Immunisation with purified Coxiella burnetii phase I lipopolysaccharide confers partial protection in mice independently of co-administered adenovirus vectored vaccines.

Q fever is a highly infectious zoonosis caused by the Gram-negative bacterium Coxiella burnetii. The worldwide distribution of Q fever suggests a need for vaccines that are more efficacious, affordable, and does not induce severe adverse reactions in vaccine recipients with pre-existing immunity against Q fever. Potential Q fever vaccine antigens include lipopolysaccharide (LPS) and several C. burnetii surface proteins. Antibodies elicited by purified C. burnetii lipopolysaccharide (LPS) correlate with protection against Q fever, while antigens encoded by adenoviral vectored vaccines can induce cellular immune responses which aid clearing of intracellular pathogens. In the present study, the immunogenicity and the protection induced by adenoviral vectored constructs formulated with the addition of LPS were assessed. Multiple vaccine constructs encoding single or fusion antigens from C. burnetii were synthesised. The adenoviral vectored vaccine constructs alone elicited strong cellular immunity, but this response was not correlative with protection in mice. However, vaccination with LPS was significantly associated with lower weight loss post-bacterial challenge independent of co-administration with adenoviral vaccine constructs, supporting further vaccine development based on LPS.

Prerequisites, barriers and opportunities in care for Q-fever patients: a Delphi study among healthcare workers.

BACKGROUND: Q-fever is a zoonotic disease that can lead to illness, disability and death. This study aimed to provide insight into the perspectives of healthcare workers (HCWs) on prerequisites, barriers and opportunities in care for Q-fever patients. METHODS: A two-round online Delphi study was conducted among 94 Dutch HCWs involved in care for Q-fever patients. The questionnaires contained questions on prerequisites for high quality, barriers and facilitators in care, knowledge of Q-fever, and optimization of care. For multiple choice, ranking and Likert scale questions, frequencies were reported, while for rating and numerical questions, the median and interquartile range (IQR) were reported. RESULTS: The panel rated the care for Q-fever patients at a median score of 6/10 (IQR = 2). Sufficient knowledge of Q-fever among HCWs (36%), financial compensation of care (30%) and recognition of the disease by HCWs (26%) were considered the most important prerequisites for high quality care. A lack of knowledge was identified as the most important barrier (76%) and continuing medical education as the primary method for improving HCWs' knowledge (76%). HCWs rated their own knowledge at a median score of 8/10 (IQR = 1) and the general knowledge of other HCWs at a 5/10 (IQR = 2). According to HCWs, a median of eight healthcare providers (IQR = 4) should be involved in the care for Q-fever fatigue syndrome (QFS) and a median of seven (IQR = 5) in chronic Q-fever care. CONCLUSIONS: Ten years after the Dutch Q-fever epidemic, HCWs indicate that the long-term care for Q-fever patients leaves much room for improvement. Facilitation of reported prerequisites for high quality care, improved knowledge among HCWs, clearly defined roles and responsibilities, and guidance on how to support patients could possibly improve quality of care. These prerequisites may also improve care for patients with persisting symptoms due to other infectious diseases, such as COVID-19.

A protein-protein interaction map reveals that the Coxiella burnetii effector CirB inhibits host proteasome activity.

Coxiella burnetii is the etiological agent of the zoonotic disease Q fever, which is featured by its ability to replicate in acid vacuoles resembling the lysosomal network. One key virulence determinant of C. burnetii is the Dot/Icm system that transfers more than 150 effector proteins into host cells. These effectors function to construct the lysosome-like compartment permissive for bacterial replication, but the functions of most of these effectors remain elusive. In this study, we used an affinity tag purification mass spectrometry (AP-MS) approach to generate a C. burnetii-human protein-protein interaction (PPI) map involving 53 C. burnetii effectors and 3480 host proteins. This PPI map revealed that the C. burnetii effector CBU0425 (designated CirB) interacts with most subunits of the 20S core proteasome. We found that ectopically expressed CirB inhibits hydrolytic activity of the proteasome. In addition, overexpression of CirB in C. burnetii caused dramatic inhibition of proteasome activity in host cells, while knocking down CirB expression alleviated such inhibitory effects. Moreover, we showed that a region of CirB that spans residues 91-120 binds to the proteasome subunit PSMB5 (beta 5). Finally, PSMB5 knockdown promotes C. burnetii virulence, highlighting the importance of proteasome activity modulation during the course of C. burnetii infection.

