Henan Medical University

Influenza viruses cause seasonal epidemics almost every year, and it is difficult to diagnose quickly and accurately. Machine learning and peripheral blood protein omics have brought new ideas to the research of clinical markers.

Prediction of key molecular marker of influenza virus infection by the established machine learning model and peripheral blood protein omics.

This study used the testing data of 850 patients (including influenza, COVID-19 and mixed infections) and 265 healthy individuals, to establish and validate a diagnostic prediction model for influenza infection and verified the potential value of this model in the differential diagnosis of influenza, COVID-19 and healthy people.

The overall analysis showed that there were significant differences in 9 clinical features in the influenza group. Principal component analysis can effectively group samples based on these clinical features. Based on the random forest model and LASSO regression model found that the selected features are clinical indicators that can accurately distinguish influenza patients. We performed proteome sequencing combined with machine learning and found a total of 26 DEPs. Through PPI and WGCNA analysis, we identified several genes related to the proportion of monocytes. We then analyzed the correlation of these factors with immune cell proportions and found that SAA1 and SAA2 were highly correlated with various vital immunocyte. ROC curve analysis shows that SERPINA3 can distinguish influenza, COVID-19, mixed infection and healthy people; SAA1 can distinguish COVID-19, mixed infection and healthy people; SAA2 can distinguish influenza and healthy people. In influenza, high expression of SERPINA3, SAA1, and SAA2 is associated with higher risk. Finally, we used the ELISA method to confirm that SAA2 protein can be used as an auxiliary diagnostic indicator for influenza infection.

Preliminary results showed that SAA2 is an important molecular marker specific to influenza infection.

To investigate the association between renal mean perfusion pressure (MPP) and prognosis in sepsis-associated acute kidney injury (SA-AKI).

Data were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Group-based trajectory modeling (GBTM) was applied to identify dynamic MPP patterns, while restricted cubic spline (RCS) curves were utilized to confirm the non-linear relationship between MPP and mortality. Cox regression analysis assessed the risk of mortality across different MPP levels, adjusting for potential confounders. Subgroup analyses and sensitivity analyses were conducted to ensure the robustness of the findings.

A total of 2318 patients with SA-AKI were stratified into five MPP trajectories by GBTM. Patients in Traj-1 and Traj-2, characterized by consistently low MPP (<60 mmHg), demonstrated markedly higher 90-d mortality (62.86% and 26.98%). RCS curves revealed a non-linear inverse relationship between MPP and 90-d mortality, identifying 60 mmHg as the optimal threshold. Patients with MPP ≤ 60 mmHg exhibited significantly elevated 90-d mortality compared to those with MPP > 60 mmHg (29.81% vs. 20.88%). Cox regression analysis established Traj-1 and Traj-2 as independent risk factors for increased mortality relative to Traj-3 (60-70 mmHg), with hazard ratios (HRs) of 4.67 (95%-CI 3.28-6.67) and 1.45 (95%-CI 1.20-1.76). MPP > 60 mmHg was significantly associated with reduced 90-d mortality (HR 0.65, 95%-CI 0.55-0.77). Subgroup and PSM analyses supported these findings.

Dynamic MPP trajectory serves as a valuable prognostic biomarker for SA-AKI. Early monitoring of MPP trends offers critical insights into renal perfusion management, potentially improving outcomes in SA-AKI.

Background: Data on the effects of mindfulness-based stress reduction (MBSR) positive psychological traits and experiential avoidance (EA) among cancer patients are lacking.Objective: This randomized controlled trial (RCT) aimed to: (1) compare the efficacy between MBSR and treatment-as-usual (TAU) control groups in increasing posttraumatic growth (PTG), hope, and optimism and reducing EA across time measurements (T₀, T₁, and T₂) among head and neck cancer (HNC) patients and (2) evaluate the mediation effects of hope, optimism, and EA on the relationship between MBSR and PTG.Methods: A total of 80 HNC participants were randomized to MBSR (n = 40) and TAU (n = 40) groups with the researchers and data analyst blinded, and the group allocation of the participants was concealed. A one-hour MBSR session was conducted once a week, with 45 minutes of home assignments, for six weeks in the MBSR group. The outcomes across time measurements were compared using a mixed linear model following intention-to-treat (ITT) analysis. Mediation effects of hope, optimism, and EA on the relationship between MBSR and PTG were assessed with PROCESS.Results: MBSR significantly increased the degree of optimism from T₀ to T₁ (mean difference = 1.825, 95% CI = 0.907-2.743, SE = 0.381, p < .001) with a medium effect size (d = 0.563) and from T₁ to T₂ (mean difference = 1.650, 95% CI = 0.829-2.470, SE = 0.328, p < .001) with a medium effect size (d = 0.630). Initially, MBSR did not increase the degree of hope from T₀ to T₁ (p = .677), but it significantly increased hope from T₁ to T₂ (mean difference = 2.524, 95% CI = 1.676-3.373, SE = 0.340, p < .001) with a medium effect size (d = 0.735). Conversely, MBSR did not sustain the changes in the degree of PTG and EA beyond T₁. EA partially mediated the relationship between MBSR and PTG, but not hope and optimism.Conclusion: MBSR can be recommended as part of the treatment regimen for HNC patients.Trial registration: ClinicalTrials.gov identifier: NCT04800419.