RESUMO
Greenhouse gases (GHGs) are the primary drivers of global climate change. Human activities, particularly those related to energy production, transportation, and industry, have long contributed to the escalating levels of GHGs in the Earth's atmosphere. Recognizing the significance of this issue, universities, including Thammasat University, play a vital role in Greenhouse gas (GHG) emissions research and education, carrying a responsibility to address the matter. This study is aimed aims to assess the greenhouse gas emissions and mitigation measures at Thammasat University (Lampang campus), Thailand. The emissions are categorized into 3 types: (1) direct GHG emissions; (2) energy-related indirect GHG emissions; and (3) other indirect GHG emissions. Activity data from the years 2019 to 2022 was used for the calculations, resulting in GHG emissions of 1051.70, 778.28, 558.64, and 1034.531 tons of carbon dioxide equivalent. Among these emissions, energy-related indirect GHG emissions from electricity purchases represent the majority, accounting for approximately 78.55% of the total emissions. Consequently, implementing mitigation strategies, such as solar panel installations and solid waste reduction (combined scenario), has the potential to reduce GHG emissions by up to 57.78%. Furthermore, the university should actively promote GHG emissions reduction through the enactment of energy-saving policies and the adoption of energy-efficient technologies to reduce reliance on energy purchases.
RESUMO
BACKGROUND: Leptospirosis is an important health problem in Thailand. People infected with leptospirosis may not have any mild symptoms, whereas some people have acute and severe illnesses. It is crucial to strengthen critical patients' diagnosis and treatment to prevent severe complications and reduce mortality. This study was performed to explore a set of parameters for the prediction of severe leptospirosis illness under routine clinical practice. METHODS: A case-control study was conducted in eight general hospitals in Thailand. Retrospective collection data were used, and key information was retrieved from inpatient medical files. Patients were grouped into two severity categories, severe and non-severe infection. Backward elimination was used to reach the final multivariate model. RESULTS: The six significant predictors identified in the study were hemoptysis (OR = 25.80, 95% CI 5.69, 116.92), hypotension (blood pressure < 90/60 mmHg) (OR = 17.33, 95% CI 6.89, 43.58), platelet count < 100,000/µL (OR = 8.37, 95% CI 4.65, 15.09), white blood cell count (WBC) > 14,000/µL (OR = 5.12, 95% CI 2.75, 9.51), hematocrit ≤ 30% (OR = 3.49, 95% CI 1.61, 7.57), and jaundice (OR = 3.11, 95% CI 1.71, 5.65). These predictors could correctly predict the severity of leptospirosis infection in 91.31% of the area under the receiver operation curve (AuROC). CONCLUSIONS: The results of this study showed that severe leptospirosis infections have identifiable predictors. The predictors may be used to develop a scoring system for predicting the level of severity.