Usporedba djelovanja blokatora kalcijevih kanala, blokatora autonomnoga živčanog sustava te inhibitora slobodnih radikala na hiposekreciju inzulin iz izolirnih langerhansovih otočića štakora uzrokovanu diazinonom

Hyperglycaemia has been observed with exposure to organophosphate insecticides. This study was designed to compare the effects of calcium channel blockers, alpha-adrenergic, beta-adrenergic, and muscarinic receptor blockers, and of free radical scavengers on insulin secretion from diazinon-treated islets of Langerhans isolated from the pancreas of rats using standard collagenase digestion, separation by centrifugation, and hand-picking technique. The islets were then cultured in an incubator at 37 °C and 5 % CO2. In each experimental set 1 mL of 8 mmol L-1 glucose plus 125 µg mL-1 or 625 µg mL-1 of diazinon were added, except for the control group, which received 8 mmol L-1 glucose alone. The cultures were then treated with one of the following: 30 µmol L-1 atropine, 100 µmol L-1 ACh + 10 µmol L-1 neostigmine, 0.1 µmol L-1 propranolol, 2 µmol L-1 nifedipine, 50 µmol L-1 phenoxybenzamine, or 10 µmol L-1 alphatocopherol. In all experiments, diazinon significantly reduced glucose-stimulated insulin secretion at both doses, showing no dose dependency, as the average inhibition for the lower dose was 62.20 % and for the higher dose 64.38 %. Acetylcholine and alpha-tocopherol restored, whereas atropine potentiated diazinoninduced hyposecretion of insulin. Alpha-, beta- and calcium channel blockers did not change diazinoninduced effects. These findings suggest that diazinon affects insulin secretion mainly by disturbing the balance between free radicals and antioxidants in the islets of Langerhans and by inducing toxic stress.U osoba izloženih organofosfatnim insekticidima zamijećen je nastanak hiperglikemije. Svrha je ovo istraživanja bila usporediti djelovanje blokatora kalcijevih kanala, alfa i beta-adrenergičkih i muskarinskih receptora te inhibicije slobodnih radikala na lučenje inzulina iz Langerhansovih otočića izoliranih iz štakora tretiranih diazinonom. Otočići su izolirani iz gušterače štakora s pomoću standardnog postupka digestije kolagenazom, odvajanja centrifugiranjem i metodom ručnog probira (engl. hand-picking) te su kultivirani u inkubatoru pri 37 °C i 5 % CO2. Pokusne su kulture inkubirane s 1 mL glukoze u koncentraciji od 8 mmol L-1 te diazinonom u dozi od 125 μg mL-1, odnosno 625 μg mL-1. U kontrolu je dodana samo glukoza u koncentraciji od 8 mmol L-1. Nakon toga je u kulture dodan jedan od sljedećih agenasa: 30 µmol L-1 atropin, 100 µmol L-1 ACh + 10 µmol L-1 neostigmin, 0,1 µmol L-1 propranolol, 2 µmol L-1 nifedipin, 50 µmol L-1 fenoksibenzamin, odnosno 10 µmol L-1 alfa-tokoferol. U svim je pokusima diazinon značajno smanjio lučenje inzulina, s time da je doza od 125 μg mL-1 dovela do 62,2 %-tne inhibicije, a doza od 625 μg mL-1 do 64,38 %-tne inhibicije lučenja inzulina, što upućuje na djelovanje neovisno o dozi. Acetilkolin i alfa-tokoferol su ponovno potaknuli lučenje inzulina, za razliku od atropina koji ga je dodatno smanjio. Primjena blokatora alfa i beta-adrenergičkih receptora te blokatora kalcijevih kanala nije utjecala na djelovanje diazinona. Autori zaključuju da diazinon utječe na lučenje inzulina ponajviše narušavanjem ravnoteže između slobodnih radikala i antioksidansa u Langerhansovim otočićima te dovodi do toksičnoga stresa

QANet: Tensor Decomposition Approach for Query-based Anomaly Detection in Heterogeneous Information Networks

IEEE Complex networks have now become integral parts of modern information infrastructures. This paper proposes a user-centric method for detecting anomalies in heterogeneous information networks, in which nodes and/or edges might be from different types. In the proposed anomaly detection method, users interact directly with the system and anomalous entities can be detected through queries. Our approach is based on tensor decomposition and clustering methods. We also propose a network generation model to construct synthetic heterogeneous information network to test the performance of the proposed method. The proposed anomaly detection method is compared with state-of-the-art methods in both synthetic and real-world networks. Experimental results show that the proposed tensor-based method considerably outperforms the existing anomaly detection methods

Link prediction in multiplex networks based on interlayer similarity

Some networked systems can be better modeled by multilayer structure where the individual nodes develop relationships in multiple layers. Multilayer networks with similar nodes across layers are also known as multiplex networks. This manuscript proposes a novel framework for predicting forthcoming or missing links in multiplex networks. The link prediction problem in multiplex networks is how to predict links in one of the layers, taking into account the structural information of other layers. The proposed link prediction framework is based on interlayer similarity and proximity-based features extracted from the layer for which the link prediction is considered. To this end, commonly used proximity-based features such as AdamicAdar and Jaccard Coefficient are considered. These features that have been originally proposed to predict missing links in monolayer networks, do not require learning, and thus are simple to compute. The proposed method introduces a systematic approach to take into account interlayer similarity for the link prediction purpose. Experimental results on both synthetic and real multiplex networks reveal the effectiveness of the proposed method and show its superior performance than state-of-the-art algorithms proposed for the link prediction problem in multiplex networks