Flu Detection Research Articles

Conducting Paper Based on Few-Layered Hydrogen-Substituted Graphdiyne for Swine Flu Detection

Conducting Paper Based on Few-Layered Hydrogen-Substituted Graphdiyne for Swine Flu Detection

Nanoengineered phosphorus doped graphitic carbon nitride based ultrasensitive biosensing platform for Swine flu detection.

In the present study, we developed an amino-polyindole modified phosphorus doped graphitic carbon nitride nanomaterial (APIN/P-g-C3N4) based immunosensing biochip for Serum amyloid A (SAA) biomarker towards early diagnosis of Swine flu. The P-g-C3N4 was synthesis via polycondensation and functionalized with APIN. Further, the biochip was fabricated by modifying the working area of SPE with APIN/P-g-C3N4 using drop cast method, APIN introduced the larger loading of -NH2 group moieties onto P-g-C3N4 matrix and benefitted to reinforced the biomolecules via covalent linkages. The monoclonal anti-SAA was conjugated onto APIN/P-g-C3N4/SPE using EDC-NHS chemistry and BSA was added for non-specific site blocking. The structural, chemical, composition and morphological characteristics of the synthesized, functionalized nanomaterial and fabricated biochips were investigated by XRD, XPS, FT-IR spectroscopy, SEM, FE-SEM and TEM techniques. Further, the electrochemical characterization and response studies of fabricated biochip were analyzed using the CV and DPV techniques. Based on the analytical performance of the proposed immunosensing biochip i.e. BSA/anti-SAA/APIN/P-g-C3N4/SPE, it is capable to detect SAA protein with ultra sensitivity of 79.5 μA log (mL ng-1) cm-2, ultralow limit of detection of 5ngmL-1 and wider linear detection range of 5ngmL-1-500μgmL-1 with quick response time of 10min. Moreover, the fabricated immunosensing biochips was used to analyse SAA protein in spiked serum samples and the achieved results demonstrated the good agreement with the electrochemical response observed in standard SAA protein samples in analytical solution. The proposed biochip can provide insights for developing a wide range of clinical screening tools for detecting various contagious diseases.

Nanostructured zirconia embedded porous carbon based ultrasensitive electrochemical biosensor for SAA biomarker detection

In present work, we report results of the studies related to the development of biosensor based on nanostructured zirconia embedded into the mesoporous carbon nanosheets (nZrO2@PC) towards Serum Amyloid A (SAA) biomarker (a swine flu biomarker) detection. In healthy subjects, SAA is secreted in lower concentration (12.2 ± 15.0 μg mL−1) with respect to swine flu infected patients (49.4 ± 14.1 μg mL−1). Thus, it is considered as a potential biomarker for swine flu detection. In process to develop biosensing platform for swine flu, nZrO2@PC was synthesized via low temperature hydrothermal method and further functionalized using 3-aminopropyltriethoxysilane (APTES). The functionalized material (APTES/nZrO2@PC) was electrophoretically deposited onto the glass electrode coated with indium tin oxide (ITO), subsequently immobilized the monoclonal anti-SAA (antibodies) via EDC-NHS chemistry and bovine serum albumin (BSA) by drop cast method. The structural and morphological properties of the synthesized material and the fabricated electrodes were investigated by X-ray diffraction, Brunauer-Emmett-Teller analysis, scanning electron microscopy, transmission electron microscopy and Fourier-transform infrared spectroscopy techniques. The electrochemical characterization and response studies of fabricated electrodes was performed using cyclic voltammetry (CV) technique. The developed immunoelectrode (BSA/anti-SAA/APTES/nZrO2@PC/ITO) has ability to detect SAA protein with excellent sensitivity of 95.88 μA [log (μg mL−1)]−1 cm−2, linear detection range of 10–100 μg mL−1 with quick response time of 15 min. The performance of the immunosensor was also validated by analyzing the electrochemical response of SAA spiked serum samples and the obtained results indicate excellent correlation with the standard samples.

Effects of climatic factors on the prevalence of influenza virus infection in Cheonan, Korea.

