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Both phytic acid (PA) and catechin (CA) are well known antioxidants of natural origin. They were frequently tried on experimental level as hepatoprotectants, relying only on their antioxidant properties. The present study was conducted mainly to outline the other biochemical pathways underlying the hepatotherapeutic potential of both drugs and to check a possible synergistic action if prescribed concomitantly. As both materials are frequently taken on a daily basis in food and drinks, it will be helpful to pursue their possible utility and/or to check if their value is really of medical importance. For this purpose, CCl4 was used as a hepatotoxin, we evaluated plasma total sialic acid (TSA), serum ascorbic acid (AA) levels, liver tissue thiobarbituric acid reactive species (TBARS) as a marker for lipid peroxidation and total protein (TP) content as a rough marker to measure hepatic synthetic capability in 80 male Wistar rats as experimental models. Animals were classified into 8 groups (10 rats each), the first as control, the second as PA treated (0.3 mg kg -1), orally, the third as CA treated (30 mg kg -1), intraperitoneally, the fourth given both drugs, as a single daily dose for 2 weeks. The same design was repeated 24 hours after CCl4-intoxication (1mL kg -1), intraperitoneally, as a single dose.The results revealed that both PA and CA when used individually, significantly down-regulated TSA in both physiologic (no CCl4 treatment) and pathologic (CCl4-intoxication) states accompanied by significant decrease in lipoperoxidation. The therapeutic action against TSA and the antioxidant power were abolished by co-administration of both drugs. AA was only decreased by PA and the combination in the physiologic state. Both PA and CA showed significant therapeutic effect for protein synthesis against CCl4-intoxication, but the combination abolished this effect. We conclude that both drugs can be considered as a chemotherapeutic against hepatopathies and we for the first time contraindicate the concomitant use of both drugs.

Air pollution in industrial environments, particularly in the chrome plating process, poses significant health risks to workers due to high concentrations of hazardous pollutants. Exposure to substances like hexavalent chromium, volatile organic compounds (VOCs), and particulate matter can lead to severe health issues, including respiratory problems and lung cancer. Continuous monitoring and timely intervention are crucial to mitigate these risks. Traditional air quality monitoring methods often lack real-time data analysis and predictive capabilities, limiting their effectiveness in addressing pollution hazards proactively. This paper introduces a real-time air pollution monitoring and forecasting system specifically designed for the chrome plating industry. The system, supported by Internet of Things (IoT) sensors and AI approaches, detects a wide range of air pollutants, including NH3, CO, NO2, CH4, CO2, SO2, O3, PM2.5, and PM10, and provides real-time data on pollutant concentration levels. Data collected by the sensors are processed using LSTM, Random Forest, and Linear Regression models to predict pollution levels. The LSTM model achieved a coefficient of variation (R²) of 99 % and a mean absolute percentage error (MAE) of 0.33 for temperature and humidity forecasting. For PM2.5, the Random Forest model outperformed others, achieving an R² of 84 % and an MAE of 10.11. The system activates factory exhaust fans to circulate air when high pollution levels are predicted to occur in the next hours, allowing for proactive measures to improve air quality before issues arise. This innovative approach demonstrates significant advancements in industrial environmental monitoring, enabling dynamic responses to pollution and improving air quality in industrial settings.

Illegal logging poses a significant threat to environmental conservation, contributing to deforestation, ecosystem disruption, and economic losses. Traditional detection methods, such as ground patrols and satellite imagery, are often ineffective, expensive, and time-consuming. This paper introduces a novel Unmanned Aerial Vehicles (UAV)-based IoT system for chainsaw noise detection and prevention. The system uses low-cost IoT nodes with microphones and microcontrollers to detect chainsaw sounds via a Convolutional Neural Network (CNN) model. These nodes reduce power consumption, allowing the battery to last at least 8 years. Detected chainsaw sounds are transmitted to a LoRaWAN gateway and forwarded to a cloud server. The cloud server processes the data and dispatches a drone equipped with a camera to the site. The drone streams live video to the cloud, where AI algorithms analyze the footage for human presence. If detected, the drone activates sound and light alarms to deter illegal logging. Experimental results show that IoT nodes can detect chainsaw sounds up to 100 m, with the CNN model achieving an accuracy of 99.37 % and a loss of 0.0185. Comparisons with Logistic Regression, Decision Tree, Random Forest, and SVM demonstrated the superior performance of CNN. This UAV-IoT system offers a fast, reliable, scalable, and real-time solution for detecting and preventing illegal logging. Leveraging advanced AI and IoT technologies, this system enhances environmental monitoring and conservation efforts. It represents a significant step forward in the fight against illegal logging, providing a practical, effective, and sustainable solution.