heterogeneous by construction. Profiles are conditional on the documentary evidence base and the settings it represents; baseline health-system capacity was not incorporated, so the same pathogen could plausibly yield a different configuration elsewhere. The framework may offer a common vocabulary for discussing domain-specific preparedness needs under the revised International Health Regulations and the developing WHO Pandemic Agreement, but external validation is required.

Following the relaxation of pandemic control measures in China, the impact of coronavirus disease 2019 (COVID-19) on cognitive function in the Chinese population has not been investigated. Therefore, this study aimed to assess cognitive function and identify its risk factors in the subacute phase (<3 months) of COVID-19 patients during the epidemic wave of the Omicron variant. In this nationwide smartphone-based online assessment from January 15 to 29, 2023, the Integrated Cognitive Assessment (ICA) and the Number Ordering Test (NOT) were used to assess cognitive function. Among the 9663 participants, 8905 (92.2%) were COVID-19 survivors. These patients performed poorly on most neuropsychological results (P<0.05); however, after controlling for socio-demographics, only the ICA accuracies were lower (P<0.05, partial η²≤0.001). After the initial recovery, 5832 (65.5%) COVID-19 survivors reported inflexible thinking, 5419 (60.9%) noted slowed information processing speed, and 4344 (48.8%) reported insomnia. Female sex, older age, low education level, living in Northwest China, and insomnia were significantly associated with poor performance on the key neuropsychological results (range, absolute β values 0.023‒0.430, P<0.05). In the subacute phase of COVID-19, subjective cognitive complaints were highly prevalent, whereas objective cognitive differences were subtle. Despite the mild overall impact, vulnerable subgroups-such as females, older adults, individuals with low education level, and patients with insomnia-showed greater susceptibility to these cognitive changes.

Depression and anxiety together are ranked among the leading causes of disability, affecting hundreds of millions of individuals worldwide (GBD 2019 Mental Disorders Collaborators, 2022). Yet, psychiatric care faces persistent structural barriers: limited specialist availability, geographic inequities, diagnostic subjectivity, and the stigma that deters help-seeking behaviors (Patel et al., 2018). Digital technologies, including mobile health platforms, internet-delivered interventions, and artificial intelligence (AI)-driven analytic tools, are beginning to address these constraints by extending the reach and scalability of mental health services (Graham et al., 2019; Torous et al., 2021). This transition was accelerated by the coronavirus disease 2019 (COVID-19) pandemic, as clinicians worldwide adopted remote assessment and teletherapy out of necessity (Wind et al., 2020). The promise of these tools, however, goes beyond crisis response: it not only enables population-level screening but also provides new forms of therapeutic engagement and the discovery of objective diagnostic biomarkers.

Cytomegalovirus (CMV) is a prevalent herpesvirus that remains typically asymptomatic in immunocompetent individuals but can cause severe, potentially life-threatening disease in immunocompromised patients. This study assessed the impact of CMV-associated lower respiratory tract infections (LRTIs), diagnosed by polymerase chain reaction (PCR) testing of bronchoalveolar lavage fluid (BALF), focusing on coinfections and clinical outcomes. Between 2009 and 2017, a total of 2666 visits involving 1301 immunocompromised patients with suspected LRTI who underwent diagnostic bronchoalveolar lavage (BAL) were analyzed. The primary outcomes included CMV detection in BAL and resulting treatment modifications. Among these, 235 BALFs from 157 patients tested positive for CMV. Among these CMV-positive cases, immunosuppression was classified as hematological conditions (n = 88), solid organ transplantation (n = 70), or other causes of immunosuppression (n = 77). Overall, 59 cases exhibited isolated CMV infection, which was associated with a higher percentage of macrophages in BALF, compared to cases with CMV and coinfection [75.5%, 95% confidence interval (CI): 44.0-85.0 vs. 37.5%, 95% CI: 11.5%-67.0%, p = 0.008]. A total of 176 cases demonstrated CMV and coinfection, which were associated with neutrophilia in BALF (49.5%, 95% CI: 15.5-82.5; vs. CMV-monoinfection: 7.5%, 95% CI:2-36, p = 0.002). BAL findings prompted treatment modifications in 61.7% (n = 145) of all CMV-positive cases. The 30-day mortality rate was 13.6%, with a significantly elevated hazard ratio for those with CMV-positive BAL diagnosis (unadjusted HR 2.85, 95% CI: 1.86-4.36). Cases with a CMV infection exhibited a significantly higher probability of requiring hospitalization compared to CMV-negative cases, (OR 1.39, 95% CI: 1.03-1.87, p = 0.030). Immunocompromised patients have an increased risk of CMV infection, and the presence of CMV does not exclude a coinfection with other respiratory viruses, bacteria, or fungi. CMV detection in BALF was associated with higher hospitalization rates and mortality. Preventive and preemptive antiviral strategies are crucial, especially given the adverse impact of coinfections.

