A global call for talaromycosis to be recognised as a neglected tropical disease

Narayanasamy, Shanti; Dat, Vu Quoc; Thanh, Nguyen Tat; Ly, Vo Trieu; Chan, Jasper Fuk‐Woo; Yuen, Kwok‐Yung; Ning, Chuanyi; Liang, Hao; Li, Linghua; Chowdhary, Anuradha; Youngchim, Sirida; Supparatpinyo, Khuanchai; Aung, Ne Myo; Hanson, Josh; Andrianopoulos, Alex; Dougherty, John H.; Govender, Nelesh P.; Denning, David W.; Chiller, Tom; Thwaites, Guy; Doorn, H. Rogier van; Perfect, John R.; Le, Thuy

doi:10.1016/s2214-109x(21)00350-8

Cited by 77 publications

(77 citation statements)

References 42 publications

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“…Thus, talaromycosis is a tropical infectious disease with high morbidity and mortality and is a serious threat to regional health. Thuy Le, Linghua Li, and other experts call for talaromycosis to be recognized as a neglected tropical disease that urgently needs to be taken seriously despite the perpetuation of the condition by a cycle of poverty, stigma, and global neglect [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Machine learning-based in-hospital mortality prediction of HIV/AIDS patients with Talaromyces marneffei infection in Guangxi, China

et al. 2022

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Objective Talaromycosis is a serious regional disease endemic in Southeast Asia. In China, Talaromyces marneffei (T. marneffei) infections is mainly concentrated in the southern region, especially in Guangxi, and cause considerable in-hospital mortality in HIV-infected individuals. Currently, the factors that influence in-hospital death of HIV/AIDS patients with T. marneffei infection are not completely clear. Existing machine learning techniques can be used to develop a predictive model to identify relevant prognostic factors to predict death and appears to be essential to reducing in-hospital mortality. Methods We prospectively enrolled HIV/AIDS patients with talaromycosis in the Fourth People’s Hospital of Nanning, Guangxi, from January 2012 to June 2019. Clinical features were selected and used to train four different machine learning models (logistic regression, XGBoost, KNN, and SVM) to predict the treatment outcome of hospitalized patients, and 30% internal validation was used to evaluate the performance of models. Machine learning model performance was assessed according to a range of learning metrics, including area under the receiver operating characteristic curve (AUC). The SHapley Additive exPlanations (SHAP) tool was used to explain the model. Results A total of 1927 HIV/AIDS patients with T. marneffei infection were included. The average in-hospital mortality rate was 13.3% (256/1927) from 2012 to 2019. The most common complications/coinfections were pneumonia (68.9%), followed by oral candida (47.5%), and tuberculosis (40.6%). Deceased patients showed higher CD4/CD8 ratios, aspartate aminotransferase (AST) levels, creatinine levels, urea levels, uric acid (UA) levels, lactate dehydrogenase (LDH) levels, total bilirubin levels, creatine kinase levels, white blood-cell counts (WBC) counts, neutrophil counts, procaicltonin levels and C-reactive protein (CRP) levels and lower CD3+ T-cell count, CD8+ T-cell count, and lymphocyte counts, platelet (PLT), high-density lipoprotein cholesterol (HDL), hemoglobin (Hb) levels than those of surviving patients. The predictive XGBoost model exhibited 0.71 sensitivity, 0.99 specificity, and 0.97 AUC in the training dataset, and our outcome prediction model provided robust discrimination in the testing dataset, showing an AUC of 0.90 with 0.69 sensitivity and 0.96 specificity. The other three models were ruled out due to poor performance. Septic shock and respiratory failure were the most important predictive features, followed by uric acid, urea, platelets, and the AST/ALT ratios. Conclusion The XGBoost machine learning model is a good predictor in the hospitalization outcome of HIV/AIDS patients with T. marneffei infection. The model may have potential application in mortality prediction and high-risk factor identification in the talaromycosis population.

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Section: Introductionmentioning

confidence: 99%

Machine learning-based in-hospital mortality prediction of HIV/AIDS patients with Talaromyces marneffei infection in Guangxi, China

et al. 2022

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“…Despite these considerable challenges and unanswered questions, recent improvements in diagnostics and their increased availability in endemic areas are promising signs. For example, the development of the enzyme-linked immunosorbent assay for diagnosis of disseminated Histoplasmosis and it simplementation in Latin America are considerable steps forward in recognizing and fighting infection [ 97 ], together with creating awareness of the problem, such as the global call for talaromycosis to be recognized as a neglected tropical disease [ 98 ]. With commitment from funders, policy makers, researchers and industry, the control of these endemic dimorphic pathogens is feasible and thereby protecting vulnerable populations.…”

Section: Conclusion Open Questions and Perspectivesmentioning

confidence: 99%

Key thermally dimorphic fungal pathogens: shaping host immunity

2022

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Exposure to fungal pathogens from the environment is inevitable and with the number of at-risk populations increasing, the prevalence of invasive fungal infection is on the rise. An interesting group of fungal organisms known as thermally dimorphic fungi predominantly infects immunocompromised individuals. These potential pathogens are intriguing in that they survive in the environment in one form, mycelial phase, but when entering the host, they are triggered by the change in temperature to switch to a new pathogenic form. Considering the growing prevalence of infection and the need for improved diagnostic and treatment approaches, studies identifying key components of fungal recognition and the innate immune response to these pathogens will significantly contribute to our understanding of disease progression. This review focuses on key endemic dimorphic fungal pathogens that significantly contribute to disease, including Histoplasma , Coccidioides and Talaromyces species. We briefly describe their prevalence, route of infection and clinical presentation. Importantly, we have reviewed the major fungal cell wall components of these dimorphic fungi, the host pattern recognition receptors responsible for recognition and important innate immune responses supporting adaptive immunity and fungal clearance or the failure thereof.

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“…However, fungal morphogenesis appears to be a crucial virulence factor in the establishment of infection [13]. Evidence suggests that aerosolized infectious particles (conidia) from environmental disturbances, especially in tropical monsoon seasons, are acquired via inhalation [14][15][16][17]. After inhalation, the sizes of conidia (2 to 3 µm in diameter) allow them to infiltrate deeply into the lung alveoli.…”

Section: Introductionmentioning

confidence: 99%

Talaromyces marneffei Infection: Virulence, Intracellular Lifestyle and Host Defense Mechanisms

Pruksaphon

Nosanchuk

Ratanabanangkoon

et al. 2022

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Talaromycosis (Penicilliosis) is an opportunistic mycosis caused by the thermally dimorphic fungus Talaromyces (Penicillium) marneffei. Similar to other major causes of systemic mycoses, the extent of disease and outcomes are the results of complex interactions between this opportunistic human pathogen and a host’s immune response. This review will highlight the current knowledge regarding the dynamic interaction between T. marneffei and mammalian hosts, particularly highlighting important aspects of virulence factors, intracellular lifestyle and the mechanisms of immune defense as well as the strategies of the pathogen for manipulating and evading host immune cells.

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A global call for talaromycosis to be recognised as a neglected tropical disease

Cited by 77 publications

References 42 publications

Machine learning-based in-hospital mortality prediction of HIV/AIDS patients with Talaromyces marneffei infection in Guangxi, China

Machine learning-based in-hospital mortality prediction of HIV/AIDS patients with Talaromyces marneffei infection in Guangxi, China

Key thermally dimorphic fungal pathogens: shaping host immunity

Talaromyces marneffei Infection: Virulence, Intracellular Lifestyle and Host Defense Mechanisms

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