Research on the treatment based on syndrome differentiation of COVID-19 according to the theory of Guangwenyilun

COVID-19is highly infectious and epidemic, which belongs to the " plague" disease of traditional Chinese medicine and seriously endangers the safety of human life. Based on the theory of Guangwenyilun combined with the treatment experience of COVID-19 in TCM, syndrome differentiation-based treatment and the clinical characteristic of COVID-19is be- ing deeply analysed. COVID-19began because of the" epidemic pathogenic factors", and the pathogenesis is that the dampness and heat generated toxin which blocked the qi movement. The early onset of this disease is in the pleurodiaphragmatic inter- space, and its transmission can be divided into sequential transmission and reverse transmission. Regardless of the direction of its transmission, grasping the transmission of" epidemic pathogenic factors" on the diagnosis and treatment combined with " five distinguishing thought, exterior and interior, accompanying symptoms" theory will be of great importance to help to im- prove the effect of treatment. At the same time, this clinical approach will also provide reference for the diagnosis and treat- ment of any possible new diseases.

Virtual patients for mechanical ventilation in the intensive care unit.

BACKGROUND: Mechanical ventilation (MV) is a core intensive care unit (ICU) therapy. Significant inter- and intra- patient variability in lung mechanics and condition makes managing MV difficult. Accurate prediction of patient-specific response to changes in MV settings would enable optimised, personalised, and more productive care, improving outcomes and reducing cost. This study develops a generalised digital clone model, or in-silico virtual patient, to accurately predict lung mechanics in response to changes in MV. METHODS: An identifiable, nonlinear hysteresis loop model (HLM) captures patient-specific lung dynamics identified from measured ventilator data. Identification and creation of the virtual patient model is fully automated using the hysteresis loop analysis (HLA) method to identify lung elastances from clinical data. Performance is evaluated using clinical data from 18 volume-control (VC) and 14 pressure-control (PC) ventilated patients who underwent step-wise recruitment maneuvers. RESULTS: Patient-specific virtual patient models accurately predict lung response for changes in PEEP up to 12 cmH2O for both volume and pressure control cohorts. R2 values for predicting peak inspiration pressure (PIP) and additional retained lung volume, Vfrc in VC, are R2=0.86 and R2=0.90 for 106 predictions over 18 patients. For 14 PC patients and 84 predictions, predicting peak inspiratory volume (PIV) and Vfrc yield R2=0.86 and R2=0.83. Absolute PIP, PIV and Vfrc errors are relatively small. CONCLUSIONS: Overall results validate the accuracy and versatility of the virtual patient model for capturing and predicting nonlinear changes in patient-specific lung mechanics. Accurate response prediction enables mechanically and physiologically relevant virtual patients to guide personalised and optimised MV therapy.

Psychological crisis intervention for college students during novel coronavirus infection epidemic.

At the beginning of the 2020 New year, novel coronavirus infection continues to affect our lives. The anxiety and stress caused by rising epidemic data, the helplessness and fear caused by city closure and isolation, and the boredom and irritability caused by extended holiday grounding all have a great impact on the psychology of students. In this special stress period of "suspension of classes and non-stop learning", teachers actively help and guide students, do a good job of students' psychological support, perform the duties of spiritual mentors, and do a good job of students' psychological care.