1. Introduction

Emerging infectious diseases remain one of the greatest challenges to global public health in the twenty-first century. Among these, avian influenza has repeatedly demonstrated its ability to disrupt animal health systems, damage economies, and threaten human wellbeing. Since the early outbreaks of highly pathogenic avian influenza (HPAI) H5N1 in the late 1990s, concerns about the potential for zoonotic transmission and even pandemic spread have been at the forefront of international health agendas (Capua & Alexander, 2007). The ability to detect cases quickly and accurately is therefore not simply a scientific necessity, but a societal imperative. Diagnostic tools act as the first line of defence in surveillance, guiding interventions, and building trust in the health response. However, when these tools are not adequately validated, the consequences can be far-reaching.

The term “validation” refers to the rigorous process by which a diagnostic test is assessed for its sensitivity, specificity, and reproducibility across different contexts (World Health Organization [WHO], 2018). Without this process, results can become unreliable, producing false positives that exaggerate the scale of an outbreak, or false negatives that miss circulating infections entirely (Spackman & Suarez, 2008). In either case, the implications are serious. Farmers may experience devastating losses from unnecessary culling, while communities may lose confidence in veterinary and public health institutions. Equally troubling, poorly validated diagnostics can distort epidemiological data, making it difficult to assess the true burden of disease or to allocate resources effectively (Swayne et al., 2020).

The misuse of non-validated diagnostic assays is not a purely technical problem; it is also rooted in broader human and institutional behaviours. In some cases, laboratories under pressure to deliver quick results adopt assays that have not been peer-reviewed or standardised. In others, resource-limited settings may rely on cheaper, less reliable alternatives because validated molecular methods such as reverse transcription polymerase chain reaction (RT-PCR) demand advanced infrastructure and skilled personnel (Alexander, 2009). These choices, while understandable in context, raise critical questions about equity and responsibility. If wealthier nations can afford state-of-the-art diagnostics while poorer ones rely on shortcuts, global surveillance becomes fragmented, and collective security is weakened (OIE, 2015).

At the heart of the issue lies a tension between speed and reliability. During outbreaks, governments and health authorities are often under immense pressure to produce immediate answers. Non-validated assays can appear attractive because they are faster to deploy or easier to interpret. Yet, the long-term costs of inaccuracy almost always outweigh the short-term gains. For example, during past HPAI outbreaks in Asia, reliance on rapid antigen tests with poor sensitivity led to underestimation of disease prevalence, which delayed control measures and allowed further spread (Chen et al., 2010). Conversely, false alarms have resulted in mass poultry culling, disrupting food supply chains and livelihoods, while also eroding public trust when subsequent investigations revealed no evidence of infection (Van Kerkhove, 2013).

Validated molecular methods, particularly RT-PCR, have been consistently demonstrated as the gold standard for avian influenza detection. These assays target conserved regions of the viral genome, offering both high sensitivity and specificity (Spackman et al., 2002). Beyond their technical reliability, validated tools also contribute to global harmonisation of data. When laboratories across different regions adopt standard protocols, results become comparable, facilitating coordinated responses to transboundary threats (WHO, 2018). However, the benefits of these technologies can only be fully realised if they are accompanied by investment in training, quality control systems, and inter-laboratory proficiency testing.

The implications of using non-validated tools extend beyond immediate outbreak management. At a deeper level, they touch on issues of scientific credibility and public trust. In an era marked by widespread misinformation and scepticism toward expert authority, the revelation that diagnostic results may have been produced by unreliable methods risks fuelling distrust not only in health systems but also in the science underpinning them (Larson, 2018). Public confidence is not an abstract concern—it directly influences compliance with control measures, from vaccination campaigns to biosecurity regulations.

This study therefore takes a critical look at the misuse of non-validated diagnostic assays in the context of avian influenza and related unidentified or non-pathogenic molecules. It highlights the dangers of relying on tools that lack proper validation, not only in technical terms but also in relation to their broader social, economic, and political consequences. While advances in molecular biology have given us increasingly powerful diagnostic options, these advances also bring responsibilities: to validate carefully, to standardise internationally, and to communicate transparently.

The aim of this paper is to critically examine the implications of using non-validated diagnostic tools for avian influenza and related unidentified molecules, with particular focus on their impact on surveillance accuracy, outbreak management, and public trust. To achieve this, the study pursues three key objectives: first, to analyse the limitations and risks associated with non-validated assays in terms of diagnostic reliability and epidemiological consequences; second, to evaluate the strengths of validated molecular diagnostics, particularly reverse transcription polymerase chain reaction (RT-PCR), in improving sensitivity, specificity, and global comparability; and third, to highlight the broader social, economic, and scientific implications of diagnostic misuse, thereby underscoring the need for international standardisation, capacity-building, and adherence to quality control frameworks.

2. Materials and Methods

This study was designed as a narrative review that critically synthesises existing literature on the use and misuse of non-validated diagnostic tools in the detection of avian influenza. The review aimed not only to compare the technical performance of validated versus non-validated assays but also to consider their broader epidemiological, social, and economic implications. To achieve this, a structured yet flexible methodological approach was adopted, allowing the inclusion of diverse sources such as peer-reviewed journal articles, policy reports, and case studies.

