PHYTOCHEMICAL PROFILING, FREE-RADICAL SCAVENGING AND ANTI-INFLAMMATORY ACTIVITIES OF MELIOSMA SIMPLICIFOLIA (L.)

Objective: In the current research, to determine the stem extract of Meliosma simplicifolia (L.) for total phenol, tannin, total flavonoid, antioxidant activity, anti-inflammatory and identify the phytoconstituents utilizing GC-MS and FT-IR. Methods: The ability of the plant extract to act as hydrogen/electrons donor or scavenger of radicals was determined by in vitro antioxidant assays using 2,2-diphenyl-2-picryl-hydrazyl free radical (DPPH) scavenging, reducing power assay, superoxide radical (O2•) scavenging activity. The anti-inflammatory activity was evaluated using carrageenan and formalin-induced paw edema models using Wistar albino rats. The GC-MS and FT-IR analysis of the methanolic stem extract of M. simplicifolia was revealed the presence of phytochemicals. Results: Quantitative studies of estimated phenol, flavonoid and tannin, as for the methanol extract of stem showed the highest content of phenolic compounds (39.83±3.62GAE mg/100). Antioxidant activities were concluded the estimation M. simplicifolia stem for as followed the studies. In stem the methanol extract showed the highest DPPH scavenging activity (124.3µg/ml). The anti-inflammatory activity has shown in high doses of methanolic extract 250 mg/kg of significant value (p<0.05) inhibition of paw edema, on 6th hour, respectively. The FT-IR analysis has confirmed their characteristic peak values and functional groups. Conclusion: M. simplicifolia has an effective of anti-inflammatory activity and constitutes a potential source for the development of new treatments

Systemic ablation of Camkk2 impairs metastatic colonization and improves insulin sensitivity in TRAMP mice : Evidence for cancer cell-extrinsic CAMKK2 functions in prostate cancer

Despite early studies linking calcium-calmodulin protein kinase kinase 2 (CAMKK2) to prostate cancer cell migration and invasion, the role of CAMKK2 in metastasis in vivo remains unclear. Moreover, while CAMKK2 is known to regulate systemic metabolism, whether CAMKK2’s effects on whole-body metabolism would impact prostate cancer progression and/or related comorbidities is not known. Here, we demonstrate that germline ablation of Camkk2 slows, but does not stop, primary prostate tumorigenesis in the TRansgenic Adenocarcinoma Mouse Prostate (TRAMP) genetic mouse model. Consistent with prior epidemiological reports supporting a link between obesity and prostate cancer aggressiveness, TRAMP mice fed a high-fat diet exhibited a pronounced increase in the colonization of lung metastases. We demonstrated that this effect on the metastatic spread was dependent on CAMKK2. Notably, diet-induced lung metastases exhibited a highly aggressive neuroendocrine phenotype. Concurrently, Camkk2 deletion improved insulin sensitivity in the same mice. Histological analyses revealed that cancer cells were smaller in the TRAMP;Camkk2−/− mice compared to TRAMP;Camkk2+/+ controls. Given the differences in circulating insulin levels, a known regulator of cell growth, we hypothesized that systemic CAMKK2 could promote prostate cancer cell growth and disease progression in part through cancer cell-extrinsic mechanisms. Accordingly, host deletion of Camkk2 impaired the growth of syngeneic murine prostate tumors in vivo, confirming nonautonomous roles for CAMKK2 in prostate cancer. Cancer cell size and mTOR signaling was diminished in tumors propagated in Camkk2-null mice. Together, these data indicate that, in addition to cancer cell-intrinsic roles, CAMKK2 mediates prostate cancer progression via tumor-extrinsic mechanisms. Further, we propose that CAMKK2 inhibition may also help combat common metabolic comorbidities in men with advanced prostate cancer

Quantum-enhanced multiobjective large-scale optimization via parallelism

Traditional quantum-based evolutionary algorithms are intended to solve single-objective optimization problems or multiobjective small-scale optimization problems. However, multiobjective large-scale optimization problems are continuously emerging in the big-data era. Therefore, the research in this paper, which focuses on combining quantum mechanics with multiobjective large-scale optimization algorithms, will be beneficial to the study of quantum-based evolutionary algorithms. In traditional quantum-behaved particle swarm optimization (QPSO), particle position uncertainty prevents the algorithm from easily falling into a local optimum. Inspired by the uncertainty principle of position, the authors propose quantum-enhanced multiobjective large-scale algorithms, which are parallel multiobjective large-scale evolutionary algorithms (PMLEAs). Specifically, PMLEA-QDE, PMLEA-QjDE and PMLEA-QJADE are proposed by introducing the search mechanism of the individual particle from QPSO into differential evolution (DE), differential evolution with self-adapting control parameters (jDE) and adaptive differential evolution with optional external archive (JADE). Moreover, the proposed algorithms are implemented with parallelism to improve the optimization efficiency. Verifications performed on several test suites indicate that the proposed quantum-enhanced algorithms are superior to the state-of-the-art algorithms in terms of both effectiveness and efficiency