scholarly journals Tissue Tropism and Transmission Ecology Predict Virulence of Human RNA Viruses

2019 ◽  
Author(s):  
Liam Brierley ◽  
Amy B. Pedersen ◽  
Mark E. J. Woolhouse
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Risk Factors ◽  
Random Forest ◽  
Infectious Diseases ◽  
Rna Viruses ◽  
Rna Virus ◽  
Tissue Tropism ◽  
Virus Species ◽  
Ecological Traits

AbstractNovel infectious diseases continue to emerge within human populations. Predictive studies have begun to identify pathogen traits associated with emergence. However, emerging pathogens vary widely in virulence, a key determinant of their ultimate risk to public health. Here, we use structured literature searches to review the virulence of each of the 214 known human-infective RNA virus species. We then use a machine learning framework to determine whether viral virulence can be predicted by ecological traits including human-to-human transmissibility, transmission routes, tissue tropisms and host range. Using severity of clinical disease as a measurement of virulence, we identified potential risk factors using predictive classification tree and random forest ensemble models. The random forest model predicted literature-assigned disease severity of test data with 90.3% accuracy, compared to a null accuracy of 74.2%. In addition to viral taxonomy, the ability to cause systemic infection, having renal and/or neural tropism, direct contact or respiratory transmission, and limited (0 < R0 ≤ 1) human-to-human transmissibility were the strongest predictors of severe disease. We present a novel, comparative perspective on the virulence of all currently known human RNA virus species. The risk factors identified may provide novel perspectives in understanding the evolution of virulence and elucidating molecular virulence mechanisms. These risk factors could also improve planning and preparedness in public health strategies as part of a predictive framework for novel human infections.Author SummaryNewly emerging infectious diseases present potentially serious threats to global health. Although studies have begun to identify pathogen traits associated with the emergence of new human diseases, these do not address why emerging infections vary in the severity of disease they cause, often termed ‘virulence’. We test whether ecological traits of human viruses can act as predictors of virulence, as suggested by theoretical studies. We conduct the first systematic review of virulence across all currently known human RNA virus species. We adopt a machine learning approach by constructing a random forest, a model that aims to optimally predict an outcome using a specific structure of predictors. Predictions matched literature-assigned ratings for 28 of 31 test set viruses. Our predictive model suggests that higher virulence is associated with infection of multiple organ systems, nervous systems or the renal systems. Higher virulence was also associated with contact-based or airborne transmission, and limited capability to transmit between humans. These risk factors may provide novel starting points for questioning why virulence should evolve and identifying causative mechanisms of virulence. In addition, our work could suggest priority targets for infectious disease surveillance and future public health risk strategies.BlurbComparative analysis using machine learning shows specificity of tissue tropism and transmission biology can act as predictive risk factors for virulence of human RNA viruses.

scholarly journals Education on Disease Risk Factors in Adapting New Habits to Prospective Hajj Pilgrims at KBHI Bisri-Pusri Palembang City

2021 ◽  
Vol 1 (2) ◽  
pp. 88-94
Author(s):  
Emma Novita Emma ◽  
Agita Diora Fitri ◽  
Tia Sabrina ◽  
Andyra Priandhana ◽  
Muhammad Musa ◽  
...  
Keyword(s):  
Public Health ◽  
Risk Factors ◽  
Infectious Diseases ◽  
Community Service ◽  
Disease Risk ◽  
Test Method ◽  
Test Results ◽  
Fitness Level ◽  
Pressure Test ◽  
Early Detection And Prevention

Health istitha'ah is the health ability of the pilgrims physically and mentally with a measurable health assesment. It is essential for every citizen who will perform the hajj pilgrimage. The Indonesian Ministry of Health through the Non-Infectious Disease Integrated Service Post (Posbindu PTM) implements an early detection and prevention program for non-infectious diseases in sub-districts, schools or colleges, institutions or workplaces, and Hajj guidance groups (KBIH) especially for pilgrims. The department of public health and community medicine (IKM-IKK) of the Faculty of Medicine, Sriwijaya University perform an education on the detection of risk factors for non-infectious diseases and physical fitness assesment of prospective pilgrims in the context of community service activities at KBIH Bisri Palembang. Examinations were done on 40 prospective pilgrims using the Rockport test method. The results of the examination showed that the 24 pilgrims had moderate fitness level (60%). The blood pressure test results showed that 9 pilgrims had hypertension (22%) and the nutritional status test showed that 20 pilgrims were overweight and obese (38.5%). In addition, there is an assesment for prospective hajj pilgrims in order to understanding risk factors for non-infectious diseases. The result shows that there is a significant increase in understanding about non-infectious diseases in the adaptation of new habits.      