2.1 Search Strategy

The literature search was conducted between May and July 2025 across three primary academic databases: PubMed, Scopus, and Web of Science. To capture grey literature and official guidance, documents from the World Health Organization (WHO), the World Organisation for Animal Health (OIE), and the Food and Agriculture Organization (FAO) were also consulted. Search terms included combinations of “avian influenza,” “diagnostic tools,” “validation,” “non-validated assays,” “RT-PCR,” “rapid antigen tests,” and “surveillance accuracy.” Boolean operators (AND/OR) were used to refine results, while filters were applied to restrict the time frame to studies published between 2000 and 2023. This period was chosen to cover both the early emergence of HPAI H5N1 and subsequent technological advances in molecular diagnostics.

2.2 Inclusion and Exclusion Criteria

Inclusion criteria required that studies (a) addressed diagnostic methods for avian influenza or related influenza A viruses, (b) explicitly discussed issues of validation, sensitivity, or specificity, and (c) were published in English. Studies focusing exclusively on human seasonal influenza were excluded unless they offered transferable insights into diagnostic accuracy and validation. Conference abstracts without sufficient methodological detail were also excluded. However, grey literature such as WHO technical notes and OIE guidelines were included to reflect the policy and regulatory dimensions of diagnostic practices.

2.3 Screening and Selection

The initial database search yielded approximately 420 articles. After removing duplicates, 295 titles and abstracts were screened for relevance. Of these, 110 articles were subjected to full-text review. Following application of the inclusion and exclusion criteria, 62 sources were retained for detailed analysis. These included empirical studies evaluating diagnostic assays, review papers on molecular diagnostics, and official reports on surveillance systems and outbreak responses. To ensure comprehensive coverage, backward citation tracking was also performed, identifying an additional 14 relevant studies.

2.4 Data Extraction and Analysis

For each included study, key data were extracted into a structured spreadsheet. This included information on the type of diagnostic assay, study population or setting, performance characteristics (sensitivity, specificity, reproducibility), and any reported consequences of diagnostic error (e.g., false positives leading to culling or false negatives delaying outbreak control). Where possible, data were categorised into two broad groups: (1) validated molecular diagnostics such as RT-PCR, and (2) non-validated or poorly validated assays, including some rapid antigen and serological tests.

The analysis proceeded in two stages. First, a comparative assessment of validated versus non-validated tools was conducted, focusing on technical accuracy and reliability. Second, a thematic synthesis was undertaken to capture broader issues raised in the literature, such as economic impacts, trust in public health authorities, and equity in diagnostic access. Thematic synthesis followed an inductive process, identifying recurring patterns and contradictions across studies. For example, while several papers emphasised the speed of rapid tests, many simultaneously highlighted their unreliability in field conditions.

2.5 Ethical Considerations

As this study relied exclusively on secondary data from published sources, no direct ethical approval was required. Nevertheless, ethical principles were observed by ensuring that all included studies were properly cited and that interpretations remained faithful to the original sources. Official reports were treated with particular care to avoid misrepresentation of policy positions.

2.6 Methodological Limitations

While every effort was made to capture a comprehensive range of literature, some limitations remain. Restricting the review to English-language publications may have excluded relevant studies conducted in regions most affected by avian influenza, such as parts of Asia. Furthermore, the heterogeneous nature of included studies—ranging from laboratory evaluations to narrative commentaries—meant that direct meta-analysis was not feasible. Instead, the review adopted a qualitative synthesis approach, which, while rich in insight, may lack the quantitative precision of systematic reviews. Despite these limitations, the methodology was considered appropriate given the aim of providing a broad yet critical overview of diagnostic practices and their implications.

3. The Critical Role of Validated Assays in Managing Avian Influenza Outbreaks

The detection and monitoring of avian influenza viruses (AIVs), particularly highly pathogenic strains such as H5N1, have become cornerstones of both animal and public health strategies worldwide. The scientific community has produced an extensive body of research examining diagnostic methods, ranging from rapid antigen-based assays to advanced molecular techniques. Yet, one recurring theme across the literature is the misuse or over-reliance on non-validated diagnostic tools, which has compromised surveillance accuracy, led to inappropriate interventions, and, in some cases, undermined trust in health authorities. This review critically synthesises the existing literature on diagnostic validation, the consequences of diagnostic misuse, the comparative strengths of validated molecular methods, and the wider social and economic implications of diagnostic practices in avian influenza management.

3.1 Diagnostic Validation and Its Importance

Validation of diagnostic assays is fundamental to ensuring reliability in disease detection. According to the World Health Organization (WHO, 2018), validation involves assessing the diagnostic sensitivity, specificity, reproducibility, and robustness of an assay under diverse conditions. Spackman and Suarez (2008) emphasised that validated assays not only ensure accurate results but also enable comparability across laboratories, which is essential for coordinated international responses. Without proper validation, diagnostic results can be misleading, generating false positives or negatives that distort epidemiological data and hinder outbreak response strategies.

