mteb/CovidQA huggingface dataset:
For each test confirmed positive for RSV, on average b 7 tests were performed for influenza and found to be negative for influenza. These b 7 tests are attributed to an RSV infection, however the number of influenza-negative tests that actually tested positive for RSV is unknown. If all specimens had been tested for the same viruses (panel tests), 1/b 7 would correspond to the sensitivity for RSV testing, and the sensitivity for adenovirus and PIV given by 1/b 8 and 1/b 9 respectively. Some laboratories are known to test for viruses sequentially [22] , and so 1/b 7 -1/b 9 were not interpreted as estimates of the sensitivity for other viruses. Sequential testing may occur if a rapid test for influenza is negative and the laboratory then performs PCR or culture testing. Similarly in young children with a respiratory illness in the winter, rapid tests for RSV infection may be performed first, and only specimens with negative results submitted for subsequent testing for influenza or other
respiratory viruses [25] . By contrast, many laboratories conduct panel tests for multiple viruses for ease of handling, decreased patient sampling, and recognition that coinfection can occur. Either form of sequential testing would not bias the estimate of sensitivity applicable to test results reported to RVDSS, though significant use of rapid antigen tests in the laboratories reporting to RVDSS would reduce the overall sensitivity. As a single specimen may undergo multiple tests, the false-negative rate applicable to a specimen that has undergone multiple tests would be expected to be much lower than the system average for individual tests. Parameters b 1. to b 4 account for trends and the seasonality of truly negative specimens (patients presenting with other acute respiratory infections).
Over 50,000 tests for influenza were reported to the RVDSS each year, peaking in 2004/05 at 101,000. Overall 10% of the influenza tests were positive for influenza, ranging from 4% to 13% depending on the season. The proportion positive for RSV, parainfluenza and adenovirus averaged 9%, 3% and 2% respectively. As seen in Figure 1 , no virus was identified in 75% of specimens submitted for testing (white area under the curve). Even for the winter months of December through April, one of these 4 viruses was identified on average in no more than 30% of the specimens. The strong and consistent synchronization of negative tests with influenza positive tests, as seen in Figure 1 , is suggestive that false negative results contributed to the large number of negative tests during periods of influenza activity.
The sensitivity for influenza A testing averaged 33.7% (with model-estimated 95% confidence intervals of 33.3-34.1) for the 1999/2000-2005/06 period. Influenza B testing had a similar estimated sensitivity at 34.7 (95% CI 33.4-36.1). Estimated sensitivities varied somewhat from season to season, generally ranging from 30%-40% (Table 1) , and provincial level estimates, as well, were within a similar range. Stratifying by province or season produced similar estimates for the sensitivity of influenza A testing: 32% (95% CI 30-34) and 36% (95% CI 33-41) respectively. Estimates of sensitivity based on test results reported to the RVDSS for individual laboratories with sufficient data to fit the model showed significant variation, with estimates of sensitivity ranging from 25-65%. As expected, laboratories using primarily rapid antigen tests had lower estimated sensitivities, and laboratories that used PCR methods had higher sensitivity estimates. However, information on testing procedures
is limited primarily to the 2005/06 survey. As well, additional irregularities were noticed in the laboratory data and not all laboratories provided sufficient data to fit the model. Figure 2 illustrates a good model fit where the weekly number of influenza negative tests is well explained by the model covariates, with a few exceptions. Firstly, it is evident that additional specimens were tested during the SARS period, as indicated by the period where the number of weekly influenza negative tests exceeded the expected number, or equivalently, a period of successive positive residuals. Residuals typically capture random variation; hence represent tests that can not be allocated based on the specified model. In addition to the SARS period, testing appears to have been elevated for a number of weeks in January 2000 during the peak of the 1999/2000 A/ Sydney/05/97 (H3N2) season in which respiratory admissions were unusually elevated [26, 27] , and in December 2003, when an elevated risk
of paediatric deaths associated with the A/Fujian/411/02 (H3N2) strain [28] was identified in the US. As these periods corresponded to a period of heightened public awareness due to severe influenza outbreaks, parameter estimation was repeated without these data points. Exclusion of these data points did not alter the sensitivity estimate for influenza.
