Cross Sectional Study and Risk Factors Analysis of Francisella tularensis in Soil Samples in Punjab Province of Pakistan

Muhammad, Javed; Rabbani, Masood; Shabbir, Muhammad Zubair; Muhammad, Khushi; Ghori, Muhammad Taslim; Chaudhry, Haroon Rashid; Hassnain, Zia Ul; Jamil, Tariq; Abbas, Tariq; Chaudhry, Muhammad Hamid; Haisem-Ur-Rasool, Muhammad; Ali, Muhammad Asad; Nisar, Muhammad Imran; Kirimanjeswara, Girish S.; Jayarao, Bhushan M.

doi:10.3389/fcimb.2019.00089

Cited by 9 publications

(9 citation statements)

References 58 publications

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“…Similar findings have been reported in a study from Sudan, where F. tularensis was isolated from blood culture in brain heart infusion [24]. A similar case of F. tularensis isolation has been reported from haemorrhagic pleural effusion and blood specimen in a middle aged man from Norway with sepsis [25]. F. tularensis is known as a potential bioterrorism agent due to its high virulence and low infective dose [23].…”

Section: Discussionsupporting

confidence: 83%

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First case report of post-operative infection due to Francisella tularensis after cardiac surgery

Ali

Noor‐ul‐Amin

Arif

2019

Access Microbiology

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Background. Francisella tularensis is a rare zoonotic bacterium that spreads sporadically by various routes, including infected arthropod bites, ingestion of contaminated water and inhalation of contaminated dust. However, its occurrence in postoperative chest infection has never been reported. Pathogen isolation, serology and molecular detection methods are commonly used for the diagnosis of tularaemia. Case presentation. We present the first case report of the isolation of F. tularensis from a patient with a chest infection (a boy in his teens) following cardiac surgery for closure of a ventral septal defect. It was isolated on blood and chocolate agar on the third day after the subculture of drain fluid collected in a blood culture bottle incubated in Bact T/Alert 3-D (bioMerieux, France). The organism was identified as F. tularensis by Vitek GN ID Cards (Vitek 2 Compact, bioMerieux, France). The patient made a smooth recovery with antibiotic therapy. Conclusion. F. tularensis can cause post-operative infection, especially in patients with a rural background.

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Section: Discussionsupporting

confidence: 83%

“…A similar case of F. tularensis isolation has been reported from haemorrhagic pleural effusion and blood specimen in a middle aged man from Norway with sepsis [25].…”

Section: Discussionmentioning

confidence: 99%

First case report of post-operative infection due to Francisella tularensis after cardiac surgery

Ali

Noor‐ul‐Amin

Arif

2019

Access Microbiology

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“…In addition, 6.22% of domestic animals were found to be positive in the seroconversion test. Francisella tularensis detection over a large geographic area indicated its extension to the enzootic range ( 60 ). In addition, the first report of F. tularensis isolation from a patient suffering from lung infection was reported in 2019 ( 61 ).…”

Section: Resultsmentioning

confidence: 99%

A Review on Important Zoonotic Bacterial Tick-Borne Diseases in the Eastern Mediterranean Region

Behzadi¹,

Mostafavi²,

Rohani³

et al. 2022

JAD

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Background: Zoonotic diseases as health concerns worldwide account for more than half of the emerging infectious diseases. Arachnids are powerful vectors to transmit several diseases to humans. Additionally, these emerging zoonotic diseases have been a considerable health threat in the Eastern Mediterranean Region of the WHO (EMRO) due to the large population living close to farms and international trade with nearby countries. Methods: This review study is based on the reported three tick-borne diseases, Lyme disease, Tularemia, and Q fever, from Iran and other EMRO countries. To this end, we searched PubMed central, ISI web of Science, and Google with the related keywords in English at any time. The reported data are then sorted by countries for each disease. Results: According to the published data, 15 countries in the region have one/more emerging infectious diseases. Q fever has been the most frequent infection in EMRO countries, while Lyme was less recorded. Furthermore, Iran is among the countries with documented history of all three investigated diseases. Conclusion: Tick-borne disease is popular among EMRO countries, indicating that they have natural conditions for infections in animals and humans. It appears necessary to develop a disease management strategy and control programs against tick-borne diseases (TBDs). Moreover, the disease-resistant animal could be bred instead of susceptible livestock. Therefore, research studies to control TBDs should be regarded as a top priority plan.

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“…Our analysis further reveals that potassium is the most noteworthy feature for the presence of Coxiella in soil. Some fantastic works 9,22,23,59 prove that the prevalence of various pathogens like B. anthracis, C. burnetii, and F. tularensis positively correlates to potassium in the soil. The next essential features that improve the likelihood of persistence of the pathogenic bacteria are chromium, cadmium, nitrogen, organic matter, and soluble salts.…”

Section: Discussionmentioning

confidence: 99%

Two phase feature-ranking for new soil dataset for Coxiella burnetii persistence and classification using machine learning models

Faried

Khan

Tahir

et al. 2023

Sci Rep

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Coxiella burnetii (Cb) is a hardy, stealth bacterial pathogen lethal for humans and animals. Its tremendous resistance to the environment, ease of propagation, and incredibly low infectious dosage make it an attractive organism for biowarfare. Current research on the classification of Coxiella and features influencing its presence in the soil is generally confined to statistical techniques. Machine learning other than traditional approaches can help us better predict epidemiological modeling for this soil-based pathogen of public significance. We developed a two-phase feature-ranking technique for the pathogen on a new soil feature dataset. The feature ranking applies methods such as ReliefF (RLF), OneR (ONR), and correlation (CR) for the first phase and a combination of techniques utilizing weighted scores to determine the final soil attribute ranks in the second phase. Different classification methods such as Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Logistic Regression (LR), and Multi-Layer Perceptron (MLP) have been utilized for the classification of soil attribute dataset for Coxiella positive and negative soils. The feature-ranking methods established that potassium, chromium, cadmium, nitrogen, organic matter, and soluble salts are the most significant attributes. At the same time, manganese, clay, phosphorous, copper, and lead are the least contributing soil features for the prevalence of the bacteria. However, potassium is the most influential feature, and manganese is the least significant soil feature. The attribute ranking using RLF generates the most promising results among the ranking methods by generating an accuracy of 80.85% for MLP, 79.79% for LR, and 79.8% for LDA. Overall, SVM and MLP are the best-performing classifiers, where SVM yields an accuracy of 82.98% and 81.91% for attribute ranking by CR and RLF; and MLP generates an accuracy of 76.60% for ONR. Thus, machine models can help us better understand the environment, assisting in the prevalence of bacteria and decreasing the chances of false classification. Subsequently, this can assist in controlling epidemics and alleviating the devastating effect on the socio-economics of society.

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Cross Sectional Study and Risk Factors Analysis of Francisella tularensis in Soil Samples in Punjab Province of Pakistan

Cited by 9 publications

References 58 publications

First case report of post-operative infection due to Francisella tularensis after cardiac surgery

First case report of post-operative infection due to Francisella tularensis after cardiac surgery

A Review on Important Zoonotic Bacterial Tick-Borne Diseases in the Eastern Mediterranean Region

Two phase feature-ranking for new soil dataset for Coxiella burnetii persistence and classification using machine learning models

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