BackgroundTrichosanthes lobata (family cucurbitaceae) is used to treat malarial fever and liver disorders. This study aims to investigate possible hepatoprotective activities of ethanolic extract of Trichosanthes lobata against paracetamol-induced hepatotoxicity.MethodsHepatotoxicity was induced in Wistar male rats by oral administration, 2 g/kg body weight on 7th day after the administration of ethanolic extract of Trichosanthes lobata and silymarin (100 mg/kg). Ethanolic extract of Trichosanthes lobata was administered orally at doses of 200 mg/kg and 400 mg/kg body weight daily for 7 days. Several serum markers, aspartate transaminase, alanine transaminase, alkaline phosphatase, bilirubin, total protein was measured to assess the effect of the extract on paracetamol (acetaminophen)-induced hepatic damage. The study included histopathological examination of liver sections.ResultsBlood samples from rats treated with ethanolic extract of Trichosanthes lobata (200 mg/kg body weight and 400 mg/kg body weight) had significant reductions in serum markers in paracetamol administered animals, indicating the effect of the extract in restoring the normal functional ability of hepatocytes. Silymarin (100 mg/kg, p.o.) was used as a reference drug.ConclusionThe ethanolic extract of Trichosanthes lobata exhibits protective effects against paracetamol‒induced hepatotoxicity.

Recently, different techniques have been applied to detect, predict, and reduce traffic congestion to improve the quality of transportation system services. Deep learning (DL) is becoming increasingly valuable for solving critiques. DL applications in transportation have been collected in several recently published surveys over the last few years. The existing research has discussed the cloud environment, which does not provide timely traffic forecasts, which is the cause of frequent traffic accidents. Thus, a solid understanding of the difficulties in predicting congestion is required because the transportation system varies widely between non-congested and congested states. This research develops a bi-directional recurrent neural network (BRNN) using Gated Recurrent Units (GRUs) to extract and classify traffic into congested and non-congested. This research uses a bidirectional recurrent neural network to simulate and forecast traffic congestion in smart cities (BRNN). Urban regions worldwide struggle with traffic congestion, and conventional traffic control techniques have failed miserably. This research suggests a data-driven approach employing BRNN for traffic management in smart cities, which uses real-time data from sensors and linked devices to control traffic more efficiently. The primary measures include predicting traffic metrics such as speed, weather, current, and accident probability. Congestion prediction performance has also been improved by extracting more features such as traffic, road, and weather conditions. The proposed model achieved better measures than the existing state-of-the-art methods. This research also explores an overview and analysis of several early initiatives that have shown promising results; moreover, it explores two potential future research approaches to increase the accuracy and efficiency of large-scale motion prediction.

A series of benztriazoles bearing Mannich bases (2 -10) were synthesized from benztriazole by aminomethylation with formaldehyde and various substituted secondary amines. Titled compounds were synthesized by Mannich reaction and they were characterized by IR and 1 HNMR spectroscopy. All these Mannich bases were screened for hepatoprotective activity on carbon tetrachloride induced liver damage in rats. Only compounds 4 (250 mg/kg) protected significantly the animals from carbon tetrachloride induced hepatotoxicity. Compound 4 N-morpholinyl methyl benztriazole exhibited significant activity comparable to that of standard drug silymarin.