scholarly journals The Unique Phylogenetic Position of a Novel Tick-Borne Phlebovirus Ensures an Ixodid Origin of the GenusPhlebovirus

mSphere ◽  
2018 ◽  
Vol 3 (3) ◽  
Author(s):  
Keita Matsuno ◽  
Masahiro Kajihara ◽  
Ryo Nakao ◽  
Naganori Nao ◽  
Akina Mori-Kajihara ◽  
...  
Keyword(s):  
Public Health ◽  
Infectious Diseases ◽  
Rna Viruses ◽  
Recent Common Ancestor ◽  
Severe Illness ◽  
Ixodes Persulcatus ◽  
Hard Tick ◽  
Ancestral State ◽  
Content Type ◽  
Severe Fever

ABSTRACTThe recent emergence of novel tick-borne RNA viruses has complicated the epidemiological landscape of tick-borne infectious diseases, posing a significant challenge to public health systems that seek to counteract tick-borne diseases. The identification of two novel tick-borne phleboviruses (TBPVs), severe fever with thrombocytopenia syndrome virus (SFTSV) and Heartland virus (HRTV), as causative agents of severe illness in humans has accelerated the investigation and discoveries of novel TBPVs. In the present study, we isolated a novel TBPV designated Mukawa virus (MKWV) from host-questingIxodes persulcatusfemales captured in Japan. Genetic characterization revealed that MKWV is a member of the genusPhlebovirusin the familyPhenuiviridae. Interestingly, MKWV is genetically distinct from other known TBPVs and shares a most recent common ancestor with mosquito/sandfly-borne (insect-borne) phleboviruses. Despite its genetic similarity to insect-borne phleboviruses, the molecular footprints of its viral proteins and its biological characteristics define MKWV as a tick-borne virus that can be transmitted to mammals. A phylogenetic ancestral-state reconstruction for arthropod vectors of phleboviruses including MKWV based on viral L segment sequences indicated that ticks likely harbored ancestral phleboviruses that evolved into both the tick-borne and MKWV/insect-borne phlebovirus lineages. Overall, our findings suggest that most of the phlebovirus evolution has occurred in hard ticks to generate divergent viruses, which may provide a seminal foundation for understanding the mechanisms underlying the evolution and emergence of pathogenic phleboviruses, such as Rift Valley fever virus and SFTSV/HRTV.IMPORTANCEThe emergence of novel tick-borne RNA viruses causing severe illness in humans has complicated the epidemiological landscape of tick-borne diseases, requiring further investigation to safeguard public health. In the present study, we discovered a novel tick-borne phlebovirus fromIxodes persulcatusticks in Japan. While its viral RNA genome sequences were similar to those of mosquito/sandfly-borne viruses, molecular and biological footprints confirmed that this is a tick-borne virus. The unique evolutionary position of the virus allowed us to estimate the ancestral phlebovirus vector, which was likely a hard tick. Our findings may provide a better understanding of the evolution and emergence of phleboviruses associated with emerging infectious diseases, such as severe fever with thrombocytopenia syndrome (SFTS) and Heartland virus disease.

scholarly journals Epistasis between mutations is host-dependent for an RNA virus

2013 ◽  
Vol 9 (1) ◽  
pp. 20120396 ◽  
Author(s):  
Jasna Lalić ◽  
Santiago F. Elena
Keyword(s):  
Infectious Diseases ◽  
Host Species ◽  
Evolutionary Ecology ◽  
Environmental Changes ◽  
Rna Viruses ◽  
Rna Virus ◽  
Emerging Viruses ◽  
Viral Emergence ◽  
Overlapping Reading Frames ◽  
Reading Frames

How, and to what extent, does the environment influence the way mutations interact? Do environmental changes affect both the sign and the magnitude of epistasis? Are there any correlations between environments in the variability, sign or magnitude of epistasis? Very few studies have tackled these questions. Here, we addressed them in the context of viral emergence. Most emerging viruses are RNA viruses with small genomes, overlapping reading frames and multifunctional proteins for which epistasis is abundant. Understanding the effect of host species in the sign and magnitude of epistasis will provide insights into the evolutionary ecology of infectious diseases and the predictability of viral emergence.

scholarly journals RNA Viruses in Aquatic Unicellular Eukaryotes

Viruses ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 362
Author(s):  
Mohammadreza Sadeghi ◽  
Yuji Tomaru ◽  
Tero Ahola
Keyword(s):  
Host Species ◽  
Rna Viruses ◽  
Rna Virus ◽  
Sequence Information ◽  
Virus Species ◽  
Ecological Role ◽  
Major Fraction ◽  
Marine Virus ◽  
Unicellular Eukaryotes