The attribution of influenza negative test results to influenza and other viruses is illustrated in Figure 3 . The baseline curve is the model estimate of the number of tests that were likely truly negative for all four viruses tested. A reduction in specimen collection and testing, primarily for viruses other than influenza, is also evident over the Christmas period ( Figure 3) .
The weekly proportion of tests confirmed positive for influenza peaked each season at 15 to 30%. Accounting for the model estimated false negative rate suggests that during periods of peak influenza activity, 40-90% of tests were performed on specimens taken from persons recently infected with influenza. Influenza was confirmed in only 14% of specimens sent for testing over the winter period, whereas the sensitivity estimate would imply that up to 40% of influenza tests could be attributed to an influenza infection. The corresponding figures for the whole year indicate that 10% of specimens were confirmed positive for influenza and 30% of influenza tests could be model-attributed to an influenza infection annually.

Despite a relatively large number of tests in the off-season, the number of influenza positive tests was almost negligible; suggesting that the false positive rate applicable to RVDSS influenza testing is minimal.
The model estimated sensitivity based on influenza test results reported to the RVDSS of 30-40% is much lower than the standard assay sensitivities documented in the literature. Standard sensitivities for diagnostic procedures used by participating laboratories ranged from 64% for rapid antigen tests to 95% for RT-PCR tests, averaging 75% for the study period [23] . As performance characteristics of specific tests are generally based on high quality specimens, the difference of approximately 40% is likely linked to any one of many operational procedures that affects the quality of the specimen and its procurement. Unlike validation studies, our samples are taken from a variety of clinical settings and processed with a variety of procedures across the country. As well, variation in the indications for diagnostic testing may vary across the country.
As there are many other respiratory pathogens that are not routinely tested for, or reported to the RVDSS, including human metapneumovirus (hMPV), coronaviruses, and rhinoviruses for which patients may seek medical care and present with influenza like illness [29] [30] [31] [32] , a large proportion of negative test results was expected. The overall model fit, and the general consistency of the sensitivity estimates, suggests that these many respiratory viruses were reasonably accounted for by the seasonal baseline and that the strong association between the number of influenza positive and influenza negative tests on a weekly basis is indicative of a significant number of false negative results, rather than the activity of another virus or viruses exactly synchronous with influenza. The latter would bias the estimated sensitivity of the system downwards. However, to significantly and consistently bias the estimate, the degree of synchronization would have to be fairly strong, persist
over the whole study period, and occur in all provinces. Synchronization was not observed among the RVDSS viruses (influenza A, influenza B, RSV, adenovirus and PIV), and elsewhere other viruses such as rhinovirus, coronavirus and hMPV accounted for only a small proportion of the viral identifications and were not found to be synchronized with influenza [33] . As well, patients may present for care due to a secondary bacterial infection. While any specimen would likely test negative as the virus, at this point, is likely not detectable, the model would statistically attribute a negative test in this case to the primary infection; one of the four RVDSS viruses or to the seasonal baseline that represents other respiratory infections, depending on the level of viral activity at the time of the test. This is not considered a source of bias.
The large variation in false negative rates estimated for individual laboratories reporting to the RVDSS suggests that standardization of sample procurement, testing and reporting procedures would likely reduce the overall false negative rate. The accuracy of diagnostic tests is known to be affected by the quality of the specimen [10, 11] , its handling, the timing of collection after symptom onset, and the age of the patient [14, 15] . Even with the most sensitive molecular methodologies, yield was shown to be strongly related to the time since onset of symptoms [9, 14] , with a 3-fold decline in proportion positive within 3 to 5 days after onset of symptoms for both RT-PCR and culture procedures. For most laboratory tests, specimen procurement within 72 hours of from the onset of symptoms is recommended [6] , yet patients often present much later in the course of illness. Estimates of the median time since onset of symptoms suggest a delay of 3 and 5 days for outpatient and
inpatients respectively [15] , however these estimates are limited to patients with laboratory confirmed influenza. In addition, there are inherent differences in the performance characteristics of the currently used diagnostic tests [4, 6, 8, [34] [35] [36] [37] [38] . Lack of standardization between diagnostic tests and algorithms used in different laboratories reporting to the RVDSS adds to this complexity. The routine use of RT-PCR testing has only recently become available in Canada (only 20% of tests used RT-PCR methods as of 2005/06 [23] ), but increased use of this modality is expected to improve accuracy.
