3 research outputs found
The Relationship Between COVID-19 Vaccination and Long COVID
No full textM.P.P.Long COVID is a range of persistent conditions that last months and years after the initial acute COVID-19 infection. In the United States, 1 in 7 adults have had long COVID, and 1 in 19 adults are experiencing it as of October 2023. Example symptoms are fatigue, chronic pain, and depression. In severe cases, long COVID is deadly. Because the pandemic is only recent, there is no clear understanding of how acute COVID infection leads to long COVID. There is no cure for it, and treatment options are limited. The link between COVID-19 vaccination and acute COVID-19 infection is well-established, but only a small body of literature examines the relationship between vaccination and long COVID prevention. Studies do not reach a consensus on the efficacy of vaccination at preventing the development of long COVID. My research question therefore is whether pre-infection vaccination can reduce the probability of long COVID. Using the 2022 National Health Interview Survey (NHIS) Adult Sample, I obtain “currently experiencing long COVID” as my outcome variable and “receiving at least one dose of COVID-19 vaccination” as my key independent variable. I hypothesize that vaccination decreases the probability of developing long COVID. I make a novel contribution to the current literature by using the definition of long COVID as three months of longer COVID-19-related symptoms. I also investigate the differential effect of vaccination on long COVID for people of different ages and sexes using interaction terms. In my Linear Probability Models, having socio-demographic factors and health conditions as covariates allows me to hold their effects on long COVID constant while investigating the relationship between vaccination and long COVID. My results suggest a complex relationship between COVID vaccination and long COVID prevention that is affected by various factors, including health status and age. The relationship is inconsistent across my models, which can be attributed to measure errors in my study
Effects of drip application of different concentrations of CO₂ solution on canopy gas exchange, growth, yield, and quality of cotton
No full textChanges in atmospheric CO₂ concentration strongly affect the photosynthetic performance of C₃ plants. Cotton (Gossypium hirsutum L.), a major global cash crop, provides a suitable model to study CO₂ fertilization effects. While moderate CO₂ enrichment can promote growth and yield, the optimal regime for field-scale application remains unclear. In this study conducted in Xinjiang, China, CO₂ gas was dissolved in irrigation water at four concentrations (0.04, 0.08, 0.12, 0.16 kg·m-³) and applied via a drip irrigation system. The effects on canopy CO₂ distribution, plant physiological responses, yield, and fibre quality were assessed. Drip-applied CO₂ solutions increased canopy CO₂ concentration by gradually releasing CO₂ from the soil, which in turn enhanced plant growth indicators (SPAD, AGB, LAI, plant height). Growth promotion followed a dose–response trend, with effects rising at lower concentrations and declining at higher levels. Yield analysis showed that lint yield increased by 1.9% and 8.4% under 0.04 and 0.08 kg·m-³ treatments, respectively, compared with the control (p < 0.05). In contrast, 0.12 and 0.16 kg·m-³ treatments reduced yield by 13.4% and 5.4%, respectively (p < 0.05). Fibre quality indicators remained within the optimal range across all treatments. Overall, 0.08 kg·m-³ was identified as the most effective concentration, producing the highest yield while maintaining fibre quality. These findings provide a scientific basis for the field application of CO₂-enriched irrigation, offering a promising approach to enhance cotton productivity and the ecological sustainability of farmland systems
Identification of Novel Proteins Interacting with Vascular Endothelial Growth Inhibitor 174 in Renal Cell Carcinoma
No full textBackground/Aim: Vascular endothelial growth inhibitor (VEGI) is a multipotential cytokine that plays a role in regulating immunity, anti-angiogenesis, and inhibiting tumor growth. However, the proteins that interact with it are still unknown. In the present study, we examined the proteins that interact with VEGI174 and their expression in renal cell carcinoma (RCC). Materials and Methods: The proteins that interact with VEGI174 were identified using western blot, pull-down assay, and mass spectrometry. The expressions of VEGI174 and the interacting proteins were examined in RCC and were compared to normal renal tissues using immunohistochemical staining and RNA-seq respectively. Results: The results of the mass spectrometric analysis showed that ACLY, ENO1, ZIK1, AKR1C3, and MYC may interact with VEGI174. When compared to the TCGA database, the expression level of VEGI174 in RCC was lower than that in normal kidney using RNAseq (p<0.001). The expression levels of ACLY, ENO1, ZIK1, AKR1C3 and MYC in RCC were higher than those in normal kidney (p<0.05, all of above factors). Moreover, immunochemical staining results also showed that the expression levels of AKR1C3 in RCC were significantly higher those that in normal kidney (p<0.001) and was also positively correlated with higher RCC stage and grade. Conclusion: Taken together, our findings showed that VEGI174 may interact with ACLY, ENO1, ZIK1, AKR1C3, and MYC. The expression of ACLY, ENO1, AKR1C3 and MYC is increased in RCC. AKR1C3 was a new factor that may correlate with the progression of RCC. The results indicated that VEGI174 has more functions than we currently know in the development and progression of RCC.National Natural Science Foundation of China [81372138]; Cancer Research WalesCPCI-S(ISTP)84379-43883