Increasing sequence information indicates that RNA viruses constitute a major fraction of marine virus assemblages. However, only 12 RNA virus species have been described, infecting known host species of marine single-celled eukaryotes. Eight of these use diatoms as hosts, while four are resident in dinoflagellate, raphidophyte, thraustochytrid, or prasinophyte species. Most of these belong to the order Picornavirales, while two are divergent and fall into the families Alvernaviridae and Reoviridae. However, a very recent study has suggested that there is extraordinary diversity in aquatic RNA viromes, describing thousands of viruses, many of which likely use protist hosts. Thus, RNA viruses are expected to play a major ecological role for marine unicellular eukaryotic hosts. In this review, we describe in detail what has to date been discovered concerning viruses with RNA genomes that infect aquatic unicellular eukaryotes.

scholarly journals Combination of ADASYN-N and Random Forest in Predicting of Obesity Status in Indonesia: A Case Study of Indonesian Basic Health Research 2013

2021 ◽  
Vol 2123 (1) ◽  
pp. 012039
Author(s):  
M Aqsha ◽  
SA Thamrin ◽  
Armin Lawi
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Random Forest ◽  
Health Research ◽  
Pathological Condition ◽  
Learning Approaches ◽  
Data Generation ◽  
Basic Health ◽  
Obesity Status ◽  
Status Data

Abstract Obesity is a pathological condition due to the accumulation of excessive fat needed for body functions. The risk factors for obesity are related to their obesity status. Various machine learning approaches are an alternative in predicting obesity status. However, in most cases, the available datasets are not sufficiently balanced in their data classes. The existence of data imbalances can cause the prediction results to be inaccurate. The purpose of this paper is to overcome the problem of data class imbalance and predict obesity status using the 2013 Indonesian Basic Health Research (RISKESDAS) data. Adaptive Synthetic Nominal (ADASYN-N) can be used to balance obesity status data. The balanced obesity status data is then predicted using one of the machine learning approaches, namely Random Forest. The results obtained show that through ADASYN-N with a balance level parameter of 1 (β = 100%) after synthetic data generation and Random Forest with a tree number of 200 and involving 7 variables as risk factors, giving the results of the classification of obesity status which is good. This can be seen from the AUC value of 84.41%.

New Machine Learning Approach for Detection of Injury Risk Factors in Young Team Sport Athletes

2020 ◽  
Author(s):  
Susanne Jauhiainen ◽  
Jukka-Pekka Kauppi ◽  
Mari Leppänen ◽  
Kati Pasanen ◽  
Jari Parkkari ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Logistic Regression ◽  
Random Forest ◽  
Injury Risk ◽  
Predictive Power ◽  
Plantar Flexion ◽  
Ankle Injuries ◽  
Initial Contact ◽  
Injury Risk Factors

AbstractThe purpose of this article is to present how predictive machine learning methods can be utilized for detecting sport injury risk factors in a data-driven manner. The approach can be used for finding new hypotheses for risk factors and confirming the predictive power of previously recognized ones. We used three-dimensional motion analysis and physical data from 314 young basketball and floorball players (48.4% males, 15.72±1.79 yr, 173.34±9.14 cm, 64.65±10.4 kg). Both linear (L1-regularized logistic regression) and non-linear methods (random forest) were used to predict moderate and severe knee and ankle injuries (N=57) during three-year follow-up. Results were confirmed with permutation tests and predictive risk factors detected with Wilcoxon signed-rank-test (p<0.01). Random forest suggested twelve consistent injury predictors and logistic regression twenty. Ten of these were suggested in both models; sex, body mass index, hamstring flexibility, knee joint laxity, medial knee displacement, height, ankle plantar flexion at initial contact, leg press one-repetition max, and knee valgus at initial contact. Cross-validated areas under receiver operating characteristic curve were 0.65 (logistic regression) and 0.63 (random forest). The results highlight the difficulty of predicting future injuries, but also show that even with models having relatively low predictive power, certain predictive injury risk factors can be consistently detected.

Review the Use of Antivirus for COVID-19 Treatment

2021 ◽  
pp. 097206342098311
Author(s):  
Lukman Prayitno ◽  
Julien Rosye Mawuntu ◽  
Herna ◽  
Tri Juni Angkasawati
Keyword(s):  
Public Health ◽  
Clinical Trials ◽  
Safety Profile ◽  
Drug Information ◽  
Rna Viruses ◽  
Rna Virus ◽  
Systematic Search ◽  
Number Of Patients

