[Exploration and example interpretation of real-world herbal prescription classification based on similarity matching algorithm].

In observational studies, herbal prescriptions are usually studied in the form of "similar prescriptions". At present, the classification of prescriptions is mainly based on clinical experience judgment, but there are some problems in manual judgment, such as lack of unified criteria, labor consumption, and difficulty in verification. In the construction of a database of integrated traditional Chinese and western medicine for the treatment of coronavirus disease 2019(COVID-19), our research group tried to classify real-world herbal prescriptions using a similarity matching algorithm. The main steps include 78 target prescriptions are determined in advance; four levels of importance labeling shall be carried out for the drugs of each target prescription; the combination, format conversion, and standardization of drug names of the prescriptions to be identified in the herbal medicine database; calculate the similarity between the prescriptions to be identified and each target prescription one by one; prescription discrimination is performed based on the preset criteria; remove the name of the prescriptions with "large prescriptions cover the small". Through the similarity matching algorithm, 87.49% of the real prescriptions in the herbal medicine database of this study can be identified, which preliminarily proves that this method can complete the classification of herbal prescriptions. However, this method does not consider the influence of herbal dosage on the results, and there is no recognized standard for the weight of drug importance and criteria, so there are some limitations, which need to be further explored and improved in future research.

Clinical Leader Series: A Virtual Mental Well-being Initiative for Nurse Leaders During the COVID-19 Pandemic and Beyond.

OBJECTIVE: A virtual mental well-being initiative was developed for nurse leaders to provide education about mental health and to teach self-care skills. BACKGROUND: Because of substantial demand placed on nurse leaders during the COVID-19 pandemic, organizations must address stress and burnout by providing a continuum of care to include education, support, and intervention. METHODS: All levels of nurse leaders at a multicampus healthcare system were invited to attend. Data were collected on coping, empowerment, burnout, and quality of life. Participant responses to discussion prompts were compiled and reviewed. RESULTS: Although the independent parallel comparison did not show significant improvements, scores on the coping, empowerment, burnout, and quality-of-life measures were maintained. Discussion prompts yielded valuable insights into nurse leader experiences and session effectiveness. CONCLUSIONS: This type of education, as well as psychological support, will continue to be needed after the pandemic due to burnout, moral injury, and primary or secondary trauma. Findings are applicable to future crisis situations.

[Evolution and Potential Source Apportionment of Atmospheric Pollutants of Two Heavy Haze Episodes During the COVID-19 Lockdown in Beijing, China].

To control the spread of the 2019 novel coronavirus(COVID-19), China imposed rigorous restrictions, which resulted in great reductions in pollutant emissions. However, two heavy haze pollution episodes still occurred in Beijing. In this study, we use the air pollutants, aerosol number concentration, and meteorological elements data in Beijing, combined with the HYSPLIT model, to calculate the potential source contribution factor(PSCF) and concentration weight trajectory(CWT), and analyze the characteristics of evolution and potential source apportionment of atmospheric pollutants during the two episodes. The COVID-19 lockdown restrictions had great impacts on the diurnal variations of PM2.5 and black carbon(BC), while small impacts on the diurnal variations of CO, NO2, SO2, and O3. The primary pollutant was PM2.5 during the two haze pollution episodes, and the haze1 episode was mainly local pollution, while haze 2 was mainly local and external transportation pollution. The spectrum of aerosol number concentration was unimodal under different processes, with the peak located at 0.3 µm. During the haze episodes, the number concentration in the size range of 0.2-0.5 µm increased 3.3-13.6 times that of the clean days. The mass concentration contributions of BCliquid to BC in different processes were 64.8%-85.1%. This mass concentration of BCliquid ranked in the order of haze 1(5.04 µg·m-3) >haze 1(3.20 µg·m-3) >clean day(before COVID-19) (2.31 µg·m-3) >clean day(COVID-19) (0.76 µg·m-3). The characteristics of PSCF and CWT distribution of PM2.5 and BC were different in different processes. The PSCF high value areas of PM2.5 on the clean day(before COVID-19) and the clean day(COVID-19) were mainly distributed in the southwest and western of Beijing, and the weight concentration exceeded 30 µg·m-3. The PSCF high value areas of PM2.5 during haze 1 and haze 2 were mainly distributed in Beijing and its surrounding areas and southwestern, when the weight concentration exceeded 90 µg·m-3. The PSCF high value areas of BC were mainly distributed in Beijing and its surrounding areas on clean days(before COVID-19), clean days(COVID-19) and haze 1, with weighted concentrations exceeding 2.4, 0.9 and, 4.5 µg·m-3, respectively. The PSCF high value areas of BC on haze 2 was distributed in the southwest of Beijing, and the weight concentration exceeded 5 µg·m-3.