Big data can be used to correlate diseases and climatic factors. The prevalence of influenza (flu) virus, accounting for a large proportion of respiratory infections, suggests that the effect of climate variables according to seasonal dynamics of influenza virus infections should be investigated. Here, trends in flu virus detection were analyzed using data from 9,010 tests performed between January 2012 and December 2018 at Dankook University Hospital, Cheonan, Korea. We compared the detection of the flu virus in Cheonan area and its association with climate change. The flu virus detection rate was 9.9% (894/9,010), and the detection rate was higher for flu virus A (FLUAV; 6.9%) than for flu virus B (FLUBV; 3.0%). Both FLUAV and FLUBV infections are considered an epidemic each year. We identified 43.1% (n = 385) and 35.0% (n = 313) infections in children aged < 10years and adults aged > 60years, respectively. The combination of these age groups encompassed 78.1% (n = 698/894) of the total data. Flu virus infections correlated with air temperature, relative humidity, vapor pressure, atmospheric pressure, particulate matter, and wind chill temperature (P < 0.001). However, the daily temperature range did not significantly correlate with the flu detection results. This is the first study to identify the relationship between long-term flu virus infection with temperature in the temperate region of Cheonan.

Swine flu Detection and Location using Machine Learning Techniques and GIS

The H1N1 virus, more commonly referred to as swine flu, is an illness that is extremely infectious and can in some cases be fatal. Because of this, the lives of many individuals have been taken. The disease can be transmitted from pigs to people. This research presents an artificial neural network (ANN) classifier for disease forecasting, as well as a technique for detecting people who are sick based on the geographic region in which they are found. The source codes for these two algorithms are provided below. These coordinates serve as the foundation for the GIS coordinates that are utilized in the method for assessing the extent to which the illness has spread. The ICMR and NCDC datasets were utilized in the study. They used Dynamic Boundary Location algorithm to detect swine flu affected person’s location, the researchers discovered that the accuracy of the proposed classifier was 96 standard classifiers.

Multiwalled carbon nanotube nanofiller-polyindole polymer matrix-based efficient biosensor for the rapid detection of swine flu

Pictorial representation of the synthesis of the electrode material, fabrication and electrochemical response of the biosensing platform for swine flu detection.

3D-phosphorus doped mesoporous graphitic carbon nitride based immunosensor for swine flu detection

Illustrative representation of the immunosensing platform and its electrochemical response for the detection of swine flu.

Nanostructured Mesoporous Carbon Based Electrochemical Biosensor for Efficient Detection of Swine Flu

AbstractIn the serum sample of swine flu patients, the novel Serum Amyloid A (SAA) protein has been secreted in higher concentration (49.4±14.1 μg mL−1) with respect tothe normal subject (12.2±15.0 μg mL−1), so SAA protein has been used in swine flu diagnosis. In present work, we developed nanostructured mesoporous carbon (mPC) based electrochemical biosensor for the swine flu detection. The mPC was synthesized through the thermal decomposition process, and with 3‐aminopropyltriethoxy silane (APTES), amine functionalization of mPC was carried out. The obtained product was further deposited onto the functionalized indium tin oxide (ITO) coated glass electrode using electrophoretic deposition unit with 40V DC voltage for 150 sec. Next, biofunctionalization with monoclonal anti‐SAA antibodies (anti‐SAA) was carried out onto the APTES/mPC/ITO electrode by EDC‐NHS covalent chemistry and bovine serum albumin (BSA) was used to block non‐specific binding sites. The structural and morphological, characterization of the mPC and fabricated electrodes were investigated using X‐ray diffraction, Raman spectroscopy, scanning electron microscopy, transmission electron microscopy, atomic force microscopy, and Fourier‐transform infrared spectroscopy. However, surface area and porosity analysis were investigated via Brauner‐Emmett‐Teller technique. Next, the electrochemical characterization and response studies were performed by cyclic voltammetry technique. The fabricated biosensing platform (BSA/anti‐SAA/APTES/mPC/ITO) exhibited excellent sensitivity of 1.69 μA mL μg−1 cm−2, wider linear detection range between 30–70 μg mL−1 with a limit of detection of 3.1 μg mL−1. The biosensing performance of the fabricated biosensing platform was also investigated in artificial serum samples and showed a good correlation with the obtained electrochemical response.

