METHODS: This was a prospective non-randomized comparative study. Eyes with OAG and cataracts that were planned for either combined phacoemulsification and iStent implantation (iStent+CS) or phacoemulsification alone (CS) were recruited. The iStent inject (Model G2-M-IS) or iStent injectW (Model G2-W) trabecular micro-bypass stent (Glaukos Corporation, San Clemente, CA, USA) was implanted in the iStent+CS group. WDT was performed before and 3 months after surgery. WDT-IOP parameters including peak IOP, IOP fluctuation, and area under the curve (AUC) were compared between the two groups.
RESULTS: There were 20 eyes in the iStent+CS group and 16 eyes in the CS group. Both groups had similar pre-operative baseline IOP (15.6 ± 3.7 mm Hg vs. 15.8 ± 1.8 mm Hg in the iStent+CS and CS group, respectively, p = 0.883). The iStent+CS group experienced greater numerical reduction in peak IOP (2.6 ± 1.9 mm Hg vs. 1.9 ± 2.4 mm Hg; p = 0.355), IOP fluctuation (1.7 ± 2.2 mm Hg vs. 0.8 ± 2.5 mm Hg; p = 0.289), and AUC (54.8 ± 103.6 mm Hg × minute vs. 25.3 ± 79.0 mm Hg × minute; p = 0.355) than the CS group. There was more reduction in the number of anti-glaucoma medications in the iStent+CS group (1.4 ± 1.2) than the CS group (0.3 ± 0.9; p = 0.005).
CONCLUSION: Both combined phacoemulsification with iStent inject implantation and phacoemulsification alone reduced peak IOP, IOP fluctuation, and AUC, and none of these parameters showed statistically significant difference. Greater reduction in anti-glaucoma medications was seen in the combined group.
OBJECTIVES: By leveraging the power of advanced machine learning schemes and experimental approaches, this research aims to provide valuable insights into CO2 flux prediction in coal fire areas and inform environmental monitoring and management strategies.
METHODS: The study involves the collection of an experimental dataset specific to underground coal fire areas, encompassing various parameters related to CO2 flux and underground coal fire characteristics. Innovative feature engineering techniques are applied to capture the unique characteristics of underground coal fire areas and their impact on CO2 flux. Different machine learning algorithms, including Natural gradient boosting regression (NGRB), Extreme gradient boosting (XGboost), Light gradient boosting (LGRB), and random forest (RF), are evaluated and compared for their predictive capabilities. The models are trained, optimized, and assessed using appropriate performance metrics.
RESULTS: The NGRB model yields the best predictive performances with R2 of 0.967 and MAE of 0.234. The novel contributions of this study include the development of accurate prediction models tailored to underground coal fire areas, shedding light on the underlying factors driving CO2 flux. The findings have practical implications for delineating the spontaneous combustion zone and mitigating CO2 emissions from underground coal fires, contributing to global efforts in combating climate change.
CONTENT: A scoping review examined the impact of heat and existing mitigation and adaptation responses for vulnerable populations in temperate regions, with a focus on A|NZ. Additionally, temperature trend analysis was conducted for current and projected trends using Climate CHIP for six major heat-affected cities in A|NZ to assess the recognition of heat as a societal concern.
SUMMARY AND OUTLOOK: The review identified mitigation and adaptation strategies for existing vulnerable groups and discovered other potential vulnerable groups in A|NZ, including Indigenous people (Māori), Pacific communities, low-income groups, migrants, and visitors. Temperature trends show an increasing pattern, suggesting heightened future heat-related impacts on these populations. This review reveals A|NZ's growing vulnerability to rising temperatures, particularly among high-risk groups, and calls for stronger mitigation and adaptation strategies to address future heat-health risks.