Elevated neopterin in tuberculosis and co-infection with HIV and the effect of treatment: A systematic review, meta-analysis, and meta-regression

Saghazadeh, Amene; Rezaei, Nima

doi:10.1016/j.intimp.2022.109147

Cited by 4 publications

(2 citation statements)

References 85 publications

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“…For instance, Bayesian network meta-analysis (NMA) summarization disclosed that at week 96, there is improved differentiation in the efficacy, safety, and durability of dolutegravir when taken prior to exposure [ 57 ]. Furthermore, in recent years, some scholars have delved deeper into analyzing the treatment and immune changes of HIV-infected individuals with concurrent infections (tuberculosis [ 58 ], COVID-19 [ 59 ]) using multiple machine learning models. The application of vaccine-induced immune factors [ 60 ] has also found relevance in this domain.…”

Section: Discussionmentioning

confidence: 99%

The predictive accuracy of machine learning for the risk of death in HIV patients: a systematic review and meta-analysis

Li,

Feng,

et al. 2024

BMC Infect Dis

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Background Early prediction of mortality in individuals with HIV (PWH) has perpetually posed a formidable challenge. With the widespread integration of machine learning into clinical practice, some researchers endeavor to formulate models predicting the mortality risk for PWH. Nevertheless, the diverse timeframes of mortality among PWH and the potential multitude of modeling variables have cast doubt on the efficacy of the current predictive model for HIV-related deaths. To address this, we undertook a systematic review and meta-analysis, aiming to comprehensively assess the utilization of machine learning in the early prediction of HIV-related deaths and furnish evidence-based support for the advancement of artificial intelligence in this domain. Methods We systematically combed through the PubMed, Cochrane, Embase, and Web of Science databases on November 25, 2023. To evaluate the bias risk in the original studies included, we employed the Predictive Model Bias Risk Assessment Tool (PROBAST). During the meta-analysis, we conducted subgroup analysis based on survival and non-survival models. Additionally, we utilized meta-regression to explore the influence of death time on the predictive value of the model for HIV-related deaths. Results After our comprehensive review, we analyzed a total of 24 pieces of literature, encompassing data from 401,389 individuals diagnosed with HIV. Within this dataset, 23 articles specifically delved into deaths during long-term follow-ups outside hospital settings. The machine learning models applied for predicting these deaths comprised survival models (COX regression) and other non-survival models. The outcomes of the meta-analysis unveiled that within the training set, the c-index for predicting deaths among people with HIV (PWH) using predictive models stands at 0.83 (95% CI: 0.75–0.91). In the validation set, the c-index is slightly lower at 0.81 (95% CI: 0.78–0.85). Notably, the meta-regression analysis demonstrated that neither follow-up time nor the occurrence of death events significantly impacted the performance of the machine learning models. Conclusions The study suggests that machine learning is a viable approach for developing non-time-based predictions regarding HIV deaths. Nevertheless, the limited inclusion of original studies necessitates additional multicenter studies for thorough validation.

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Section: Discussionmentioning

confidence: 99%

The predictive accuracy of machine learning for the risk of death in HIV patients: a systematic review and meta-analysis

Li,

Feng,

et al. 2024

BMC Infect Dis

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“…Np was also proposed as a biomarker for periprosthetic joint infections [55]. Increased Np levels were observed in subjects with tuberculosis and dropped after antituberculosis therapy, suggesting Np as the biomarker of infection activity [87]. IFN-γ mediated immune response plays a principal role in resistance to intracellular pathogens such as Mycobacterium tuberculosis [88].…”

Section: Neopterin and Bacterial And Viral Diseasesmentioning

confidence: 99%

Neopterin, the Cell-Mediated Immune Response Biomarker, in Inflammatory Periodontal Diseases: A Narrative Review of a More than Fifty Years Old Biomarker

2023

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Neopterin is a biomarker of the activation of cellular immunity. The purpose of this review is to summarise neopterin metabolism, methods of its detection, and its role in inflammation, focusing on periodontal inflammatory diseases. This derivative of guanosine is a non-enzymatic product of 7,8-dihydroneopterin oxidation caused by free radicals which protect activated macrophages from oxidative stress. Various methods, usually based on enzyme-linked immunosorbent essay, high-performance liquid chromatography, or radioimmunoassay were developed for the isolation of neopterin. A wide spectrum of diseases and conditions are known to affect neopterin levels, including cardiovascular, bacterial, viral, and degenerative diseases, as well as malignant tumours. Neopterin levels were found to increase in subjects with periodontitis, especially when the oral fluid and gingival crevicular fluid were evaluated. These findings confirm the role of activated macrophages and cellular immunity in periodontal inflammatory diseases. The gingival crevicular fluid and the oral fluid appear to be the most valuable biologic fluids for the evaluation of neopterin levels in periodontitis. For gingival crevicular fluid, neopterin can be determined as the concentration or the so-called total amount. Nonsurgical periodontal treatment was associated with a decrease in neopterin levels, but an increase was also reported, suggesting the possible role of macrophages in the resolution of the periodontal lesion.

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Decoding the hidden realm: Molecular pioneering unravelling osteoarticular tuberculosis diagnosis

Jeyaraman,

Ram

et al. 2024

Journal of Clinical Orthopaedics and Trauma

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Elevated neopterin in tuberculosis and co-infection with HIV and the effect of treatment: A systematic review, meta-analysis, and meta-regression

Cited by 4 publications

References 85 publications

The predictive accuracy of machine learning for the risk of death in HIV patients: a systematic review and meta-analysis

The predictive accuracy of machine learning for the risk of death in HIV patients: a systematic review and meta-analysis

Neopterin, the Cell-Mediated Immune Response Biomarker, in Inflammatory Periodontal Diseases: A Narrative Review of a More than Fifty Years Old Biomarker

Decoding the hidden realm: Molecular pioneering unravelling osteoarticular tuberculosis diagnosis

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