Anti-inflammatory and Immune-regulatory Effects of Subcutaneous Perillae Fructus Extract Injections on OVA-induced Asthma in Mice

Perillae fructus (perilla seed) is a traditional medicinal herb used to treat bronchial asthma in Oriental medical clinics. ST36 is one of the most widely used acupuncture points, particularly for immune system regulation. Injection of an herbal extract into an acupuncture point (herbal acupuncture) is a therapeutic technique combining both acupuncture and herbal treatment. Perillae fructus extract was injected subcutaneously (Perillae fructus herbal acupuncture; PF-HA) at acupoint ST36 of OVA-induced asthmatic mice. The lung weight, bronchoalveolar fluid (BALF) cell count, the number of CCR3+, CD11b+, CD4+ and CD3e+/CD69+ cells in the lung, and the level of IgE, IL-4, IL-5 and IL-13 in BALF and serum were then measured. RT-PCR was used to measure the mRNA expression of IL-4, IL-5, IL-13 and TNF-α in the lung. Lung sections were analyzed histologically. PF-HA significantly reduced lung weight, the number of inflammatory cells in the lung and BALF, the levels of IgE and Th2 cytokines in BALF and serum, mRNA expression of Th2 cytokines in the lung, and pathological changes in lung tissue. Our results suggest that PF-HA may have an anti-inflammatory and immune-regulatory effect on bronchial allergic asthma by restoring the Th1/Th2 imbalance in the immune system and suppressing eosinophilic inflammation in airways

Orientation of Rhizophagus grandis (Coleoptera: Rhizophagidae) to oxygenated monoterpenes in a species-specific predator-prey relationship

SCOPUS: ar.jinfo:eu-repo/semantics/publishe

A hybrid algorithm for k-medoid clustering of large data sets

In this paper, we propose a novel local search heuristic and then hybridize it with a genetic algorithm for k-medoid clustering of large data sets, which is an NP-hard optimization problem. The local search heuristic selects k-medoids from the data set and tries to efficiently minimize the total dissimilarity within each cluster. In order to deal with the local optimality, the local search heuristic is hybridized with a genetic algorithm and then the Hybrid K-medoid Algorithm (HKA) is proposed. Our experiments show that, compared with previous genetic algorithm based k-medoid clustering approaches - GCA and RAR/sub w/GA, HKA can provide better clustering solutions and do so more efficiently. Experiments use two gene expression data sets, which may involve large noise components

COMPERA 2.0. a refined four-stratum risk assessment model for pulmonary arterial hypertension

Background: Risk stratification plays an essential role in the management of patients with pulmonary arterial hypertension (PAH). The current European guidelines propose a 3-strata model to categorise risk as low, intermediate, or high, based on the expected 1-year mortality. However, with this model, most patients are categorised as intermediate risk. We investigated a modified approach based on 4 risk categories with intermediate risk subdivided into intermediate-low and intermediate-high risk. Methods: We analysed data from COMPERA, a European pulmonary hypertension registry, and calculated risk at diagnosis and first follow-up based on functional class (FC), 6 min walking distance (6 MWD) and serum levels of brain natriuretic peptide (BNP) or N-terminal fragment of pro-BNP (NT-proBNP), using refined cut-off values. Survival was assessed with Kaplan-Meier analyses, log-rank testing, and Cox proportional hazards models. Results: Data from 1,655 patients with PAH were analysed. Using the 3-strata model, most patients were classified as intermediate risk (76.0% at baseline and 63.9% at first follow-up). The refined 4-strata risk model yielded a more nuanced separation and predicted long-term survival, especially at follow-up assessment. Changes in risk from baseline to follow-up were observed in 31.1% of the patients with the 3-strata model and in 49.2% with the 4-strata model. These changes, including those between the intermediate-low and intermediate-high strata, were associated with changes in long-term mortality risk. Conclusions: Modified risk stratification using a 4-strata model based on refined cut-off levels for FC, 6MWD and BNP/NT-proBNP was more sensitive to prognostically relevant changes in risk than the original 3-strata model