• Journal of Dental Anesthesia and Pain Medicine
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• v.21 no.4
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• pp.283-309
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• 2021

Achieving profound anesthesia in mandibular molars with irreversible pulpitis is a tedious task. This review aimed at evaluating the success of buccal/lingual infiltrations administered with a primary inferior alveolar nerve block (IANB) injection or as a supplemental injection after the failure of the primary injection in symptomatic and asymptomatic patients with irreversible pulpitis in human mandibular molars. The review question was "What will be the success of primary and supplemental infiltration injection in the endodontic treatment of patients with irreversible pulpitis in human mandibular molars?" We searched electronic databases, including Pubmed, Scopus, and Ebsco host and we did a comprehensive manual search. The review protocol was framed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) checklist. We included clinical studies that evaluated and compared the anesthetic outcomes of primary IANB with primary and/or supplementary infiltration injections. Standard evaluation of the included studies was performed and suitable data and inferences were assessed. Twenty-six studies were included, of which 13 were selected for the meta-analysis. In the forest plot representation of the studies evaluating infiltrations, the combined risk ratio (RR) was 1.88 (95% CI: 1.49, 2.37), in favor of the secondary infiltrations with a statistical heterogeneity of 77%. The forest plot analysis for studies comparing primary IANB + infiltration versus primary IANB alone showed a low heterogeneity (0%). The included studies had similar RRs and the combined RR was 1.84 (95% CI: 1.44, 2.34). These findings suggest that supplemental infiltrations given along with a primary IANB provide a better success rate. L'Abbe plots were generated to measure the statistical heterogeneity among the studies. Trial sequential analysis suggested that the number of patients included in the analysis was adequate. Based on the qualitative and quantitative analyses, we concluded that the infiltration technique, either as a primary injection or as a supplementary injection, given after the failure of primary IANB, increases the overall anesthetic efficacy.

• Journal of Korea Water Resources Association
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• v.56 no.4
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• pp.235-243
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• 2023

Due to the revision of the River Act and the enactment of the Act on the Investigation, Planning, and Management of Water Resources, a regular bed change survey has become mandatory and a system is being prepared such that local governments can manage water resources in a planned manner. Since the topography of a bed cannot be measured directly, it is indirectly measured via contact-type depth measurements such as level survey or using an echo sounder, which features a low spatial resolution and does not allow continuous surveying owing to constraints in data acquisition. Therefore, a depth measurement method using remote sensing-LiDAR or hyperspectral imaging-has recently been developed, which allows a wider area survey than the contact-type method as it acquires hyperspectral images from a lightweight hyperspectral sensor mounted on a frequently operating drone and by applying the optimal bandwidth ratio search algorithm to estimate the depth. In the existing hyperspectral remote sensing technique, specific physical quantities are analyzed after matching the hyperspectral image acquired by the drone's path to the image of a surface unit. Previous studies focus primarily on the application of this technology to measure the bathymetry of sandy rivers, whereas bed materials are rarely evaluated. In this study, the existing hyperspectral image-based water depth estimation technique is applied to rivers with vegetation, whereas spatio-temporal hyperspectral imaging and cross-sectional hyperspectral imaging are performed for two cases in the same area before and after vegetation is removed. The result shows that the water depth estimation in the absence of vegetation is more accurate, and in the presence of vegetation, the water depth is estimated by recognizing the height of vegetation as the bottom. In addition, highly accurate water depth estimation is achieved not only in conventional cross-sectional hyperspectral imaging, but also in spatio-temporal hyperspectral imaging. As such, the possibility of monitoring bed fluctuations (water depth fluctuation) using spatio-temporal hyperspectral imaging is confirmed.