On 31 January 2020, World announced COVID-19 as an Emergency Public Health of International Concern. The number of patients in Indonesia continues to grow. Anti-viral in the COVID-19 Drug Information Laboratory in Indonesia are Lopinavir/Ritonavir, Favipiravir, Remdemsivir, Oseltamivir, Chloroquine Phosphate and Hydroxychloroquine Phosphate. Therefore, it is necessary to know the basis and management of its use. An online systematic search was performed on articles published until 30 March 2020. We use search keywords that are tailored to the purpose of writing. All six antivirals were used for the treatment of RNA virus. Chloroquine, Hydroxychloroquine and Remdesivir effectively control the SARS-CoV2 virus invitro. Lopinavir/Ritonavir, Hydroxychloroquine and Oseltamivir have been used clinically for the treatment of SARS-CoV2 virus. In 2020, there are 42 clinical trials of six antivirals. Guidance of the antivirus are from China, Belgium and Indonesia. Its differences are based on the patient’s condition. There is a lack of evidence of six antiviral effectiveness against the SARS-CoV2 virus. It has been used for other RNA viruses. It is supported by a safety profile. In a pandemic situation and the absence of a specific antivirus, the use of the six antiviruses can be done and be useful.

scholarly journals Machine Learning for Predicting the Risk for Childhood Asthma Using Prenatal, Perinatal, Postnatal and Environmental Factors

Healthcare ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1464
Author(s):  
Zineb Jeddi ◽  
Ihsane Gryech ◽  
Mounir Ghogho ◽  
Maryame EL Hammoumi ◽  
Chafiq Mahraoui
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Random Forest ◽  
Childhood Asthma ◽  
Support Vector ◽  
Prenatal Risk ◽  
Prenatal Risk Factors ◽  
Chi Squared

The prevalence rate for childhood asthma and its associated risk factors vary significantly across countries and regions. In the case of Morocco, the scarcity of available medical data makes scientific research on diseases such as asthma very challenging. In this paper, we build machine learning models to predict the occurrence of childhood asthma using data from a prospective study of 202 children with and without asthma. The association between different factors and asthma diagnosis is first assessed using a Chi-squared test. Then, predictive models such as logistic regression analysis, decision trees, random forest and support vector machine are used to explore the relationship between childhood asthma and the various risk factors. First, data were pre-processed using a Chi-squared feature selection, 19 out of the 36 factors were found to be significantly associated (p-value < 0.05) with childhood asthma; these include: history of atopic diseases in the family, presence of mites, cold air, strong odors and mold in the child’s environment, mode of birth, breastfeeding and early life habits and exposures. For asthma prediction, random forest yielded the best predictive performance (accuracy = 84.9%), followed by logistic regression (accuracy = 82.57%), support vector machine (accuracy = 82.5%) and decision trees (accuracy = 75.19%). The decision tree model has the advantage of being easily interpreted. This study identified important maternal and prenatal risk factors for childhood asthma, the majority of which are avoidable. Appropriate steps are needed to raise awareness about the prenatal risk factors.

scholarly journals Revealing RNA virus diversity and evolution in unicellular algae transcriptomes

2021 ◽  
Author(s):  
Justine Charon ◽  
Shauna Murray ◽  
Edward C Holmes
Keyword(s):  
Rna Viruses ◽  
Rna Virus ◽  
Sequence Similarity ◽  
Sequence Divergence ◽  
Virus Species ◽  
Sequencing Project ◽  
Virus Diversity ◽  
Microbial Eukaryotes ◽  
Unicellular Algae ◽  
Positive Sense

Remarkably little is known about the diversity and evolution of RNA viruses in unicellular eukaryotes. We screened a total of 570 transcriptomes from the Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP) project that encompasses a wide diversity of microbial eukaryotes, including most major photosynthetic lineages (i.e. the microalgae). From this, we identified 30 new and divergent RNA virus species, occupying a range of phylogenetic positions within the overall diversity of RNA viruses. Approximately one-third of the newly described viruses comprised single-stranded positive-sense RNA viruses from the order Lenarviricota associated with fungi, plants and protists, while another third were related to the order Ghabrivirales, including members of the protist and fungi-associated Totiviridae. Other viral species showed sequence similarity to positive-sense RNA viruses from the algae-associated Marnaviridae, the double-stranded RNA Partitiviridae, as well as a single negative-sense RNA virus related to the Qinviridae. Importantly, we were able to identify divergent RNA viruses from distant host taxa, revealing the ancestry of these viral families and greatly extending our knowledge of the RNA viromes of microalgal cultures. Both the limited number of viruses detected per sample and the low sequence identity to known RNA viruses imply that additional microalgal viruses exist that could not be detected at the current sequencing depth or were too divergent to be identified using sequence similarity. Together, these results highlight the need for further investigation of algal-associated RNA viruses as well as the development of new tools to identify RNA viruses that exhibit very high levels of sequence divergence.

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