Content of Essential Trace Elements in the Hair of Residents of the Caspian Region of the Republic of Kazakhstan Who Recovered from COVID-19.
Gulnara BatyrovaZhenisgul TlegenovaVictoria KononetsGulmira UmarovaYerlan BazargaliyevGulaim TaskozhinaNurgul KereyevaYeskendir UmarovPublished in: Diagnostics (Basel, Switzerland) (2022)
This study aimed to investigate the content of essential elements in the hair of unvaccinated residents of the Caspian region who recovered from COVID-19. This cross-sectional study involved 260 unvaccinated permanent residents of Mangistau oblast aged 18-60. The diagnosis and severity of COVID-19 were based on clinical signs and symptoms, laboratory data, R-graph results, and oxygen saturation by the Clinical Protocol of the Ministry of Health of the Republic of Kazakhstan. Inductively coupled plasma mass spectrometry determined the content of trace elements cobalt (Co), chromium (Cr), copper (Cu), iron (Fe), iodine (I), manganese (Mn), selenium (Se), and zinc (Zn). The content of Cr ( p < 0.05), Cu ( p < 0.05), Fe ( p < 0.001), I ( p < 0.05), Mn ( p < 0.001), and Zn ( p < 0.05) in the hair of individuals who had a coronavirus infection was lower than those who did not have this infection. There were significantly higher levels of Cu ( p < 0.05) in the hair of participants who had moderate or severe COVID-19 compared to those with mild forms. The results of multiple regression analysis showed that in the presence of a COVID-19 infection in a subject's history, the content of Cr (0.871 (95% CI: 0.811; 0.936)), Cu (0.875 (95% CI: 0.803; 0.955)), Fe (0.745 (95% CI: 0.636; 0.873)), and Mn (0.642 (95%CI: 00.518; 0.795)) decreased in the hair. The data obtained indicate that past COVID-19 infections affect the trace element status of the inhabitants of the Caspian region of Kazakhstan.
Keyphrases
- sars cov
- coronavirus disease
- metal organic framework
- aqueous solution
- mass spectrometry
- respiratory syndrome coronavirus
- public health
- heavy metals
- room temperature
- electronic health record
- big data
- magnetic resonance
- physical activity
- high intensity
- depressive symptoms
- health information
- high resolution
- ms ms
- deep learning
- gold nanoparticles
- convolutional neural network
- transition metal
- dual energy
- health promotion