Influenza A virus (IAV) remains a major public health threat due to its high genetic variability and ability to evade host immune responses. While antiviral drugs and vaccines remain the key measures against influenza infection, their limited efficacy and the emergence of resistant viral strains have prompted growing interest in exploring complementary therapeutic approaches. In this review, we critically evaluate current evidence on how plant-derived compounds modulate innate immune signaling networks, including toll-like receptor, retinoic acid-inducible gene I, mitogen-activated protein kinase, and nuclear factor kappa B pathways during IAV infection. We further address the pathway cross-talk that underpins this broad immunomodulatory activity, the translational barriers including bioavailability, metabolic transformation, and standardization that currently limit clinical progression, and the mechanistic gaps that future studies must resolve. Collectively, this review argues that phytocompounds should be understood as modulators of an integrated innate immune network, an insight that reframes both their therapeutic potential and the experimental standards required to advance them.

Eosinophilic granulomatosis with polyangiitis (EGPA) is a systemic vasculitis characterized by high clinical heterogeneity. The conventional binary classification based on antineutrophil cytoplasmic antibody (ANCA) status inadequately captures this heterogeneity and provides limited guidance for individualised treatment decisions. This study aimed to identify and validate clinical-biological endophenotypes in EGPA using unsupervised machine learning methods based on multidimensional real-world clinical data, to inform precision medicine approaches.

In this retrospective single-centre observational study, we included 205 patients diagnosed with EGPA between January 2015 and December 2023 at China-Japan Friendship Hospital. Comprehensive data on demographics, clinical manifestations, laboratory tests, imaging features, and treatment information were systematically collected. Consensus clustering combined with K-means algorithm was applied to identify patient subgroups based on 14 core features, including peripheral blood eosinophil count, fractional exhaled nitric oxide (FeNO), serum total IgE, C-reactive protein (CRP), ANCA status, and multi-system involvement. Principal component analysis was used for dimensionality reduction and visualisation. Differences in clinical characteristics, laboratory parameters, imaging patterns, and treatment responses were compared among the subgroups. An online prediction tool was developed and validated based on the clustering results.

Consensus clustering analysis identified three stable endophenotypes (optimal cluster number k = 3). Subgroup 1 (n = 78, 38.0%): Eosinophilic-Airway Inflammation Type, characterized by markedly elevated eosinophil count (6.10 × 10⁹/L, IQR: 5.10-8.00), FeNO (79 ppb, IQR: 55-112) and serum total IgE (798 IU/mL, IQR: 602-980), predominant respiratory symptoms, with ground-glass opacities and consolidation as main findings on chest high-resolution computed tomography, and among patients who received mepolizumab, a partial remission rate of 89.7% was observed. Subgroup 2 (n = 65, 31.7%): ANCA-Systemic Vasculitis Type, defined by a significantly higher ANCA positivity rate (26.2%) compared to the other subgroups, significant systemic inflammatory response (CRP: 33.5 mg/L, IQR: 22.5-48.5) and frequent peripheral nervous system involvement (76.9%), receiving higher initial glucocorticoid doses. Subgroup 3 (n = 62, 30.2%): Paucieosinophilic-Multi-Organ Damage Type, presenting with relatively low eosinophil-related indices but higher proportions of skin, gastrointestinal, and cardiac involvement, commonly showing fibrotic changes on imaging, and with a numerically lower observed overall response rate to available biologics (51.6%). The developed online prediction tool showed good performance in an independent validation cohort (overall accuracy 90.0%, Kappa agreement with expert judgement 0.850).

Using unsupervised machine learning on real-world data, this study successfully identified three distinct clinical-biological endophenotypes in EGPA, revealing different underlying pathophysiological mechanisms and treatment response patterns, and developed a clinically translatable prediction tool. This endophenotype framework and tool may help inform individualised treatment strategies and provide an exploratory data-driven classification basis and decision support for future precision medicine research in EGPA.