RESUMEN
The COVID-19 pandemic, emerging/re-emerging infections as well as other non-communicable chronic diseases, highlight the necessity of smart microfluidic point-of-care diagnostic (POC) devices and systems in developing nations as risk factors for infections, severe disease manifestations and poor clinical outcomes are highly represented in these countries. These POC devices are also becoming vital as analytical procedures executable outside of conventional laboratory settings are seen as the future of healthcare delivery. Microfluidics have grown into a revolutionary system to miniaturize chemical and biological experimentation, including disease detection and diagnosis utilizing muPads/paper-based microfluidic devices, polymer-based microfluidic devices and 3-dimensional printed microfluidic devices. Through the development of droplet digital PCR, single-cell RNA sequencing, and next-generation sequencing, microfluidics in their analogous forms have been the leading contributor to the technical advancements in medicine. Microfluidics and machine-learning-based algorithms complement each other with the possibility of scientific exploration, induced by the framework's robustness, as preliminary studies have documented significant achievements in biomedicine, such as sorting, microencapsulation, and automated detection. Despite these milestones and potential applications, the complexity of microfluidic system design, fabrication, and operation has prevented widespread adoption. As previous studies focused on microfluidic devices that can handle molecular diagnostic procedures, researchers must integrate these components with other microsystem processes like data acquisition, data processing, power supply, fluid control, and sample pretreatment to overcome the barriers to smart microfluidic commercialization.Copyright © 2023 by Begell House, Inc.
RESUMEN
Background: Kidney involvement in coronavirus disease 2019 (COVID-19) pathology has been supported by high frequency of angiotensin-converting enzyme 2 (ACE2) expression on renal cells and reports of acute kidney injury. However, the association between host viral load and kidney function is not clear. Aim: In this study, plasma levels of renal markers (urea nitrogen, creatinine, and estimated glomerular filtration rate (eGFR)) and electrolytes (sodium, potassium, chlorine, and bicarbonate) were assessed in relation to SARS-CoV-2 viral load of COVID-19 patients. Patients and Methods: This cross-sectional study involved 144 consenting COVID-19 patients admitted to the Ogun state COVID-19 isolation center between May and December 2020. All participants presented with mild respiratory symptoms and did not require ICU admission or ventilation support. Data included reverse transcriptase polymerase chain reaction (RT-PCR) cycle threshold (CT) value, blood urea nitrogen (BUN), creatinine, sodium, potassium, chlorine, bicarbonate measurements, and glomerular filtration rate. Reference intervals were used as comparators, and multiple linear regression model was fitted. Statistical significance was set at P < 0.05. Results: BUN level and creatinine were elevated in 4 (2.8%) and 42 (29.2%) patients, respectively, with lowered eGFR observed in 37 (25.7%) patients. Hyponatremia and hypokalemia were observed in 35 (24.3%) and 21 (14.6%) patients, respectively, while hypochloremia was observed in 21 (14.6%) patients. Lowered bicarbonate was observed in 29 (20.1%) patients. Linear regression showed statistically significant association (R2 = 0.340, P = 0.032) between RT-PCR CT value and eGFR (ß = 0.006, P = 0.017) as well as HCO3 (ß = -0.262, P = 0.036). Conclusion: COVID-19 patients with mild respiratory symptoms exhibited renal abnormalities, electrolytes, and acid-base imbalances which were partly associated with SARS-CoV-2 viral load.