Comparison of Demographic, Epidemiological, Immunological, and Clinical Characteristics of Patients with HIV Mono-infection Versus Patients Co-infected with HCV or/and HBV: A Serbian Cohort Study

Ranin, Jovan; Salemović, Dubravka; Brmbolic, Branko; Marinković, Jelena; Boričić, Ivan; Pesic, Pavlovic I.; Zerjav, S; Stanojević, Marija; Jevtović, Djordje

doi:10.2174/1570162x16666180717115614

Cited by 5 publications

(3 citation statements)

References 74 publications

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“…The higher the feature score is, the more contribution a variable makes to the detective of coinfected with HBV patients in HIV cases. PLA was the most accurate variable in our study, closely followed by CR, AST, HB, ALT, TBIL, leucocyte, age, marital status, and treatment condition, which is similar to previous studies [33][34][35]. PLA had the highest detective value in the heterosexual group, followed by ALT, AST, TBIL, leucocyte, and symptom severity.…”

Section: Discussionsupporting

confidence: 90%

A Noninvasive Prediction Model for Hepatitis B Virus Disease in Patients with HIV: Based on the Population of Jiangsu, China

Yin

Xue

Shi

et al. 2021

BioMed Research International

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Objective. To establish a machine learning model for identifying patients coinfected with hepatitis B virus (HBV) and human immunodeficiency virus (HIV) through two sexual transmission routes in Jiangsu, China. Methods. A total of 14197 HIV cases transmitted by homosexual and heterosexual routes were recruited. After data processing, 12469 cases (HIV and HBV, 1033; HIV, 11436) were left for further analysis, including 7849 cases with homosexual transmission and 4620 cases with heterosexual transmission. Univariate logistic regression was used to select variables with significant P value and odds ratio for multivariable analysis. In homosexual transmission and heterosexual transmission groups, 10 and 6 variables were selected, respectively. For identifying HIV individuals coinfected with HBV, a machine learning model was constructed with four algorithms, including Decision Tree, Random Forest, AdaBoost with decision tree (AdaBoost), and extreme gradient boosting decision tree (XGBoost). The detective value of each variable was calculated using the optimal machine learning algorithm. Results. AdaBoost algorithm showed the highest efficiency in both transmission groups (homosexual transmission group: accuracy = 0.928 , precision = 0.915 , recall = 0.944 , F − 1 = 0.930 , and AUC = 0.96 ; heterosexual transmission group: accuracy = 0.892 , precision = 0.881 , recall = 0.905 , F − 1 = 0.893 , and AUC = 0.98 ). Calculated by AdaBoost algorithm, the detective value of PLA was the highest in homosexual transmission group, followed by CR, AST, HB, ALT, TBIL, leucocyte, age, marital status, and treatment condition; in the heterosexual transmission group, the detective value of PLA was the highest (consistent with the condition in the homosexual group), followed by ALT, AST, TBIL, leucocyte, and symptom severity. Conclusions. The univariate logistics regression combined with the AdaBoost algorithm could accurately screen the risk factors of HBV in HIV coinfection without invasive testing. Further studies are needed to evaluate the utility and feasibility of this model in various settings.

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Section: Discussionsupporting

confidence: 90%

A Noninvasive Prediction Model for Hepatitis B Virus Disease in Patients with HIV: Based on the Population of Jiangsu, China

Yin

Xue

Shi

et al. 2021

BioMed Research International

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“…In this study, we report a cohort of 389 patients with laboratory‐confirmed SARS‐CoV‐2 infection. Most patients presented with fever, cough and fatigue, which share similar clinical features with previous beta‐coronavirus infections, such as SARS and Middle East respiratory syndrome (MERS) [9,10]. Highly pathogenic human coronaviruses (hCoV) infections frequently lead to lower respiratory tract disease, and disease severity was influenced by initial viral titers in airways, age and co‐morbid conditions [6,11,12].…”

Section: Discussionmentioning

confidence: 99%

Using IL-2R/lymphocytes for predicting the clinical progression of patients with COVID-19

Hou

Zhang

Huang

et al. 2020

Clinical and Experimental Immunology

132

121

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Effective laboratory markers for the estimation of disease severity and predicting the clinical progression of coronavirus disease-2019 (COVID-19) is urgently needed. Laboratory tests, including blood routine, cytokine profiles and infection markers, were collected from 389 confirmed COV-ID-19 patients. The included patients were classified into mild (n = 168), severe (n = 169) and critical groups (n = 52). The leukocytes, neutrophils, infection biomarkers [such as C-reactive protein (CRP), procalcitonin (PCT) and ferritin] and the concentrations of cytokines [interleukin (IL)-2R, IL-6, IL-8, IL-10 and tumor necrosis factor (TNF)-α] were significantly increased, while lymphocytes were significantly decreased with increased severity of illness. The amount of IL-2R was positively correlated with the other cytokines and negatively correlated with lymphocyte number. The ratio of IL-2R to lymphocytes was found to be remarkably increased in severe and critical patients. IL-2R/lymphocytes were superior compared with other markers for the identification of COVID-19 with critical illness, not only from mild but also from severe illness. Moreover, the cytokine profiles and IL-2R/lymphocytes were significantly decreased in recovered patients, but further increased in disease-deteriorated patients, which might be correlated with the outcome of COVID-19. Lymphopenia and increased levels of cytokines were closely associated with disease severity. The IL-2R/lymphocyte was a prominent biomarker for early identification of severe COVID-19 and predicting the clinical progression of the disease.

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“…[HIV], determinando a coinfecção HIV-HCV, o que pode mudar o curso natural da história da infecção pelo HCV [3][4][5][6] .…”

Section: Introductionunclassified

Prevalência e potenciais mecanismos de exposição à infecção pelo vírus da hepatite C numa coorte de pacientes que vivem com HIV

Ferrufino¹

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A Rafael, meu querido esposo, sempre me desafiando a fazer e dar o melhor, e por sua companhia em cada momento.A meus queridos pais Maria Edilia e José Luís, os quais, com seu exemplo, me ensinaram que tudo que vale a pena, sempre, é resultado de uma batalha que se ganha com esforço e dedicação.A minhas queridas irmãs Marianela, Patrícia e Angélica, pelo apoio e pela presença constante mesmo na distância.A meu filho Pedro, pela alegria que trouxe a minha vida.À professora Maria Cassia, por ser minha orientadora neste trabalho, pelos seus valiosos ensinamentos e pela dedicação na procura da excelência.À professora Ana Luiza Bierrenbach, pelas contribuições no desenvolvimento deste projeto.

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Comparison of Demographic, Epidemiological, Immunological, and Clinical Characteristics of Patients with HIV Mono-infection Versus Patients Co-infected with HCV or/and HBV: A Serbian Cohort Study

Abstract: HIV/HCV co-infection was found to be more prevalent than HIV/HBV co-infection in a Serbian cohort. Co-infection with HCV was related to more profound immunodeficiency prior to therapy initiation, reflecting a possible unfavorable impact of HCV on the natural history of HIV infection.

Cited by 5 publications

References 74 publications

A Noninvasive Prediction Model for Hepatitis B Virus Disease in Patients with HIV: Based on the Population of Jiangsu, China

A Noninvasive Prediction Model for Hepatitis B Virus Disease in Patients with HIV: Based on the Population of Jiangsu, China

Using IL-2R/lymphocytes for predicting the clinical progression of patients with COVID-19

Prevalência e potenciais mecanismos de exposição à infecção pelo vírus da hepatite C numa coorte de pacientes que vivem com HIV

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