Detecting Dengue/Flu Infections Based on Tweets Using LSTM and Word Embedding

With the massive spike in the use of Online Social Network Sites (OSNSs) platforms such as Web 2.0, microblogs services and online blogs, etc., valuable information in the form of sentiment, thoughts, opinions, as well as epidemic outbreaks, etc. are transferred. With the OSNSs being widely accessible, this work aims at proposing a novel approach for disease (dengue or flu) detection based on social media posts. For this purpose, an automated approach is designed with the help of LSTM (Long Short Term Memory) and word embedding techniques. Then the performance of the proposed approach is validated using a set of standard evaluation matrices. In addition, the effectiveness of the selected models is evaluated with performance measurement techniques. The accuracy of the proposed research approach is evaluated using two word embedding techniques; Word2Vec with Skip-gram (SG) and Word2Vec with Continuous-bag-of-words (CBOW). Based on the results conducted in this paper the LSTM Word2Vec with CBOW achieved better results compared to LSTM with Word2Vec SG features embedding technique. Our findings prove that the proposed method yields 94% accuracy compared to state-of-the-art approaches. Consequently, LSTM performed better than other leading methods in the detection of disease-infected people in tweets. In the end, spatial analysis is performed to identify the disease infected region.

Flu Outbreak Prediction Using Twitter Posts Classification and Linear Regression With Historical Centers for Disease Control and Prevention Reports: Prediction Framework Study.

BackgroundSocial networking sites (SNSs) such as Twitter are widely used by diverse demographic populations. The amount of data within SNSs has created an efficient resource for real-time analysis. Thus, data from SNSs can be used effectively to track disease outbreaks and provide necessary warnings. Current SNS-based flu detection and prediction frameworks apply conventional machine learning approaches that require lengthy training and testing, which is not the optimal solution for new outbreaks with new signs and symptoms.ObjectiveThe objective of this study was to propose an efficient and accurate framework that uses data from SNSs to track disease outbreaks and provide early warnings, even for newest outbreaks, accurately.MethodsWe presented a framework of outbreak prediction that included 3 main modules: text classification, mapping, and linear regression for weekly flu rate predictions. The text classification module used the features of sentiment analysis and predefined keyword occurrences. Various classifiers, including FastText (FT) and 6 conventional machine learning algorithms, were evaluated to identify the most efficient and accurate one for the proposed framework. The text classifiers were trained and tested using a prelabeled dataset of flu-related and unrelated Twitter postings. The selected text classifier was then used to classify over 8,400,000 tweet documents. The flu-related documents were then mapped on a weekly basis using a mapping module. Finally, the mapped results were passed together with historical Centers for Disease Control and Prevention (CDC) data to a linear regression module for weekly flu rate predictions.ResultsThe evaluation of flu tweet classification showed that FT, together with the extracted features, achieved accurate results with an F-measure value of 89.9% in addition to its efficiency. Therefore, FT was chosen to be the classification module to work together with the other modules in the proposed framework, including a regression-based estimator, for flu trend predictions. The estimator was evaluated using several regression models. Regression results show that the linear regression–based estimator achieved the highest accuracy results using the measure of Pearson correlation. Thus, the linear regression model was used for the module of weekly flu rate estimation. The prediction results were compared with the available recent data from CDC as the ground truth and showed a strong correlation of 96.29% .ConclusionsThe results demonstrated the efficiency and the accuracy of the proposed framework that can be used even for new outbreaks with new signs and symptoms. The classification results demonstrated that the FT-based framework improves the accuracy and the efficiency of flu disease surveillance systems that use unstructured data such as data from SNSs.

2520. Epidemiology, Clinical Manifestations, and Outcomes of the 2017–2018 Influenza Season Among Hospitalized Patients at a Tertiary Care Center

