Use of non-prescription antibiotics can portend danger and predispose the populace to changes in bacterial resistance pattern. The aims of this study were to (a) evaluate the knowledge and attitudes of residents of Al-Ahsa community, Saudi Arabia on the use of non-prescribed antibiotics. (b) To identify possible predictors (if any) for self-medication within the community. A cross-sectional survey study, using self-administered questionnaire was conducted in two sections; demographics and self-medication attitude (in form of self-antibiotic use). Questions contained the following outcomes; for demographics; gender, age, education level and common disease within the community. Whereas the second part evaluated sources of information, knowledge of antibiotics, frequency/duration of use, underlined illness in which drug use was employed, names of antibiotics used and awareness of adverse effects of antibiotics. Results revealed that the adult population in the 18-40 year age range constituted about 82.5% of the respondents. Also 18-29 age group made of 60.5% of the respondents and that 56.8% the respondents are university graduates. Cold (18.8%) and sore throat (13.0%) were the diseases commonly found among the community that drove them to using non-prescribed antibiotics. About 337 (72.8%) of the respondent mention the use of antibiotics to treat the illness, and 21 (4.5%) were aiming to prevent the illness. While, 19.4% of the respondents admitted to taking non-prescribed antibiotics for both prevention and treatment of illness. 43.6% of the respondents disclosed that they are not aware of the dangers of using non-prescribed antibiotics. In conclusion the use of non-prescribed antibiotics in this community is evident, as a significant number use them from previous experience for prevention and treatment of illness. Therefore introduction of rational use of drugs will help in limiting the attendant development of bacterial resistance.
Water is the most necessary and significant element for all life on earth. Unfortunately, the quality of the water resources is constantly declining as a result of population development, industry, and civilization progress. Due to their extreme toxicity, heavy metals removal from water has drawn researchers' attention. A lot of scientific applications use artificial neural networks (ANNs) because of their excellent ability to map nonlinear relationships. ANNs shown excellent modelling capabilities for the water treatment remediation. The adsorption process uses a variety of variables, making the interaction between them nonlinear. Selecting the best technique can produce excellent results; the adsorption approach for removing heavy metals is highly effective. Different studies show that the ANNs modelling approach can accurately forecast the adsorbed heavy metals and other contaminants in order to remove them.