Visual classification of pressure injury stages for nurses: A deep learning model applying modern convolutional neural networks.

AIMS: To develop a deep learning model for pressure injury stages classification based on real-world photographs and compare its performance with that of clinical nurses to seek the opportunity of its application in clinical settings. DESIGN: This was a retrospective observational study using a deep learning model. REVIEW METHODS: A plastic surgeon and two wound care nurses labelled a set of pressure injury images. We applied several modern Convolutional Neural Networks architectures and compared the performances with those of clinical nurses. DATA SOURCES: We retrospectively analysed the electronic medical records of hospitalized patients between January 2019 and April 2021. RESULTS: A set of 2464 pressure injury images were compiled and analysed. Using EfficientNet, in classifying pressure injury images, the macro F1-score was calculated to be 0.8941, and the average performance of two experienced nurses was reported as 0.8781. CONCLUSION: A deep learning model for classifying pressure injury images by stages was successfully developed, and the performance of the model was compared with that of experienced nurses. The classification model developed in this study is expected to help less-experienced nurses or those working in under-resourced healthcare settings determine the stages of pressure injury. IMPACT: Our deep learning model can minimize discrepancies in nurses' assessment of classifying pressure injury stages. Follow-up studies on improving the performance of deep learning models using modern techniques and clinical usability will lead to improved quality of care among patients with pressure injury. NO PATIENT OR PUBLIC CONTRIBUTION: Patients or the public were not involved in our research's design, conduct, reporting or dissemination plans because this was a retrospective study that used electronic medical records.

The complete mitochondrial genome of Ricania shantungensis (Hemiptera: Ricaniidae) in Korea.

In this study, we have sequenced and annotated the complete mitochondrial genome of Ricania shantungensis (Hemiptera: Ricaniidae) for the first time. The circular mitogenome of R. shantungensis was 15,789 bp, including 13 protein-coding genes, two ribosomal RNA genes, 22 transfer RNAs, and a single control region of 1,363 bp. Its AT ratio was 74.6%. According to the phylogenetic tree, R. shantungensis was clustered with the genus Ricania.

Characteristics of Microbial Communities of Pachygrontha antennata (Hemiptera: Pachygronthidae) in Relation to Habitat Variables.

The microbial community interacts with the environment and the health and immune function of its host both directly and indirectly. However, very few studies about microbial communities have considered habitat and external environmental variables. This study examined environmental influences on the microbial community of Pachygrontha antennata, which is found in various habitats (e.g., urban, forested, and agricultural areas). The results demonstrated that the composition of the microbial community differed according to land use, while the bacterial diversity did not. In urban areas with high environmental heterogeneity, microbial community diversity tended to be high. Furthermore, bacteria in forests and agricultural areas (e.g., Paraburkholderia, Burkholderia) have been found to be highly correlated with habitat variables. Therefore, we suggest that habitat variables should be considered in future symbiotic studies.