Suzhou Vocational Health College

To examine whether digital health literacy, chronic disease self-efficacy, and psychological distress mediate the relationship between social support and self-management behavior in older adult patients with chronic diseases. Using convenience sampling, we surveyed 536 older adult patients with chronic illnesses from three tertiary hospitals in Anhui Province between October 2023 and May 2024. Instruments included a demographic questionnaire, Digital Health Literacy Scale, Social Support Rating Scale, Brief Symptom Rating Scale, Chronic Disease Self-Efficacy Scale, Chronic Disease Self-Management Study Scale, and the age-adjusted Charlson Comorbidity Index. The average self-management score was 20.87 ± 10.37. Self-management behavior was positively correlated with social support, digital health literacy, and self-efficacy (r = 0.391, 0.536, 0.423; all P < 0.01), and negatively correlated with psychological distress (r = − 0.265, P < 0.01). Social support had a significant total effect on self-management (P < 0.001; 95% CI: 0.488–0.920), including both direct (P = 0.008; 95% CI: 0.033–0.278) and indirect effects (P < 0.001; 95% CI: 0.376–0.726). Mediators accounted for 72.26% of the total effect, with chain mediation explaining 18.25%. Older adult patients with chronic diseases exhibit low levels of self-management. Digital health literacy and self-efficacy are sequential mediators linking social support to improved self-management. Interventions targeting these mediators may enhance chronic disease management in this population.

Background Conventional symptomatic treatments for allergic rhinitis often yield limited efficacy, prompting growing acceptance of traditional Chinese medicine modalities such as acupuncture. Adjunctive acupuncture at the Neiyingxiang (EX-HN 9) point shows promise for improving patient outcomes, but existing research is hindered by methodological flaws that compromise result reliability and applicability. Objective This study aimed to evaluate the efficacy and safety of Neiyingxiang acupuncture as an adjunct to conventional symptomatic treatment for allergic rhinitis, focusing on disease control and short-term prognosis. Methods From March 2019 to September 2024, 100 eligible patients were randomly assigned to two groups ( n = 50 each). The control group received standard symptomatic management (allergen avoidance, diet/exercise guidance, and intranasal fluticasone propionate spray), whereas the observation group received the same standard care in combination with Neiyingxiang acupuncture. The key outcomes included clinical effective rate, nasal symptom/sign scores, total nasal symptom score (TNSS), total non-nasal symptom score (TNNSS), serum biomarkers (IgA, IgE, IL-1, IL-4), and adverse events. Results The observation group achieved a significantly higher total effective rate (96.00%) than the control group (80.00%, P &lt; 0.05). At 1-month follow-up, the observation group showed more substantial reductions in nasal symptom/sign scores, TNSS, and TNNSS (all P &lt; 0.05). Serum analyses revealed superior immunomodulatory effects in the observation group: greater increases in IgA and IL-1, and more marked decreases in IgE and IL-4 (all P &lt; 0.05). Adverse reaction rates were 6.00% (observation group) and 8.00% (control group), with no statistical difference ( P &gt; 0.05). Conclusion Neiyingxiang acupuncture combined with conventional symptomatic treatment significantly enhances the management of allergic rhinitis, delivering more effective symptomatic relief and robust immunomodulatory benefits without increasing adverse risk.

Bispecific antibodies (BsAbs) are engineered immunoglobulins that can simultaneously recognize two distinct antigens or two distinct epitopes on the same antigen. They exhibit cooperative therapeutic effects surpassing those of natural monoclonal antibodies, such as bridging the immune cells and tumor cells to stimulate targeted immune response, or blocking codependent signaling pathways. These advantages make them attractive therapeutic reagents for various diseases such as cancers, immunodeficiency syndromes, and ophthalmic disorders. However, the unique structural characteristics of BsAbs pose various challenges to their preparation. In the past few decades, various types of BsAbs have been designed and prepared through genetic engineering or chemical conjugation strategies, many of which have been approved as drugs or entered clinical trials. This review provides a systematic summary of these strategies and their corresponding principles, and focuses on the application of modern genetic engineering and chemical conjugation methods in the generation of BsAbs.

Insects heavily depend on chemical communication to mediate ecological interactions, where specific compounds trigger distinct behavioral responses shaping their life history traits. Odorant-binding proteins (OBPs) are critical targets for identifying behavior-modulating compounds. Here, we employed a reverse chemical ecology approach to identify potential larval attractants of Spodoptera litura. This study first analyzed the tissue distribution and ligand-binding profile of S. litura OBP8 (SlitOBP8) using quantitative real-time PCR and fluorescence-based competitive binding assays, determined that SlitOBP8 was highly expressed in the antennae of larvae, and preliminarily screened 12 ligands. Subsequently, RNA interference (RNAi), site-directed mutagenesis, and behavioral assays were combined to functionally validate candidate odorants eliciting larval attraction. The results indicated that SlitOBP8 played a significant role in the process of larvae recognizing the 2-heptanone. Our findings deepen understanding of olfactory mechanisms underpinning host plant selection by insect herbivores and provide a foundation for novel behavior-based control strategies against S. litura.

This study addresses the challenges of high-dimensional data, such as the curse of dimensionality and feature redundancy, which can be viewed as an inherent asymmetry in the data space. To restore a balanced symmetry and build a more complete feature representation, we propose an enhanced feature engineering model (EFEM) that employs a novel dual-strategy approach. First, we present a symmetrical feature selection algorithm that combines an improved Dolphin Swarm Algorithm (DSA) with the Maximum Relevance–Minimum Redundancy (mRMR) criterion. This method not only selects an optimal, high-relevance feature subset, but also identifies the remaining features as a complementary, redundant subset. Second, an ensemble learning-based feature reconstruction algorithm is introduced to mine potential information from these redundant features. This process transforms fragmented, redundant information into a new, synthetic feature, thereby establishing a form of information symmetry with the selected optimal subset. Finally, the EFEM constructs a high-performance feature space by symmetrically integrating the optimal feature subset with the synthetic feature. The model’s superior performance is extensively validated on nine standard UCI regression datasets, with comparative analysis showing that it significantly outperforms similar algorithms and achieves an average goodness-of-fit of 0.9263. The statistical significance of this improvement is confirmed by the Wilcoxon signed-rank test. Comprehensive analyses of parameter sensitivity, robustness, convergence, and runtime, as well as ablation experiments, further validate the efficiency and stability of the proposed algorithm. The successful application of the EFEM in a real-world product demand forecasting task fully demonstrates its practical value in complex scenarios.