Delayed SARS-CoV-2 Clearance in Patients with Obesity

Zhang, Xiujun; Lin, Bin; Ye, Gang; Liu, Longgen; Lu, Jicun; Lü, Zeng; Xue, Yuan

doi:10.2147/idr.s319029

Cited by 9 publications

(7 citation statements)

References 20 publications

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“…Even after correcting for several other factors, the Black, Asian, and Minority Ethnic (BAME) groups are at very high risk of death from COVID-19 infection, according to current mortality [70]. COVID-19 infection is more likely in those with metabolic risk factors such as obesity, diabetes, and cardiovascular disease, according to numerous research [71][72][73]. In addition, Plasma proteome examination recognized COVID-19 disease biomarkers progression, and the important pathways are platelet degranulation, complement system, and coagulation cascades.…”

Section: Markers For Covid-19 In Pco Patientsmentioning

confidence: 99%

The Role of Microparticles in Polycystic Ovarian Syndrome. An Updated Review

Almaghrabi¹

2022

Int J Pharm Res Allied Sci

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Microparticles (MPs) are vesicles of less than 1 μm in diameter (submicron vesicles) shed from plasma membranes to cell activation, injury, and apoptosis response. They consisted of membrane proteins and cytosolic material from the cell they originated. These vesicles are vital mediators of pathological and physiological cellular processes. Polycystic ovary syndrome (PCOS) is a regular endocrine, menstrual and metabolic condition that affects 10-15% of females in their reproductive period. Numerous researches have described the association between low-grade chronic inflammation and PCOS; however, the relation is not well understood. Chronic lowgrade inflammation is reflected as a risk factor for cardiovascular disease, atherosclerosis, and endothelial dysfunction, and it is linked to abdominal obesity and insulin resistance (IR). MPs may be useful biomarkers for the early detection of cardiovascular disease and thrombosis in PCOS patients. In March 2020, the novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) became pandemic, wreaking havoc on healthcare systems worldwide and the global economy. Obesity, diabetes, and cardiovascular disease have all been linked to COVID-19 increased risk of infection. PCOS patients have recently been identified as an underserved and potentially high-risk demographic for COVID-19 problems. This article tried to review and present recent studies that explored the role of microparticles in polycystic ovarian syndrome.

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Section: Markers For Covid-19 In Pco Patientsmentioning

confidence: 99%

The Role of Microparticles in Polycystic Ovarian Syndrome. An Updated Review

Almaghrabi¹

2022

Int J Pharm Res Allied Sci

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“…[1] Many studies have summarized the characteristics of patients with COVID-19 and identified several factors associated with the viral shedding time (VST). [2][3][4][5][6][7] However, few studies have developed predictive models for the VST of the severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) Delta variant.…”

Section: Introductionmentioning

confidence: 99%

Nomogram for Predicting Viral Shedding Time of SARS-CoV-2 Delta Variant in Patients with COVID-19: A Retrospective Study

Zhou,

Fan,

et al. 2023

Infect Dis Immun

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Background The coronavirus disease 2019 (COVID-19) cases continue to rise, and the demand for medical treatment and resources in healthcare systems surges. Assessing the viral shedding time (VST) of patients with COVID-19 can facilitate clinical decision making. Although some studies have been conducted on the factors affecting the VST of severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), few prediction models are currently available. Methods This retrospective study included the consecutive patients with COVID-19 admitted to Xi’an Chest Hospital in Shaanxi, China, for treatment between December 19, 2021 and February 5, 2022. The clinical data of the patients were extracted from their electronic medical records. Combining significant factors affecting the VST, a nomogram was developed to predict the VST of the SARS-CoV-2 Delta variant in patients with COVID-19. Results We included 332 patients in this study. The average VST was 21 d. VST was significantly prolonged in patients with severe clinical symptoms, sore throat, old age, long time from onset to diagnosis, and an abnormal white blood cell count. Consequently, we developed a nomogram prediction model using these 5 variables. The concordance index (C-index) of this nomogram was 0.762, and after internal validation using bootstrapping (1000 resamples), the adjusted C-index was 0.762. The area under the nomogram’s receiver operator characteristic curve showed good discriminative ability (0.965). The calibration curve showed high consistency. The VST was prolonged in the group with lower model fitting scores according to the Kaplan-Meier curve (χ2=286, log-rank P < 0.001). Conclusions We developed a nomogram for predicting VST based on 5 easily accessible factors. It can effectively estimate the appropriate isolation period, control viral transmission, and optimize clinical strategies.

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“…Studies have shown that male, elderly, overweight COVID-19 patients have prolonged DVS [4,5]. Abnormal inflammatory indicators, such as interleukin-6 (IL-6) levels, C-reactive protein (CRP) levels, lactate dehydrogenase (LDH) levels, neutrophil count (NC), lymphocyte count (LC), and monocyte count (MC), are important factors for an extended DVS in COVID-19 patients [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…Data are presented as the median (IQR) or n (%). P values were calculated using Wilcoxon's rank-sum test, the χ 2 test, or Fisher's exact test 4. Ensi Luo et al…”

mentioning

confidence: 99%

Development and external validation of a prognostic tool for nonsevere COVID-19 inpatients

Luo,

Zhong,

Wen

et al. 2023

Epidemiol. Infect.

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To develop a machine learning model and nomogram to predict the probability of persistent virus shedding (PVS) in hospitalized patients with coronavirus disease 2019 (COVID-19), the clinical symptoms and signs, laboratory parameters, cytokines, and immune cell data of 429 patients with nonsevere COVID-19 were retrospectively reviewed. Two models were developed using the Akaike information criterion (AIC). The performance of these two models was analyzed and compared by the receiver operating characteristic (ROC) curve, calibration curve, net reclassification index (NRI), and integrated discrimination improvement (IDI). The final model included the following independent predictors of PVS: sex, C-reactive protein (CRP) level, interleukin-6 (IL-6) level, the neutrophil-lymphocyte ratio (NLR), monocyte count (MC), albumin (ALB) level, and serum potassium level. The model performed well in both the internal validation (corrected C-statistic = 0.748, corrected Brier score = 0.201) and external validation datasets (corrected C-statistic = 0.793, corrected Brier score = 0.190). The internal calibration was very good (corrected slope = 0.910). The model developed in this study showed high discriminant performance in predicting PVS in nonsevere COVID-19 patients. Because of the availability and accessibility of the model, the nomogram designed in this study could provide a useful prognostic tool for clinicians and medical decision-makers.

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Delayed SARS-CoV-2 Clearance in Patients with Obesity

Cited by 9 publications

References 20 publications

The Role of Microparticles in Polycystic Ovarian Syndrome. An Updated Review

The Role of Microparticles in Polycystic Ovarian Syndrome. An Updated Review

Nomogram for Predicting Viral Shedding Time of SARS-CoV-2 Delta Variant in Patients with COVID-19: A Retrospective Study

Development and external validation of a prognostic tool for nonsevere COVID-19 inpatients

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