Background2017–2018 Influenza season showed widespread activity and is expected to be of “high severity.”MethodsRetrospective chart review of patients with Influenza admitted from September 1, 2017 to April 1, 2018. Diagnosis was confirmed by Rapid flu test (RIDT) or Target Enriched Multiplex PCR (TEM PCR). Demographic, clinical, lab, treatment, and outcomes data were obtained. Analysis included prevalence and relative risk (RR)Results220 patients were identified (47% males, 73% White). Median age was 70 years (range 18–99). 65% had Flu A and 27% Flu B. 81% came from home, 17% from a facility (nursing home, assisted living). 49% had flu vaccination (Figure 1). Flu strain and vaccination status had no association RR 1.31 (95% CI 0.85–2.01, P = 0.21). Common comorbidities were lung disease 44%, obesity 41%, DM 36%, CAD 34%, CHF 31% (Figure 2). Common presentations were respiratory 79% and constitutional 53%. 68% were hypoxic and 4% hypotensive on arrival. 42% had new CXR/CT finding and 55% had pneumonia. Sensitivity of RIDT was 38%. 91% were treated with oseltamivir (21% within 48 hours of flu detection). Median treatment duration was 5 days. Hospitalizations peaked in January (Figure 3). Median length of hospital stay was 6 days. 23% had severe flu (needed NPPV 13%, intubation 12%, pressor 5%, ICU stay 16%) which showed significant association with arrival from facility RR 2.21 (95% CI 1.36–3.56, P = 0.001), lung disease RR 1.91 (95% CI 1.17–3.14, P = 0.01) and co-detection of respiratory pathogen (TEM PCR/sputum culture/serology) RR 2.65 (CI 1.60–4.38, P = 0.0001), but none with age >65 RR 1.46 (95% 0.83–2.56, P = 0.18), flu type RR 1.59 (95% CI 0.85–2.98, P = 0.14), active smoking RR 1.40 (95% CI 0.79–2.47, P = 0.24) or vaccination RR 1.21 (95% CI 0.70–2.12, P = 0.48). Fatality rate was 6% with significant association with arrival from facility RR 4.56 (95% CI 1.55–13.40, P = 0.006).Conclusion2017–2018 Influenza season among hospitalized patients involved more elderly and peaked in January. Sensitivity of flu swab was 38% calling for better utilization of TEM PCR in hospitalized patients. Severe flu had significant association with arrival from facility, lung disease and co-detection of respiratory pathogen. Fatality had significant association with arrival from facility. Confounders not accounted. Disclosures All authors: No reported disclosures.

2087. Comparison of Alere™ i Influenza Flu A and B 2 and Cobas® Influenza A/B Nucleic Acid Amplification Tests for Detection of Influenza A/B in Nasopharyngeal Swabs Collected from Children

BackgroundEarly diagnosis of influenza virus is critical for patient management and infection control. Both, Alere™ i Influenza A and B two assay (Alere i; Abbott Laboratories) and Cobas® Influenza A/B nucleic acid test (LIAT; Roche Molecular Systems Inc.) are rapid sample-to-answer CLIA waived molecular assays for flu detection. The aim of this study was to compare the performance of these two commercially available flu assays.MethodsA total of 201 children <18 years were prospectively enrolled from January to April 2018. Subjects with a physician’s test order of Flu test were considered eligible for study participation. Nasopharyngeal swab specimens were obtained after consenting the subjects. Aliquots made were frozen for testing on different diagnostic platforms per manufacturer’s instructions. CDC Flu A/B PCR was used as a reference method to evaluate the performance of the two platforms.ResultsAmong the 201 specimens tested, CDC PCR detected Flu A/B in 107 samples (Flu A: 73, Flu B: 36; Dual flu A/B positive: 2), while Alere i assay detected 102 samples (Flu A: 69, Flu B: 37; dual flu A/B positive: 4; invalid rate: 1/201–0.5%) and LIAT assay detected 112 samples (Flu A: 74, Flu B: 38; invalid rate: 11/201–5.5%). The overall sensitivities for Alere i vs. LIAT (95.3% vs. 100%) and specificities (100% vs. 94.7%) were comparable. Alere i assay missed five samples detected by CDC PCR at high Ct values of 33.81 (range 31.75–36.04). Standard of care BD Veritor™ Flu A/B antigen test (BD) had a sensitivity and specificity of 77.6% (68.3–84.8) and 98.9% (93.3–99.9).ConclusionThe diagnostic performance for Alere™ i and LIAT flu assays were found comparable. Both molecular assays had >17% higher sensitivity than BD veritor antigen test. LIAT assay was found to be more sensitive than Alere i whereas Alere i had greater specificity than LIAT.Table 1:Alere i and LIAT assay results vs. CDC Flu A/B PCR PCRAssayTargetTrue PositiveFalse-positiveTrue NegativeFalse NegativeSensitivity % (Range)Specificity % (Range) Alere i Flu A681127593.2 (84.1–97.5)99.2 (95.1–100.0)Flu B352163197.2 (83.8–99.9)98.8 (95.2–99.8)Overall102094595.3 (88.9–98.3)100 (95.1–100) LIAT Flu A7311270100 (93.8–100)99.2 (95.1–100.0)Flu B344161294.4 (80.0–99.0)97.6 (93.5–99.2)Overall1075890100 (95.7–100)94.7 (87.5–98.0)Disclosures N. Kanwar, Alere Inc.: Collaborator, Research grant. R. Selvarangan, Abbott Diagnostics: Investigator, Research grant.

