Identification of risk factors for the onset of delirium associated with COVID-19 by mining nursing records.

COVID-19 has a range of complications, from no symptoms to severe pneumonia. It can also affect multiple organs including the nervous system. COVID-19 affects the brain, leading to neurological symptoms such as delirium. Delirium, a sudden change in consciousness, can increase the risk of death and prolong the hospital stay. However, research on delirium prediction in patients with COVID-19 is insufficient. This study aimed to identify new risk factors that could predict the onset of delirium in patients with COVID-19 using machine learning (ML) applied to nursing records. This retrospective cohort study used natural language processing and ML to develop a model for classifying the nursing records of patients with delirium. We extracted the features of each word from the model and grouped similar words. To evaluate the usefulness of word groups in predicting the occurrence of delirium in patients with COVID-19, we analyzed the temporal changes in the frequency of occurrence of these word groups before and after the onset of delirium. Moreover, the sensitivity, specificity, and odds ratios were calculated. We identified (1) elimination-related behaviors and conditions and (2) abnormal patient behavior and conditions as risk factors for delirium. Group 1 had the highest sensitivity (0.603), whereas group 2 had the highest specificity and odds ratio (0.938 and 6.903, respectively). These results suggest that these parameters may be useful in predicting delirium in these patients. The risk factors for COVID-19-associated delirium identified in this study were more specific but less sensitive than the ICDSC (Intensive Care Delirium Screening Checklist) and CAM-ICU (Confusion Assessment Method for the Intensive Care Unit). However, they are superior to the ICDSC and CAM-ICU because they can predict delirium without medical staff and at no cost.

Molecular diversity and distribution of indigenous arbuscular mycorrhizal communities colonizing roots of two different winter cover crops in response to their root proliferation.

A clear understanding of how crop root proliferation affects the distribution of the spore abundance of arbuscular mycorrhizal fungi (AMF) and the composition of AMF communities in agricultural fields is imperative to identify the potential roles of AMF in winter cover crop rotational systems. Toward this goal, we conducted a field trial using wheat (Triticum aestivum L.) or red clover (Trifolium pratense L.) grown during the winter season. We conducted a molecular analysis to compare the diversity and distribution of AMF communities in roots and spore abundance in soil cropped with wheat and red clover. The AMF spore abundance, AMF root colonization, and abundance of root length were investigated at three different distances from winter crops (0 cm, 7.5 cm, and 15 cm), and differences in these variables were found between the two crops. The distribution of specific AMF communities and variables responded to the two winter cover crops. The majority of Glomerales phylotypes were common to the roots of both winter cover crops, but Gigaspora phylotypes in Gigasporales were found only in red clover roots. These results also demonstrated that the diversity of the AMF colonizing the roots did not significantly change with the three distances from the crop within each rotation but was strongly influenced by the host crop identity. The distribution of specific AMF phylotypes responded to the presence of wheat and red clover roots, indicating that the host crop identity was much more important than the proliferation of crop roots in determining the diversity of the AMF communities.