Population or system level sensitivity estimates that include the effects of sample quality are limited. Grijalva and colleagues [39] estimated the diagnostic sensitivity in a capture recapture study of children hospitalized for respiratory complications at 69% for a RT-PCR based system and 39% for a clinical-laboratory based system (passive surveillance of tests performed during clinical practice, and using a variety of commercially available tests).

Though the expected proportion of influenza tests that were due to influenza infections is unknown and variable, our model estimate of 30% appears plausible. Cooper and colleagues [33] attributed 22% of telephone health calls for cold/flu to influenza over two relatively mild years, and elsewhere 20% of admissions for acute respiratory infections (including influenza) in adults aged 20-64 years were attributed to influenza, and 42% for seniors [1] .
While there are limitations with this approach, there are no other simple alternatives to assist in the interpretation of the RVDSS data. It would have been helpful to analyze data based on each specimen sent for testing. With only the number of weekly tests and number of positive results, we were unable to calculate the number of specimens that were actually found to be negative for all four viruses, or to estimate the extent of co-infection. Coinfection, which was not accounted for in our model, could result in an under-estimation of the number of falsely negative tests, as the attribution of an influenza negative test that was actually coinfected with influenza and another respiratory virus would have to be split between the viruses. With auxiliary information associated with each specimen, model estimates of false negative rates based on, for example, test type, time since onset of symptoms, age of the patient, or clinical presentation would have allowed us to explore the reasons
for the high false negative rates. As the false negative rate appears to be laboratory dependant (data not shown), this estimated range is applicable only to the RVDSS for the study period. A significant reduction in the false negative rate is anticipated as methods become standardized and with the uptake of the new RT-PCR methods. As positive results, particularly for culture, are often obtained a week or more after the specimen was received, some positive results may have been reported in a different week than the test. Multiple test results for a single specimen may have also contributed to reporting irregularities. These irregularities would tend to bias the estimated parameter towards zero, and hence the estimated sensitivity towards 1. Considering the overall model fit and the relative severity of influenza [1] , we conclude that our estimate of sensitivity may be slightly over-estimated (number of false negatives under-estimated).
Poor test sensitivity contributes to the chronic underestimation of the burden of influenza in the general population. Since estimates of the burden of illness drive planning for preventive and therapeutic interventions, it is important to improve all aspects leading to improved diagnostic accuracy. We have illustrated a simple method that uses the surveillance data itself to estimate the system wide sensitivity associated with the weekly proportion of tests confirmed positive. Although our estimate of sensitivity is only applicable to the interpretation of the RVDSS data over the study period, similar estimates for specific cohorts or laboratory procedures may help guide further investigation into the reasons for the large number of false negative test results. The capacity for improved diagnostic accuracy will ultimately improve our understanding of the epidemiology of influenza.
Can’t RIDD off viruses

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4061530/

SHA: ef58c6e2790539f30df14acc260ae2af4b5f3d1f

Authors: Bhattacharyya, Sankar
Date: 2014-06-18
DOI: 10.3389/fmicb.2014.00292
License: cc-by
Abstract: The mammalian genome has evolved to encode a battery of mechanisms, to mitigate a progression in the life cycle of an invasive viral pathogen. Although apparently disadvantaged by their dependence on the host biosynthetic processes, an immensely faster rate of evolution provides viruses with an edge in this conflict. In this review, I have discussed the potential anti-virus activity of inositol-requiring enzyme 1 (IRE1), a well characterized effector of the cellular homeostatic response to an overloading of the endoplasmic reticulum (ER) protein-folding capacity. IRE1, an ER-membrane-resident ribonuclease (RNase), upon activation catalyses regulated cleavage of select protein-coding and non-coding host RNAs, using an RNase domain which is homologous to that of the known anti-viral effector RNaseL. The latter operates as part of the Oligoadenylate synthetase OAS/RNaseL system of anti-viral defense mechanism. Protein-coding RNA substrates are differentially treated by the IRE1
RNase to either augment, through cytoplasmic splicing of an intron in the Xbp1 transcript, or suppress gene expression. This referred suppression of gene expression is mediated through degradative cleavage of a select cohort of cellular RNA transcripts, initiating the regulated IRE1-dependent decay (RIDD) pathway. The review first discusses the anti-viral mechanism of the OAS/RNaseL system and evasion tactics employed by different viruses. This is followed by a review of the RIDD pathway and its potential effect on the stability of viral RNAs. I conclude with a comparison of the enzymatic activity of the two RNases followed by deliberations on the physiological consequences of their activation.
