Surgical productivity recovery after the COVID-19 pandemic in Japan

IntroductionPrevious studies demonstrated that the surgical productivity regressed in 2020. This study therefore explored whether the COVID-19 pandemic had any significant lasting effect of reducing the surgical productivity in Japan. This is a retrospective observational study which is an extension of the previous ones.MethodsThe authors analyzed 18,805 surgical procedures performed during the study period from April 1 through September 30 in 2016–22. A non-radial and non-oriented Malmquist model under the variable returns-to-scale assumptions was employed. The decision-making unit (DMU) was defined as a surgical specialty department. Inputs were defined as (1) the number of assistants, and (2) the surgical duration. The output was defined as the surgical fee. The study period was divided into 42 one-month periods. The authors added all the inputs and outputs for each DMU during these study periods, and computed its Malmquist index, efficiency change and technical change. The outcome measures were its annual productivity, efficiency, and technical changes between the same months in each year.ResultsThere was no statistically significant difference in annual productivity, efficiency, and technical changes between pre-pandemic and post-pandemic periods.DiscussionNo evidence was found to suggest that the COVID-19 pandemic has any significant lasting effect of reducing the surgical productivity

Integration of Multi-Sensor Data to Estimate Plot-Level Stem Volume Using Machine Learning Algorithms–Case Study of Evergreen Conifer Planted Forests in Japan

The development of new methods for estimating precise forest structure parameters is essential for the quantitative evaluation of forest resources. Conventional use of satellite image data, increasing use of terrestrial laser scanning (TLS), and emerging trends in the use of unmanned aerial systems (UASs) highlight the importance of modern technologies in the realm of forest observation. Each technology has different advantages, and this work seeks to incorporate multiple satellite, TLS- and UAS-based remote sensing data sets to improve the ability to estimate forest structure parameters. In this paper, two regression analysis approaches are considered for the estimation: random forest regression (RFR) and support vector regression (SVR). To collect the dependent variable, in situ measurements of individual tree parameters (tree height and diameter at breast height (DBH)) were taken in a Japanese cypress forest using the nondestructive TLS method, which scans the forest to obtain dense and accurate point clouds under the tree canopy. Based on the TLS data, the stem volume was then computed and treated as ground truth information. Topographic and UAS information was then used to calculate various remotely sensed explanatory variables, such as canopy size, canopy cover, and tree height. Canopy cover and canopy shapes were computed via the orthoimages derived from the UAS and watershed segmentation method, respectively. Tree height was computed by combining the digital surface model (DSM) from the UAS and the digital terrain model (DTM) from the TLS data. Topographic variables were computed from the DTM. The backscattering intensity in the satellite imagery was obtained based on L-band (Advanced Land Observing Satellite-2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2)) and C-band (Sentinel-1) synthetic aperture radar (SAR). All satellite (10–25 m resolution), TLS (3.4 mm resolution) and UAS (2.3–4.6 cm resolution) data were then combined, and RFR and SVR were trained; the resulting predictive powers were then compared. The RFR method yielded fitting R2 up to 0.665 and RMSE up to 66.87 m3/ha (rRMSE = 11.95%) depending on the input variables (best result with canopy height, canopy size, canopy cover, and Sentinel-1 data), and the SVR method showed fitting R2 up to 0.519 and RMSE up to 80.12 m3/ha (rRMSE = 12.67%). The RFR outperformed the SVR method, which could delineate the relationship between the variables for better model accuracy. This work has demonstrated that incorporating various remote sensing data to satellite data, especially adding finer resolution data, can provide good estimates of forest parameters at a plot level (10 by 10 m), potentially allowing advancements in precision forestry

Cell-type-specific Augmentation of the Tumoricidal Activity of Polymeric Adriamycin Combined with Galactosamine

The optimization of drug delivery system with approaches to a target in structure has been implicated to play a role in cancer chemotherapy, because it can reduce the adverse effects. However, this system partly reduces the direct cytotoxicity of anticancer drugs against tumor cells, in comparison to its free form. In the present study, poly(ﾎｱ- malic acid) adriamycin (poly ADR) coated with saccharides including galactosamine which recognizes galactose-lectin specific to hepatocytes was prepared, and its cytotoxicities against Hep G2 cells (human hepatoblastoma), AZ521 cells (human gastric cancer) and KNS cells (human lung cancer) was evaluated using an in vitro cytotoxicity assay. In both AZ521 cells and KNS cells, poly ADR as well as poly ADR coated with glucosamine, glactosamine or mannosamine provided relatively lower cytotoxicities than the free form of ADR. In contrast, Hep G2 cells were to more efficiently sensitized, compared with the free form of ADR or poly ADR combined with or without glucosamine or mannosamine (P<0.01, respectively). These results indicate that poly ADR coated with galactosamine used as a cell recognition element is thus applicable for targeting cancer chemotherapy in the treatment of hepatocellular carcinoma

Trials for Risk Assessment of Japanese Encephalitis Based on Serologic Surveys of Wild Animals

Flavivirus Encephaliti