RESUMEN
OBJECTIVES: Many reverse transcription polymerase chain reaction (RT-PCR) methods exist that can detect SARS-CoV-2 RNA in different matrices. RT-PCR is highly sensitive, although viral RNA may be detected long after active infection has taken place. SARS-CoV-2 proteins have shorter detection windows hence their detection might be more meaningful. Given salivary droplets represent a main source of transmission, we explored the detection of viral RNA and protein using four different detection platforms including SISCAPA peptide immunoaffinity liquid chromatography-mass spectrometry (SISCAPA-LC-MS) using polyclonal capture antibodies. METHODS: The SISCAPA-LC MS method was compared to RT-PCR, RT-loop-mediated isothermal amplification (RT-LAMP), and a lateral flow rapid antigen test (RAT) for the detection of virus material in the drool saliva of 102 patients hospitalised after infection with SARS-CoV-2. Cycle thresholds (Ct) of RT-PCR (E gene) were compared to RT-LAMP time-to-positive (TTP) (NE and Orf1a genes), RAT optical densitometry measurements (test line/control line ratio) and to SISCAPA-LC-MS for measurements of viral protein. RESULTS: SISCAPA-LC-MS showed low sensitivity (37.7â¯%) but high specificity (89.8â¯%). RAT showed lower sensitivity (24.5â¯%) and high specificity (100â¯%). RT-LAMP had high sensitivity (83.0â¯%) and specificity (100.0â¯%). At high initial viral RNA loads (<20 Ct), results obtained using SISCAPA-LC-MS correlated with RT-PCR (R2 0.57, p-value 0.002). CONCLUSIONS: Detection of SARS-CoV-2 nucleoprotein in saliva was less frequent than the detection of viral RNA. The SISCAPA-LC-MS method allowed processing of multiple samples in <150â¯min and was scalable, enabling high throughput.
Asunto(s)
COVID-19 , Espectrometría de Masas , Técnicas de Diagnóstico Molecular , ARN Viral , SARS-CoV-2 , Saliva , Humanos , Saliva/virología , Saliva/química , SARS-CoV-2/aislamiento & purificación , SARS-CoV-2/inmunología , SARS-CoV-2/genética , COVID-19/diagnóstico , COVID-19/virología , ARN Viral/análisis , Espectrometría de Masas/métodos , Técnicas de Amplificación de Ácido Nucleico/métodos , Reacción en Cadena de la Polimerasa de Transcriptasa Inversa/métodos , Masculino , Sensibilidad y Especificidad , Femenino , Persona de Mediana Edad , Fosfoproteínas/análisis , Fosfoproteínas/inmunología , Proteínas de la Nucleocápside de Coronavirus/análisis , Proteínas de la Nucleocápside de Coronavirus/inmunología , Antígenos Virales/análisis , Antígenos Virales/inmunología , Adulto , Cromatografía Liquida/métodosRESUMEN
Hypertension is the leading modifiable risk factor for cardiovascular disease, but less than 50% have their blood pressure controlled. A possible avenue to support hypertension management is a holistic approach, using non-pharmacological interventions. Since hypertension is mediated in part by dysregulation of the autonomic nervous system (ANS), biofeedback may help improve hypertension management by targeted self-regulation and self-awareness of parameters that regulate the ANS. This systematic review aimed to assess the effectiveness of biofeedback on blood pressure in hypertensive patients. The review was pre-registered on PROSPERO and followed the PICO strategy. A total of 1782 articles were retrieved, 20 met the inclusion criteria. Sample sizes ranged from 15 to 301 participants; with a median age of 49.3 (43.3-55.0) years and 45% were female. There was a significant effect of biofeedback on systolic (-4.52, Z = 2.31, P = 0.02, CI [-8.35, -0.69]) and diastolic blood pressure (-5.19, Z = 3.54, P = 0.0004, CI [-8.07, -2.32]). Six different biofeedback modalities were used, with biofeedback delivered by psychologists, trained therapists and research assistants. There was no publication bias, heterogeneity was rated as substantial and data quality was rated to be poor. This review demonstrated that biofeedback had a significant effect on blood pressure. However, this should be viewed in the context of included studies being limited by heterogeneity and dated literature, meaning the research does not reflect the current biofeedback technology such as wearable devices. Future research should incorporate these technologies with robust methodology to fully understand the effect of biofeedback on hypertension.