Influenza: incidence, symptoms and treatment

Forecasting influenza hemagglutinin mutations through the lens of anomaly detection

The influenza virus hemagglutinin is an important part of the virus attachment to the host cells. The hemagglutinin proteins are one of the genetic regions of the virus with a high potential for mutations. Due to the importance of predicting mutations in producing effective and low-cost vaccines, solutions that attempt to approach this problem have recently gained significant attention. A historical record of mutations has been used to train predictive models in such solutions. However, the imbalance between mutations and preserved proteins is a big challenge for the development of such models that need to be addressed. Here, we propose to tackle this challenge through anomaly detection (AD). AD is a well-established field in Machine Learning (ML) that tries to distinguish unseen anomalies from normal patterns using only normal training samples. By considering mutations as anomalous behavior, we could benefit existing rich solutions in this field that have emerged recently. Such methods also fit the problem setup of extreme imbalance between the number of unmutated vs. mutated training samples. Motivated by this formulation, our method tries to find a compact representation for unmutated samples while forcing anomalies to be separated from the normal ones. This helps the model to learn a shared unique representation between normal training samples as much as possible, which improves the discernibility and detectability of mutated samples from the unmutated ones at the test time. We conduct a large number of experiments on four publicly available datasets, consisting of three different hemagglutinin protein datasets, and one SARS-CoV-2 dataset, and show the effectiveness of our method through different standard criteria.

Ensemble learning-based early detection of influenza disease

Across the world, the seasonal disease influenza is a respiratory illness that impacts all age groups in many ways. Its symptoms are fever, chills, aches, pains, headaches, fatigue, cough, and weakness. Seasonal influenza can cause mild to severe illness and lead to death at times. The task of early detection of influenza is an important research area these days. Various studies show that machine learning techniques have attracted many researchers' attention to the early detection of influenza disease. In this paper, early detection of Influenza disease among all age groups is done using various machine learning techniques. Influenza Research Database and the Human Surveillance Records data sets are used. Data analysis is undertaken, and ensemble-based stacked algorithms are implemented on the whole data set. The performance of different models has been evaluated using different performance metrics. Overall, the study proposes efficient machine learning models that can be implemented to provide a cheaper and quicker diagnostic tool for detecting influenza.

Acute Influenza B Infection Presenting as Cardiac Tamponade: A Case Report

Influenza A and B acute infections usually affect primarily the respiratory system. In rare cases, however, the cardiovascular system is also compromised either via the direct effect of the virus or via the worsening of preexisting cardiac conditions. We present a rare case of acute Influenza B infection presenting as pericardial effusion and cardiac tamponade.

A healthy 32-year-old female was presented to the emergency room with influenza-like symptoms for four days, where she was monitored for a few hours and was subsequently discharged to home after testing positive for Influenza B by polymerase chain reaction (PCR). On the fifth day, she returned to the emergency room with worsening symptoms, primarily exertional dyspnea. She was hypotensive and tachycardic and temporarily improved with fluid administration. She was transferred to the intensive care unit, where a bedside point of care ultrasound (POCUS) and later a formal transthoracic echocardiogram revealed that she had pericardial effusion with sonographic signs of cardiac tamponade. Emergent pericardiocentesis was performed and resulted in hemodynamic and symptomatic improvement. The pericardial drain that was initially left in place and continued to drain pericardial fluid (700 ccs in total), was removed 3 days later, after echocardiographic confirmation of the resolution of the pericardial effusion. She completed a five-day course of Oseltamivir and was subsequently discharged home safely.

In summary, our case describes an acute Influenza B infection that was complicated by pericardial effusion and cardiac tamponade. It also highlights the importance of bedside POCUS and echocardiography in the early diagnosis and treatment of cardiac tamponade cases, frequently with pericardiocentesis as in our case.

Differential Diagnosis of the Etiologies of Bacterial and Viral Infections Using Infrared Microscopy of Peripheral Human Blood Samples and Multivariate Analysis

Human viral and bacterial infections are responsible for a variety of diseases that are still the main causes of death and economic burden for society across the globe. Despite the different responses of the immune system to these infections, some of them have similar symptoms, such as fever, sneezing, inflammation, vomiting, diarrhea, and fatigue. Thus, physicians usually encounter difficulties in distinguishing between viral and bacterial infections on the basis of these symptoms. Rapid identification of the etiology of infection is highly important for effective treatment and can save lives in some cases. The current methods used for the identification of the nature of the infection are mainly based on growing the infective agent in culture, which is a time-consuming (over 24 h) and usually expensive process. The main objective of this study was to evaluate the potential of the mid-infrared spectroscopic method for rapid and reliable identification of bacterial and viral infections based on simple peripheral blood samples. For this purpose, white blood cells (WBCs) and plasma were isolated from the peripheral blood samples of patients with confirmed viral or bacterial infections. The obtained spectra were analyzed by multivariate analysis: principle component analysis (PCA) followed by linear discriminant analysis (LDA), to identify the infectious agent type as bacterial or viral in a time span of about 1 h after the collection of the blood sample. Our preliminary results showed that it is possible to determine the infectious agent with high success rates of 82% for sensitivity and 80% for specificity, based on the WBC data.

Molecular modeling and lead design of substituted zanamivir derivatives as potent anti-influenza drugs

Background

Influenza virus spreads infection by two main surface glycoproteins, namely hemagglutinin (HA) and neuraminidase (NA). NA cleaves the sialic acid receptors eventually releasing newly formed virus particles which then invade new cells. Inhibition of NA could limit the replication of virus to one round which is insufficient to cause the disease.

Results

An experimentally reported series of acylguanidine zanamivir derivatives was used to develop GQSAR model targeting NA in different strains of influenza virus, H1N1 and H3N2. A combinatorial library was developed and their inhibitory activities were predicted using the GQSAR model.

Conclusion

The top leads were analyzed by docking which revealed the binding modes of these inhibitors in the active site of NA (150-loop). The top compound (AMA) was selected for carrying out molecular dynamics simulations for 15 ns which provided insights into the time dependent dynamics of the designed leads. AMA possessed a docking score of −8.26 Kcal/mol with H1N1 strain and −7.00 Kcal/mol with H3N2 strain. Ligand-bound complexes of both H1N1 and H3N2 were observed to be stable for 11 ns and 7 ns respectively. ADME descriptors were also calculated to study the pharmacokinetic properties of AMA which revealed its drug-like properties.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1374-1) contains supplementary material, which is available to authorized users.