The public telecare service in the Basque Country

Introduction: There is wide-reaching agreement on the challenges arising from population ageing and, particularly, in regard to sustainability of the social and healthcare systems. A new model of care must place greater emphasis on prevention and proactive care. The Basque Country is one of regions in Europe with the most elderly population, which is why the Public Administrations, and particularly the Basque Government, work intensely to transform its services. The Basque Telecare Service BTS is a reference in this transformation, through 3 areas: socialcare-healthcare coordination, person-focused care and the intensive use of ICTs. Practice change: Telecare is a social service tied to primary care, strongly consolidated in Spain and highly appreciated. It is a service traditionally intended for emergency situations and is therefore based on a reactive model. However, in the present decade, and with clear-cut leadership of the Basque Country, Telecare is being adapted to the new needs, reinforcing the preventive and proactive focus. Targeted population: BTS is intended for different target populations: the elderly, people with disabilities, and people with mental illness. Particularly, in situations of risk because of loneliness, due to the effect this has on people’s health. Timeline: In 2011, the telecare service became a competence of the Basque Government it was formerly allocated in the local councils, and it is now a single service for all of the population. It is an instrument of mediation between social services and home care, and with health services. In 2016 a new stage was launched towards Personalised Telecare. Highlights: After the involvement of the Basque Government, the population using the Telecare has doubled, now encompassing more than 47,000 people 44% of women in Basque Country over the age of 85. 59% of the users live alone. The integrated social-health focus has become a catalyst for innovation, and specifically includes additional services: advice on health matters, help with making physician appointments, collaboration in the flu vaccination campaign, carers support and detection of physical and economic abuse. Sustainability and transferability: The BTS costs €193.29/person/year, and the user population pays their share of €39.38/person/year. These figures clearly demonstrate its sustainable nature, backed by the high quality perceived by the population. Many regions of Spain and, in general, of Europe, have shown interest in the BTS. Conclusions and lessons learnt: For decades, societies have equipped themselves with a strong healthcare system capable of dealing with acute diseases. The social services, however, have not required the existence of a powerful network given that care was provided by the family environment, and especially by the women. When the prevalence of disease shifts from acute to chronic, and when the care required must also be addressed from a more professional and institutional environment, increasing dysfunctions appear. The Basque Country’s commitment to a social service such as Telecare confirms that, over and above the enormous importance of having a quality public healthcare system, it is essential to act on social factors in order to maintain and improve health.

Trends detection of flu based on ensemble models with emotional factors from social networks

Influenza is an acute respiratory illness and widespread activity that occurs every year. Detection and prevention of influenza in its earliest stage would reduce the spread range of the illness. Sina microblog is a popular microblogging service in China, which can be treated as perfect reference sources for flu detection because of its real‐time character. A large number of active users post about their daily life continually. In this paper, we investigate the real‐time flu detection problem and propose a flu detection model with emotion factors and semantic information. First, we extract flu‐related microblog posts automatically in real time by adopting support vector machine (SVM) filter and semantic features. We use association rule mining to extract strongly associated features as additional features for posts to overcome the limitation of 140 words, including sentiment information, which can help us to classify the posts without flu‐related features. Then, the conditional random field model is revised and applied to detect the transition time of flu so that we can find out which place is more likely to have influenza outbreak and when it is more likely to have influenza outbreak in a city or province in China. Experimental results on detecting flu situation during certain times in some locations show the robustness and effectiveness of the proposed model, which might help health authorities in predicting flu outbreak ahead and take timely control action and response. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley &amp; Sons, Inc.

