Technologies-enhanced Anatomical Study in Undergraduate Medical Students

Anatomy is an essential pre-clinical subject of undergraduate medical education. The traditional practical studies on cadavers are widely used in medical schools. It enhances active and deep learning, preparing students for clinical practice. However, the high costs, the time-consuming, and the health problems, which the staff exposed to chemical hazards are considered. Computer-based learning (CBL) technologies can increase the efficiency of students in understanding anatomy. This review provides an overview of CBL technologies such as Anatomage table 7.0, zSpace, Complete Anatomy app, and 4D Interactive Anatomy that prepare Ramathibodi (RA) medical students for enhanced anatomical understanding and self-learning. The integration methods between traditional cadaveric dissection and the CBL in the curriculum can enhance the classroom experience, student engagement, learning, retention, and improvement of RA medical student's knowledge in anatomy.

Nitrate Is Nitrate: The Status Quo of Using Nitrate through Vegetable Extracts in Meat Products

Nitrate and nitrites are used to give the characteristic color to cured meat products and to preserve them. According to the scientific knowledge available at the moment, these compounds are approved as food additives based on a detailed ponderation between the potential risks and benefits. The controversy over nitrites has increased with the release of an IARC Monograph suggesting an association between colorectal cancer and dietary nitrite in processed meats. The trend in “clean label” products reinforced the concern of consumers about nitrates and nitrites in meat products. This review aims to explain the role of nitrates and nitrites used in meat products. The potential chemical hazards and health risks linked to the consumption of cured meat products are described. Different strategies aiming to replace synthetic nitrate and nitrite and obtain green-label meat products are summarized, discussing their impact on various potential hazards. In the light of the present knowledge, the use or not of nitrite is highly dependent on the ponderation of two main risks—the eventual formation of nitrosamines or the eventual out-growth of severe pathogens. It is evident that synthetic nitrite and nitrate alternatives must be researched, but always considering the equilibrium that is the safety of a meat product.

Effectiveness of a Participatory Program on Pesticide Use Behavior and Blood Cholinesterase Levels in Chiang Mai Province, Northern Thailand

This quasiexperimental study was designed to determine the effectiveness of a participatory program on pesticide use behavior and blood cholinesterase levels. The participants were 18–60 years old, living in Thung Satok subdistrict, San Pa Tong District, Chiang Mai Province. Eighty subjects, including 32 farmers and 48 consumers, were recruited to participate in the study voluntarily by a purposive sampling technique. Data concerning each individual’s behaviors were collected using questionnaires, and blood cholinesterase levels were obtained from whole blood finger, providing whole blood pre and postexperiment. The data were analyzed using Fishers’ exact and paired t-tests, for the problem situations were independently analyzed. The results show that the participatory process made it possible to improve methods for the prevention of the unhealthy effects of pesticides. After participating in the activities, both groups showed significantly higher scores in before and after knowledge ( P < 0.05 ) and a decrease in pesticide contamination in their bodies as a result of the measurement of average cholinesterase which increased significantly ( P < 0.001 ). In conclusion, the participatory program was applied to solve health problems from chemical hazards. The program can raise awareness about chemical toxicity, leading to the modification of the related behavior toward chemicals and reduction of the contamination of chemicals in the body. Therefore, the adoption of participatory processes can effectively solve problems related to chemical hazards that affect health.

Global event-based surveillance of chemical incidents

Background The number of chemicals in our society and in our daily lives continues to increase. Accompanying this is an increasing risk of human exposure to and injury from hazardous substances. Performing regular, structured surveillance of chemical incidents allows a greater awareness of the types of chemical hazards causing injury and the frequency of their occurrence, as well as providing a better understanding of exposures. Objective The objective of performing event-based surveillance (EBS) and capturing chemical incidents is to use this information to increase the situational awareness of chemical incidents, improve the management of these incidents and to inform measures to protect public health. Methods This paper describes a method for EBS for chemical incidents, including the sources used, storing the gathered information and subsequent analysis of potential trends in the data. Results We describe trends in the type of incidents that have been detected, the chemicals involved in these incidents and the health effects caused, in different geographic regions of the world. Significance The methodology presented here provides a rapid and simple means of identifying chemical incidents that can be set up rapidly and with minimal cost, the outputs of which can be used to identify emerging risks and inform preparedness planning, response and training for chemical incidents.

The EU-SENSE System for Chemical Hazards Detection, Identification, and Monitoring

Chemical reconnaissance, defined as hazards detection, identification, and monitoring, requires tools and solutions which provide reliable and precise data. In this field, the advances of artificial intelligence can be applied. This article aims to propose a novel approach for developing a chemical reconnaissance system that relies on machine learning, modelling algorithms, as well as the contaminant dispersion model to combine signals from different sensors and reduce false alarm rates. A case study of the European Union Horizon 2020 project–EU-SENSE is used and the main features of the system are analysed: heterogeneous sensor nodes components, chemical agents to be detected, and system architecture design. Through the proposed approach, chemical reconnaissance capabilities are improved, resulting in more effective crisis management. The idea for the system design can be used and developed in other areas, namely, in biological or radiological threat reconnaissance.

Analisa Potensi Bahaya dan Upaya Pencegahan Kecelakaan Kerja Dengan Job Safety Analisys (JSA) Pada Divisi Pencucian di PT “X”

The work process in the washing division of the "X" Company consists of three kinds of processes: washing, squeezing and drying. All of these work processes involve interactions between workers, raw materials, equipment and the environment. These interactions can create hazards and risks to both health and accident risks if they are not carried out according to the correct procedures and steps. The purpose of this study is to determine the potential hazards and risk rating in order to provide recommendations for their control in the washing division of the "X" Company. The research method is observational, which is only observing objects without controlling any variables. The steps taken are to identify and analyze the existing hazards and the risks that may occur so that analysis of potential hazards, types of accidents that may occur and recommendations for their control is needed. The research results show that the potential hazards in the washing division are 52, consisting of 46 ergonomic hazards and 6 chemical hazards. On this basis, recommendations for control have been prepared to prevent accidents that must be carried out.

BIKE: Dietary Exposure Model for Foodborne Microbiological and Chemical Hazards

BIKE is a Bayesian dietary exposure assessment model for microbiological and chemical hazards. A graphical user interface was developed for running the model and inspecting the results. It is based on connected Bayesian hierarchical models, utilizing OpenBUGS and R in tandem. According to occurrence and consumption data given as inputs, a specific BUGS code is automatically written for running the Bayesian model in the background. The user interface is based on shiny app. Chronic and acute exposures are estimated for chemical and microbiological hazards, respectively. Uncertainty and variability in exposures are visualized, and a few optional model structures can be used. Simulated synthetic data are provided with BIKE for an example, resembling real occurrence and consumption data. BIKE is open source and available from github.