Diagnostic significance of C-reactive protein and hematological parameters in acute toxoplasmosis

Sandri, Vanessa; Gonçalves, Itamar Luís; Neves, Gustavo Machado das; Paraboni, Marisa Lúcia Romani

doi:10.1007/s12639-020-01262-0

Cited by 3 publications

(2 citation statements)

References 50 publications

(52 reference statements)

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“…CRP values can vary in ehrlichiosis, legionnaires’ disease, and rabies infection, whereas evidence on fungal coccidioidomycosis seems contrasted since low CRP values have been reported in immunocompetent cases—yet, such low values may have occurred due to the immunocompromised state [ 1013 , 1014 , 1015 , 1016 , 1017 , 1018 , 1019 ]. Data on toxoplasmosis, again, vary, as significantly high CRP values have been reported after infection, but a study revealed no significant difference between cases and controls [ 1020 , 1021 ]. Increased CRP values have also been reported in the acute phase of cholera, as seen in old literature data [ 1022 ].…”

Section: Current Evidence On C-reactive Protein and Potential Conditionsmentioning

confidence: 99%

C-Reactive Protein: Pathophysiology, Diagnosis, False Test Results and a Novel Diagnostic Algorithm for Clinicians

Mouliou

2023

Diseases

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The current literature provides a body of evidence on C-Reactive Protein (CRP) and its potential role in inflammation. However, most pieces of evidence are sparse and controversial. This critical state-of-the-art monography provides all the crucial data on the potential biochemical properties of the protein, along with further evidence on its potential pathobiology, both for its pentameric and monomeric forms, including information for its ligands as well as the possible function of autoantibodies against the protein. Furthermore, the current evidence on its potential utility as a biomarker of various diseases is presented, of all cardiovascular, respiratory, hepatobiliary, gastrointestinal, pancreatic, renal, gynecological, andrological, dental, oral, otorhinolaryngological, ophthalmological, dermatological, musculoskeletal, neurological, mental, splenic, thyroid conditions, as well as infections, autoimmune-supposed conditions and neoplasms, including other possible factors that have been linked with elevated concentrations of that protein. Moreover, data on molecular diagnostics on CRP are discussed, and possible etiologies of false test results are highlighted. Additionally, this review evaluates all current pieces of evidence on CRP and systemic inflammation, and highlights future goals. Finally, a novel diagnostic algorithm to carefully assess the CRP level for a precise diagnosis of a medical condition is illustrated.

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Section: Current Evidence On C-reactive Protein and Potential Conditionsmentioning

confidence: 99%

C-Reactive Protein: Pathophysiology, Diagnosis, False Test Results and a Novel Diagnostic Algorithm for Clinicians

Mouliou

2023

Diseases

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“…The presence of cancer in this procedure is regression process, the techniques and algorithms are based on a model design in new machine learning. In the proposed model the unseen data provides a good exact result on testing stages [20]. The main three stages of machine learning that will be applied to available datasets are preprocessing of data, selection of features or its extraction, and classification [21].…”

Section: Survivor Of Cancermentioning

confidence: 99%

A Feature Fusion Based Hybrid Approach for Breast Cancer Classification

Iftikhar

Amin

Abbas

2022

JCBI

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A detected type of cancer is breast cancer commonly in women. According to some estimate one in nine women is diagnosed with breast cancer. It is unfortunate that due to a lack of proper facilities, the diagnosis of breast cancer in patients is being delayed, which is leading to an increase in the possible death rate. Many different statistical methods and Machine Learning algorithms are often employed in the study to make breast cancer detection more accurate. Machine learning (ML) has allowed doctors to achieve remarkable results, and healthcare is using ML-based models to detect breast cancer in women. This allows analyzing the healthcare data and uses the traditional computer-aided detection (CAD) to assess breast cancer. Machine learning has become an accepted clinical practice and allows doctors to evaluate the ML model to detect breasts at an early stage. A major aim is to diagnose patients with breast cancer by analyzing the data of patients and classifying them into two categories, having diagnosis results as Benign "B" or Malignant “M”. In this study different machine learning algorithms are used to classify cancer as either its malignant or benign. The Kaggle data set was used for applying these algorithms to get the best accuracy. MLP is more efficient and accurate algorithm to classify the breast tumor. And here also fitted the matthews_corrcoef for MLP is 0.89% and accuracy score for the random forest is 0.94%.

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Correlations between the degree of infection by wild strain of Toxoplasma gondii in vitro and porcine hematological parameters

Barros Oliveira,

Paulino da Silva,

de Lima

et al. 2024

Experimental Parasitology

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Diagnostic significance of C-reactive protein and hematological parameters in acute toxoplasmosis

Cited by 3 publications

References 50 publications

C-Reactive Protein: Pathophysiology, Diagnosis, False Test Results and a Novel Diagnostic Algorithm for Clinicians

C-Reactive Protein: Pathophysiology, Diagnosis, False Test Results and a Novel Diagnostic Algorithm for Clinicians

A Feature Fusion Based Hybrid Approach for Breast Cancer Classification

Correlations between the degree of infection by wild strain of Toxoplasma gondii in vitro and porcine hematological parameters

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