Detecting influenza states based on hybrid model with personal emotional factors from social networks

In this paper, we exhibit how social media data can be used to detect and analyze real-word phenomena with several data mining techniques. We investigate the real-time flu detection problem and propose a flu state detection model with personal emotional factors and semantic information (Em-Flu model). First, we extract flu-related microblog posts automatically in real-time using a hybrid model composed by Support Vector Machine with features extracted from Restricted Boltzmann Machine. In order to overcome the limitation of 140 words for posts, expect for sentiment related features, association semantic rules are also adopted as additional features, such as bag of words, negative words, degree adverbs and sentiment words dictionary. For flu state detection at specific location, we propose an unsupervised model based on personal emotional factors to figure out what state of flu in specific place. For comparison, a supervised model is also built by adopting Conditional Random Fields to decide whether a poster has “really” catch flu and what influenza stage the poster is in. Some statistic methods and prior rules are adopted in supervised model to get the flu state of specific locations by counting the number of microblog posts in different flu states. By considering personal emotional factors, spatial features and temporal patterns of influenza, the performance of unsupervised and supervised models are both improved. The system could tell when and where influenza epidemic is more likely to occur at certain time in specific locations. In different experiments results, the hybrid models show robustness and effectiveness than state-of-the-art unsupervised and supervised model only considering the number of posts.

Design and analysis of Micro-Cantilever based Biosensor for Swine flu Detection

Swine flu which comes under the category of influenza is caused by N1H1 virus. This Disease in 2009 was termed as pandemic by WHO (World Health Organisation). It was fast spreading almost all over the world then. It is same as normal Flu and can be prevented by flu shots/vaccines. The real importance of the disease is because , it may cause serious health conditions like Pneumonia, Respiratory asthma, bronchitis different types of mucus line cancers ,unlike normal flu. Sometimes human life can be endangered by post effects of Swine flu. According to a study Infant mortality is because due to lack of detection of diseases. Thus Detection of Swine flu virus, which is cause for infant mortality is important. This can be done using the most trusted form by using MEMS. In this paper the Micro-Cantilever is used as the sensing unit. Using COMSOL 4.3b, we designed rectangular cantilever and also a rectangle with a hole punched forming SCR (Stress Concentration Region) concept, analysed and optimized.

Design and analysis of Micro-Cantilever based Biosensor for Swine flu Detection

Swine flu which comes under the category of influenza is caused by N1H1 virus. This Disease in 2009 was termed as pandemic by WHO (World Health Organisation). It was fast spreading almost all over the world then. It is same as normal Flu and can be prevented by flu shots/vaccines. The real importance of the disease is because , it may cause serious health conditions like Pneumonia, Respiratory asthma, bronchitis different types of mucus line cancers ,unlike normal flu. Sometimes human life can be endangered by post effects of Swine flu. According to a study Infant mortality is because due to lack of detection of diseases. Thus Detection of Swine flu virus, which is cause for infant mortality is important. This can be done using the most trusted form by using MEMS. In this paper the Micro-Cantilever is used as the sensing unit. Using COMSOL 4.3b, we designed rectangular cantilever and also a rectangle with a hole punched forming SCR (Stress Concentration Region) concept, analysed and optimized.

A Study on Implementing Physiology-Based Approach and Optical Flow Algorithm to Thermal Screening System for Flu Detection

Viral outbreaks such as SARS, H5N1, and H1N1 have led the need of temperature monitoring in areas where crowds are expected such as in hospitals and airports. Infrared (IR) thermal screening, hitherto, has been found as powerful, quick and non-invasive method to detect the elevated temperature of febrile individuals when the temperature of the face is above 36°. Past research has proved that the most excellent area that represents the core body temperature is the medial canthal areas of the face as it is less dependent on the effects of ambient temperature. In realistic situation, the moving subjects are random and the angles of the skin surface and distances change dynamically and misdetection of canthal area could also be due to any other unwanted objects that have the same temperature. The normal approach is to ask a single person to stand still for 2 seconds to capture the temperature. However this approach is ineffective and this will lead to long queue in public. To solve this issue, we have proposed a combination of physiology-based and optical flow algorithm to extract vascular network from the face. The thermal camera is provided with colour and audible alarming, hence when the camera detect an abnormal temperature from a person, automatic febrile individual tracking alarm will

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