Incidence and severity of SARS-CoV-2 infection in former Q fever patients as compared to the Dutch population, 2020-2021.

Surveillance data shows a geographical overlap between the early coronavirus disease 2019 (COVID-19) pandemic and the past Q fever epidemic (2007-2010) in the Netherlands. We investigated the relationship between past Q fever and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in 2020/2021, using a retrospective matched cohort study.In January 2021, former Q fever patients received a questionnaire on demographics, SARS-CoV-2 test results and related hospital/intensive care unit (ICU) admissions. SARS-CoV-2 incidence with 95% confidence intervals (CI) in former Q fever patients and standardised incidence ratios (SIR) to compare to the age-standardised SARS-CoV-2 incidence in the general regional population were calculated.Among 890 former Q fever patients (response rate: 68%), 66 had a PCR-confirmed SARS-CoV-2 infection. Of these, nine (14%) were hospitalised and two (3%) were admitted to ICU. From February to June 2020 the SARS-CoV-2 incidence was 1573/100 000 (95% CI 749-2397) in former Q fever patients and 695/100 000 in the general population (SIR 2.26; 95% CI 1.24-3.80). The incidence was not significantly higher from September 2020 to February 2021.We found no sufficient evidence for a difference in SARS-CoV-2 incidence or an increased severity in former Q fever patients vs. the general population during the period with widespread SARS-CoV-2 testing availability (September 2020-February 2021). This indicates that former Q fever patients do not have a higher risk of SARS-CoV-2 infection.

Identifying disrupted biological factors and patient-tailored interventions for chronic fatigue in adolescents and young adults with Q-Fever Fatigue Syndrome, Chronic Fatigue Syndrome and Juvenile Idiopathic Arthritis (QFS-study): study protocol for a randomized controlled trial with single-subject experimental case series design (preprint)

Background: Chronic fatigue with a debilitating effect on daily life is a frequently reported symptom among adolescents and young adults with a history of Q-fever infection (QFS). Persisting fatigue after infection may have a biological origin with psychological and social factors contributing to the disease phenotype. This is consistent with the biopsychosocial framework, which considers fatigue to be the result of a complex interaction between biological, psychological and social factors. In line, similar manifestations of chronic fatigue are observed in Chronic Fatigue Syndrome/Myalgic Encephalomyelitis (CFS/ME) and Juvenile Idiopathic Arthritis (JIA). Cognitive behavioural therapy is often recommended as treatment for chronic fatigue, considering its’ effectiveness on the group-level. However, not everybody benefits on the individual level. More treatment success at the individual level might be achieved with patient-tailored treatments that incorporate the biopsychosocial framework. Methods In addition to biological assessments of blood, stool, saliva, and hair, the QFS-study consists of a randomized controlled trial (RCT) in which a single-subject experimental case series ( N = 1 ) design will be implemented using Experience Sampling Methodology in fatigued adolescents and young adults with QFS, CFS/ME and JIA (aged 12–29). With the RCT design, the effectiveness of patient-tailored PROfeel lifestyle advices will be compared against generic dietary advices in reducing fatigue severity at the group-level. Pre-post analyses will be conducted to determine relevance of intervention order. By means of the N = 1 design, effectiveness of both advices will be measured at the individual level. Discussion The QFS-study is a comprehensive study exploring disrupted biological factors and patient-tailored lifestyle advices as intervention in adolescent and young adults with QFS and similar manifestations of chronic fatigue. Practical or operational issues are expected during the study, but can be overcome through innovative study design, statistical approaches, and recruitment strategies. Ultimately, the study aims to contribute to biological research and (personalized) treatment in QFS and similar manifestations of chronic fatigue. Trial registration: Trial NL8789 (www.trialregister.nl). Registered July 21, 2020.