Text: Establishment of infection by a virus, even in permissive host cells, is beset with a plethora of challenges from innate-antiviral and cell-death pathways. Therefore, the host response to a virus infection might prove to be inhibitory for the viral life cycle in a direct or an indirect manner. The direct mechanism involves expression of multiple anti-viral genes that have evolved to recognize, react, and thereby rid the infected host of the viral nucleic acid (Zhou et al., 1997; Thompson et al., 2011) . On the other hand the pathways, e.g., those that culminate in initiating an apoptotic death for the host cell, indirectly serve to limit the spread of virus (Roulston et al., 1999) . A major difference between these two mechanisms is that while the former response is transmissible to neighboring uninfected cells through interferon (IFN) signaling, the latter is observed mostly in cis. Recent reports, however, have demonstrated transmission of an apoptotic signal between cells
that are in contact through gap junctions, although such a signaling from an virus infected host cell to an uninfected one is not known yet (Cusato et al., 2003; Udawatte and Ripps, 2005; Kameritsch et al., 2013) . Successful viral pathogens, through a process of active selection, have evolved to replicate and simultaneously evade or block either of these host responses. The viral nucleic acids which could be the genome (positive-sense singlestranded RNA virus) or RNA derived from transcription of the genome [negative-stranded single-sense RNA or double-stranded RNA (dsRNA) or DNA virus], offer critical targets for both detection and eradication. The viral nucleic acid targeting armaments in the host arsenal include those that recognize the associated molecular patterns like toll-like receptors (TLRs), DDX58 (or RIG-1), IFIH1 (or MDA5), IFIT proteins [IFN-stimulated genes (ISG)56 and ISF54], etc. (Aoshi et al., 2011; Bowzard et al., 2011; Jensen and Thomsen, 2012) . This is followed
by IFN signaling and expression or activation of factors that target the inducer for degradation or modification like OAS/ribonuclease L (RNaseL) system, APOBEC3, MCPIP1, the ZC3HAV1/exosome system and RNAi pathways (Gao et al., 2002; Sheehy et al., 2002; Guo et al., 2007; Daffis et al., 2010; Sidahmed and Wilkie, 2010; Schmidt et al., 2012; Cho et al., 2013a; Lin et al., 2013) . In this review we focus on two proteins containing homologous RNase domains, RNaseL with a known direct antiviral function and Inositolrequiring enzyme 1 (IRE1 or ERN1) which has an RNaseL-like RNase domain with a known role in homeostatic response to unfolded proteins in the endoplasmic reticulum (ER) and a potential to function as an antiviral (Figure 1 ; Tirasophon et al., 2000) .
In mammalian cells the tell-tale signs of RNA virus infection, like the presence of cytosolic RNA having 5 -ppp or extensive (>30 bp) dsRNA segments are detected by dedicated pathogen associated molecular pattern receptors (PAMPs) or pattern recognition receptors (PRRs) in the host cell, like RIG-1, MDA5, and the IFIT family of proteins (Aoshi et al., 2011; Bowzard et al., 2011; Vabret and Blander, 2013) . The transduction of a signal of this recognition results in the expression of IFN genes the products www.frontiersin.org FIGURE 1 | Schematic representation of the ribonuclease activity of IRE1 and RNaseL showing cross-talk between the paths catalysed by the enzymes. The figure shows activation of RNase activity following dimerization triggered by either accumulation of unfolded proteins in the ER-lumen or synthesis of 2-5A by the enzyme OAS, respectively, for IRE1 and RNaseL. The cleavage of Xbp1u by IRE1 releases an intron thus generating Xbp1s. The IRE1 targets in RIDD pathway or
all RNaseL substrates are shown to undergo degradative cleavage. The cleavage products generated through degradation of the respective substrate is shown to potentially interact with RIG-I thereby leading to Interferon secretion and trans-activation of Oas genes through Interferon signaling. Abbreviations: RIG-I = retinoic acid inducible gene-I, Ifnb = interferon beta gene loci, IFN = interferons, ISG = interferon-sensitive genes, 2-5A = 2 -5 oligoadenylates.
