Long-term sequelae refer to persistent symptoms or signs for >6 months after SARS-CoV-2 infection. The most common symptoms of sequelae are fatigue and neuropsychiatric symptoms (concentration difficulty, amnesia, cognitive dysfunction, anxiety, and depression). However, approved treatments have not been fully established. Herbal medicines are administered for 12 weeks to patients who continuously complain of fatigue or cognitive dysfunction for >4 weeks that only occurred after COVID-19 diagnoses. Based on the Korean Medicine syndrome differentiation diagnosis, patients with fatigue will be administered Bojungikgi-tang or Kyungok-go, whereas those with cognitive dysfunction will be administered Cheonwangbosim-dan. Results could support evidence that herbal medicines may mitigate fatigue and cognitive dysfunction caused by COVID-19. Furthermore, by investigating the effects of herbal medicines on changes in metabolite and immune response due to COVID-19, which may be responsible for sequelae, the potential of herbal medicines as one of the therapeutic interventions for post-acute sequelae of SARS-CoV-2 infection can be evaluated. Therefore, the effects of herbal medicine on fatigue and cognitive dysfunction sequelae due to COVID-19 will be elucidated in this study to provide an insight into the preparation of medical management for the post-acute sequelae of SARS-CoV-2 infection.

The purpose of the present study was to examine the body composition of So-Yang type males according to Sasang constitutional medicine, which is popular in Korea. Different Sasang constitutional types are associated with different muscle distributions, body shapes, and disease susceptibilities. We used the Sasang Personality Questionnaire (SPQ) as a measure of the temperament of each Sasang type. In total, 953 subjects aged over 20 years were recruited in Korea. We collected anthropometric parameters and bioimpedence information from the subjects and administered the SPQ. A logistic regression was conducted to calculate propensity scores. The percentage of skeletal muscle mass in So-Yang (SY) and non-So-Yang (non-SY) males was 45.8 ± 2.7 and 44.2 ± 3.3, respectively, before matching and 45.8 ± 2.6 and 44.9 ± 3.0, respectively, after propensity score matching. The extracellular water (ECW)/intracellular water (ICW) and extracellular water (ECW)/total body water (TBW) ratios and SPQ scores were significantly different between the SY and non-SY types. This study suggested that the SY type may be significantly and independently associated with body composition and could be associated with personality.

Background: Infections of Coronavirus Disease-2019 (COVID-19) can cause long-term effects known as long COVID. This pilot study aimed to evaluate the feasibility of a clinical study as well as the efficacy and safety of traditional East Asian herbal medicines in alleviating fatigue and cognitive dysfunction “brain fog” in patients with long COVID. Methods: This prospective pilot study investigated the use of three types of herbal medicines, Bojungikki-tang (BIT), Kyungok-go (KOG), and Cheonwangbosim-dan (CBD), for a 12-week period as potential treatments for fatigue and cognitive dysfunction in patients with long COVID. Forty-five patients with long COVID were recruited, and one of three drugs was given based on the patient’s symptoms and pattern identification. The effect of herbal medications on fatigue and cognitive function outcomes was assessed over a 36-week period, with patient adherence closely monitored. Results: After 12 weeks of herbal drug administration, fatigue symptoms improved significantly across all groups, with treatment success rates of 80%, 53.33%, and 46.67% in the BIT, KOG, and CBD groups, respectively. However, “brain fog” symptoms showed less improvement, with treatment success rates of 40%, 46.67%, and 13.33% in the BIT, KOG, and CBD groups, respectively. All adverse events reported were mild and unrelated to the medication. The study design was found to be feasible with high medication adherence. Conclusions: This study demonstrated the feasibility of conducting a clinical trial with three herbal medicines to treat long COVID symptoms like fatigue and “brain fog.”

Herbal medicine is widely prescribed worldwide. To date, however, studies on the prediction of efficacy of herbal medicine based on machine learning have very rarely been reported. The objectives of this study are to predict the efficacy of Taeumjowi-tang (one of herbal medicines) and evaluate the prediction model in treating metabolic abnormalities. Subjects were divided into an improvement group and a non-improvement group based on the difference before and after oral administration of an herbal medicine. Efficacy models of triglyceride level, high-density lipoprotein (HDL) cholesterol level, systolic blood pressure (SBP), and diastolic blood pressure (DBP) were built using a least absolute shrinkage and selection operator (LASSO) based on variables extracted from face shape, face colors, body circumference, questionnaire, voice, and tongue color. In predicting efficacy for four metabolic risk factors, the efficacy model of HDL cholesterol level showed the best the area under the receiver operating characteristic curve (AUC) value among the four models (AUC = 0.785 (confidence interval = 0.693, 0.877)). The AUC value of the efficacy model of triglyceride level was 0.659 (0.551, 0.768). Efficacy models of DBP and SBP showed AUC values of 0.665 (0.551, 0.78) and 0.54 (0.385, 0.694), respectively. The results may provide a clue to predict whether a drug will be effective for each subject with phenotypic information and to reduce the use of an ineffective drug or its side effects.