RESUMEN
Introduction: An estimated 43% of COVID-19 patients showed sequelae, including fatigue, neurocognitive impairment, respiratory symptoms, and smell or taste disorders. These sequelae significantly affect an individual's health, work capacity, healthcare systems, and socioeconomic aspects. Traditional Chinese herbal medicine (TCHM) management showed clinical benefits in treating patients with COVID-19 sequelae. This study aimed to analyze the effects of personalized TCHM management in patients with COVID-19 sequelae. Methods: After the COVID-19 outbreak in Taiwan, we recorded Chronic Obstructive Pulmonary Disease Assessment Tool (CAT), Chalder Fatigue Questionnaire (CFQ-11), and Brief Symptom Rating Scale (BSRS-5) to assess post-COVID respiratory, fatigue, and emotional distress symptoms, respectively. In this study, we retrospectively reviewed the medical records between July 2022 and March 2023. We analyzed the effects of TCHM administration after 14- and 28-days of treatment. Results: 47 patients were included in this study. The results demonstrated that personalized TCHM treatment significantly improved the CAT, CFQ-11, and BSRS-5 scores after 14 and 28 days. TCHM alleviated physical and psychological fatigue. In logistic regression analysis, there was no statistically significant differences in the severity of the baseline symptoms and TCHM administration effects concerning the duration since the initial confirmation of COVID-19, sex, age, or dietary preference (non-vegetarian or vegetarian). Conclusions: Our study suggested that personalized TCHM treatment notably reduced fatigue, respiratory and emotional distress symptoms after 14- and 28-days of treatment in patients with COVID-19 sequelae. We propose that TCHM should be considered as an effective intervention for patients with COVID-19 sequelae.
Asunto(s)
COVID-19 , Medicamentos Herbarios Chinos , SARS-CoV-2 , Humanos , Masculino , Femenino , Persona de Mediana Edad , Taiwán/epidemiología , Estudios Retrospectivos , Medicamentos Herbarios Chinos/uso terapéutico , COVID-19/complicaciones , COVID-19/epidemiología , COVID-19/psicología , Anciano , Tratamiento Farmacológico de COVID-19 , Fatiga/tratamiento farmacológico , Fatiga/etiología , Adulto , Medicina Tradicional China/métodos , Resultado del TratamientoRESUMEN
PURPOSE: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. MATERIALS AND METHODS: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. RESULTS: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88-0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. CONCLUSIONS: Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.