of which upon secretion outside the cell bind to cognate receptors, initiating further downstream signaling (Figure 1 ; Randall and Goodbourn, 2008) . The genes that are regulated as a result of IFN signaling are termed as IFN-stimulated or IFN-regulated genes (ISGs or IRGs; Sen and Sarkar, 2007; Schoggins and Rice, 2011) . Oligoadenylate synthetase or OAS genes are canonical ISGs that convert ATP into 2 -5 linked oligoadenylates (2-5A) by an unique enzymatic mechanism (Figure 1 ; Hartmann et al., 2003) . Further, they are RNA-binding proteins that function like PRRs, in a way that the 2-5A synthesizing activity needs to be induced through an interaction with dsRNA (Minks et al., 1979; Hartmann et al., 2003) . In a host cell infected by an RNA virus, such dsRNA is present in the form of replication-intermediates (RI), which are synthesized by the virus-encoded RNA-dependent RNA polymerases (RdRp) and subsequently used by the same enzyme to synthesize more genomic RNA, through
asymmetric transcription (Weber et al., 2006) . However, the replications complexes (RCs) harboring these RI molecules are found secluded inside host-membrane derived vesicles, at least in positive-strand RNA viruses, a group which contains many human pathogens (Uchil and Satchidanandam, 2003; Denison, 2008) . Reports from different groups suggest OAS proteins to be distributed both in the cytoplasm as well as in membrane-associated fractions, perhaps indicating an evolution of the host anti-viral methodologies towards detection of the membrane-associated viral dsRNAs (Marie et al., 1990; Lin et al., 2009) . DNA viruses on the other hand, produce dsRNA by annealing of RNA derived from transcription of both strands in the same viral genomic loci, which are probably detected by the cytoplasmic pool of OAS proteins (Jacobs and Langland, 1996; Weber et al., 2006) . Post-activation the OAS enzymes synthesize 2-5A molecules in a non-processive reaction producing oligomers which, although
potentially ranging in size from dimeric to multimeric, are functionally active only in a trimeric or tetrameric form (Dong et al., 1994; Sarkar et al., 1999; Silverman, 2007) . These small ligands, which bear phosphate groups (1-3) at the 5 end and hydroxyl groups at the 2 and 3 positions, serve as co-factor which can specifically interact with and thereby allosterically activate, existing RNaseL molecules (Knight et al., 1980; Zhou et al., 1997 Zhou et al., , 2005 Sarkar et al., 1999) . As part of a physiological control system these 2-5A oligomers are quite unstable in that they are highly susceptible to degradation by cellular 5 -phosphatases and PDE12 (2 -phosphodiesterase; Silverman et al., 1981; Johnston and Hearl, 1987; Kubota et al., 2004; Schmidt et al., 2012) . Viral strategies to evade or overcome this host defense mechanism ranges from preventing IFN signaling which would hinder the induction of OAS expression or thwarting activation of expressed OAS proteins by either
shielding the viral dsRNA from interacting with it or modulating the host pathway to synthesize inactive 2-5A derivatives (Cayley et al., 1984; Hersh et al., 1984; Rice et al., 1985; Maitra et al., 1994; Beattie et al., 1995; Rivas et al., 1998; Child et al., 2004; Min and Krug, 2006; Sanchez and Mohr, 2007; Sorgeloos et al., 2013) . Shielding of viral RNA from interacting with OAS is possible through enclosure of dsRNA replication intermediates in membrane enclosed compartments as observed in many flaviviruses (Ahlquist, 2006; Miller and Krijnse-Locker, 2008; Miorin et al., 2013) .