Recurrent culture-negative endocarditis and osteomyelitis caused by Q fever in Iran, a case report (preprint)

Background: Endocarditis is an infection of the endocardial layer of the heart known as fatal infection. Gold standard diagnosis of infectious endocarditis is blood culture, which in some cases can be negative. In blood culture-negative endocarditis, the early diagnosis, and treatment are much harder, which can increase morbidities and also mortality. Case presentation: In this case report we are presenting a patient with intermittent fever for three months with a history of aortic and pulmonary valve replacement and also recurrent blood culture-negative endocarditis. According to the pandemic situation, we checked the Covid-19 PCR and also performed a chest computed tomography (CT) -scan, which both were negative and did not represent any pathologic conditions. Other examinations such as transesophageal echocardiography (TEE) and blood cultures were all normal and the only abnormal finding we had was a positron emission tomography (PET) - CT scan with endocarditis and sternum osteomyelitis evidence. Conclusion: After several evaluations based on endemic epidemiology, the Real-time PCR and IFA (IgG phase I; 1:16384, IgG phase II; 1:16384) were positive for Q fever and the patient responded to the proper doxycycline and hydroxychloroquine treatment.

[For Once Not Corona Virus - an Uncommon Cause of Fever and Hepatitis]. / Für einmal nicht Coronavirus ­ Eine nicht alltägliche Ursache für Fieber und Hepatitis.

For Once Not Corona Virus - an Uncommon Cause of Fever and Hepatitis Abstract. Our case reports acute Q fever as uncommon cause of fever, typically accompanied by pneumonia and/or hepatitis. It is caused by Coxiella burnetii, a bacterium which is generally hosted by live stock and affects humans by inhaling aerosols of the animals' excrements. If detected, it may be treated effectively. It should be considered in patients living in a typical environment or with a typical history. The route of our patient's infection remains unclear since he plausibly denied contact with any animals.

Early Detection of COVID-19 Epidemic Using Search Engine Query Data (preprint)

Background: Early warnings of emerging infectious disease are crucial to prevent epidemics. However, in the early stage of the COVID-19 pandemic, traditional infectious disease surveillance failed to deliver a warning alert. The aim of this work is to develop search-engine-based surveillance methods for the early warning and prediction of COVID-19 outbreaks. Methods: By using more than 444 million Baidu search queries from China as training set, we collected 32 keywords from the Baidu Search Index that may related to COVID-19 outbreak from 18 December 2019 to 11 February 2020. The Beijing Xinfadi outbreak from 30 May 2020 to 30 July 2020 was used as independent test set. A multiple linear regression was applied to model the relationship between the daily query frequencies of keywords and the daily new cases. Findings: Our results show that 11 keywords in search queries were highly correlated to the daily numbers of confirmed cases (r =0.96, P <0.01). An abnormal initial peak (1.46 times the normal volume) in queries appeared on 31 December 2019, which could have served as an early warning signal for an outbreak. Of particular concern, on this day, the volume of the query “Wuhan Seafood Market” increased by over 240 times (from 10 to 2410), the volume of the query “Wuhan outbreak” increased by over 622 times (from 7 to 4359), and 17.5% of China’s query volume originated from Hubei Province, 51.15% of which was from Wuhan city. The quantitative model using four keywords (“Epidemic”, “Masks”, “Coronavirus” and “Clustered pneumonia”) successfully predicted the daily numbers of cases for the next two days, and detected an early signal during the Beijing Xinfadi outbreak (R2 =0.80). Interpretation: Our study demonstrates the ability of search engine query data to detect COVID-19 outbreaks, and suggests that abnormalities in query volume can serve as early warning signals.

Hepatic Manifestations of Nonhepatotropic Infectious Agents Including Severe Acute Respiratory Syndrome Coronavirus-2, Adenovirus, Herpes Simplex Virus, and Coxiella burnetii.

Nonhepatotropic viruses such as adenovirus, herpes simplex virus, flaviviruses, filoviruses, and human herpes virus, and bacteria such as Coxiella burnetii, can cause liver injury mimicking acute hepatitis. Most of these organisms cause a self-limited infection. However, in immunocompromised patients, they can cause severe hepatitis or in some cases fulminant hepatic failure requiring an urgent liver transplant. Hepatic dysfunction is also commonly seen in patients with severe acute respiratory syndrome coronavirus-2 infection. Patients with preexisting liver diseases are likely at risk for severe coronavirus disease 2019 (COVID-19) and may be associated with poor outcomes.

Classification of COVID-19 X-ray Images Using a Combination of Deep and Handcrafted Features (preprint)

Coronavirus Disease 2019 (COVID-19) demonstrated the need for accurate and fast diagnosis methods for emergent viral diseases. Soon after the emergence of COVID-19, medical practitioners used X-ray and computed tomography (CT) images of patients' lungs to detect COVID-19. Machine learning methods are capable of improving the identification accuracy of COVID-19 in X-ray and CT images, delivering near real-time results, while alleviating the burden on medical practitioners. In this work, we demonstrate the efficacy of a support vector machine (SVM) classifier, trained with a combination of deep convolutional and handcrafted features extracted from X-ray chest scans. We use this combination of features to discriminate between healthy, common pneumonia, and COVID-19 patients. The performance of the combined feature approach is compared with a standard convolutional neural network (CNN) and the SVM trained with handcrafted features. We find that combining the features in our novel framework improves the performance of the classification task compared to the independent application of convolutional and handcrafted features. Specifically, we achieve an accuracy of 0.988 in the classification task with our combined approach compared to 0.963 and 0.983 accuracy for the handcrafted features with SVM and CNN respectively.

Quantum Machine Learning for Drug Discovery (preprint)

The growing public and private datasets focused on small molecules screened against biological targets or whole organisms 1 provides a wealth of drug discovery relevant data. Increasingly this is used to create machine learning models which can be used for enabling target-based design 2-4, predict on- or off-target effects and create scoring functions 5,6. This is matched by the availability of machine learning algorithms such as Support Vector Machines (SVM) and Deep Neural Networks (DNN) that are computationally expensive to perform on very large datasets and thousands of molecular descriptors. Quantum computer (QC) algorithms have been proposed to offer an approach to accelerate quantum machine learning over classical computer (CC) algorithms, however with significant limitations. In the case of cheminformatics, one of the challenges to overcome is the need for compression of large numbers of molecular descriptors for use on QC. Here we show how to achieve compression with datasets using hundreds of molecules (SARS-CoV-2) to hundreds of thousands (whole cell screening datasets for plague and M. tuberculosis) with SVM and data re-uploading classifier (a DNN equivalent algorithm) on a QC benchmarked against CC and hybrid approaches. This illustrates a quantum advantage for drug discovery to build upon in future.

Transmission potential and forecasting of the number of Coronavirus disease 2019 cases in Hubei Province, China (preprint)

Background: Coronavirus disease 2019 (COVID-19) was first reported in Wuhan, Hubei Province, China. We aimed to describe the temporal and spatial distribution and the transmission dynamics of COVID-19 and to assess whether a hybrid model can forecast the trend of COVID-19 in Hubei Province.Method: The data of COVID-19 cases were obtained from the website of the Chinese Center for Disease Control and Prevention, whereas the data on the resident population were obtained from the website of the Hubei Provincial Bureau of Statistics. The temporal and spatial distribution and the transmission dynamics of COVID-19 were described. A combination of an autoregressive integrated moving average (ARIMA) and a support vector machine (SVM) was constructed to forecast the trend of COVID-19.Results: A total of 56,062 confirmed COVID-19 cases, which were mainly concentrated in Wuhan, were reported from 16 January to 16 March 2020 in Hubei Province. The daily number of confirmed cases exponentially increased to 3,156 before 4 February 2020, fluctuated on an upward trend to 4,823 before 13 February 2020, and then markedly decreased to one case after 16 March 2020. The highest mean reproduction number R(t) of 9.48 was recorded on 16 January 2020, after which it decreased to 2.15 on 2 February 2020 and further dropped to less than one on 13 February 2020. In the modelling stage, the mean square error, mean absolute error and mean absolute percentage error of the hybrid ARIMA–SVM model decreased by 98.59%, 89.19% and 89.68%, and those of SVM decreased by 98.58%, 87.71% and 88.94% compared with the ARIMA model. Similar results were obtained in the forecasting stage.Conclusion: Public health interventions resulted in the terminal phase of COVID-19 in Hubei Province. The hybrid ARIMA–SVM model may be a reliable tool for forecasting the trend of the COVID-19 epidemic.