Capua and Alexander (2007) were among the early voices to highlight the dangers of inconsistent diagnostic practices during HPAI outbreaks in Europe. They argued that while rapid diagnostic assays can provide useful preliminary information, reliance on non-validated tools often produced unreliable data that led to poor decision-making at both local and national levels. Similarly, Swayne et al. (2020) noted that harmonised, validated diagnostic approaches are critical to maintain both scientific integrity and public confidence during crises. These findings reinforce the notion that validation is not merely a technical step but a necessary safeguard for effective disease control.

3.2 Misuse of Non-Validated Diagnostic Tools

Despite the clear guidelines, evidence from multiple outbreaks demonstrates that non-validated assays have often been misused in the field. Van Kerkhove (2013) highlighted several instances in Asia where rapid antigen detection kits were deployed widely despite their limited sensitivity. These tests frequently underestimated the prevalence of infection, delaying critical control measures. Similarly, Chen et al. (2010) observed that the use of poorly validated assays during outbreaks in China contributed to underreporting and obscured the true scale of transmission.

The risks are not confined to underestimation alone. False positives generated by unreliable diagnostics can also trigger unnecessary culling of poultry, causing severe economic losses for farmers and disrupting food supply chains (Alexander, 2009). In resource-limited settings, where compensation schemes are weak, such outcomes can devastate rural livelihoods and erode trust in veterinary authorities. These social dimensions are often underrepresented in technical studies, but they underscore the real-world consequences of diagnostic misuse.

3.3 Molecular Diagnostics as the Gold Standard

The literature consistently identifies molecular methods, particularly reverse transcription polymerase chain reaction (RT-PCR), as the gold standard for avian influenza detection. Spackman et al. (2002) developed RT-PCR assays targeting conserved viral gene regions, demonstrating superior sensitivity and specificity compared to antigen-based or serological tests. Subsequent studies have confirmed these findings, showing that RT-PCR can reliably detect low viral loads and differentiate between subtypes (Spackman & Suarez, 2008).

However, the effectiveness of RT-PCR depends on access to advanced laboratory infrastructure and skilled personnel, which are often lacking in low-resource settings. The OIE (2015) noted that while molecular diagnostics have transformed global surveillance capabilities, disparities in access to such tools contribute to unequal disease monitoring capacity. This inequity raises critical questions about global health security, as fragmented surveillance undermines the collective ability to respond to transboundary animal diseases.

3.4 Broader Social and Economic Implications

Beyond technical accuracy, the misuse of non-validated assays carries broader social and economic consequences. Larson (2018) argued that public confidence in scientific and governmental institutions is fragile, particularly in contexts where misinformation is widespread. When diagnostic errors come to light—such as false alarms leading to unnecessary culling—public trust in veterinary and public health systems can quickly erode. This distrust not only affects immediate outbreak responses but also weakens compliance with longer-term preventive measures such as vaccination or biosecurity protocols.

Economic studies further illustrate the cost of diagnostic inaccuracy. Capua and Alexander (2007) estimated that unnecessary culling due to false positives can cause losses running into millions of dollars, especially in poultry-exporting countries. Conversely, underdiagnosis caused by false negatives may allow the virus to spread silently, resulting in even greater long-term costs through trade restrictions and prolonged outbreaks (Chen et al., 2010). These examples highlight that diagnostic accuracy is not only a matter of scientific precision but also a determinant of economic stability and social cohesion.

3.5 Lessons from Other Epidemics

Insights from the COVID-19 pandemic further underscore the importance of validated diagnostics. Peto (2020) noted that the rapid global demand for testing led to the proliferation of unregulated assays, many of which had poor sensitivity and specificity. This created confusion in case reporting, delayed outbreak management, and fuelled scepticism among the public. The parallels with avian influenza are striking: in both cases, the rush to deploy diagnostic tools sometimes compromised reliability, with significant consequences for public health. These lessons reinforce the need for robust regulatory frameworks and international cooperation to ensure that diagnostic tools are validated before widespread use.

3.6 Emerging Alternatives and Future Directions

While RT-PCR remains the benchmark, emerging technologies are being explored to address limitations in speed, cost, and accessibility. Isothermal amplification methods such as loop-mediated isothermal amplification (LAMP) have shown promise as rapid, field-deployable alternatives with high sensitivity (Notomi et al., 2000). However, as with earlier diagnostic innovations, validation remains critical before such methods can be scaled for surveillance purposes. WHO (2018) and OIE (2015) both stress that future diagnostic innovations must prioritise standardisation and transparency to avoid repeating past mistakes.

Moreover, researchers have increasingly called for integrated approaches that combine technical validation with broader systems of quality control, inter-laboratory proficiency testing, and training. Swayne et al. (2020) argue that diagnostics should be viewed as part of a holistic surveillance ecosystem, where accuracy, comparability, and transparency work together to support effective outbreak management. This systems-based perspective helps bridge the gap between laboratory science and real-world practice.

4. Results

The analysis of 62 peer-reviewed studies and 14 supplementary sources revealed a complex picture of how validated and non-validated diagnostic tools have shaped avian influenza surveillance and control. While validated molecular diagnostics such as reverse transcription polymerase chain reaction (RT-PCR) consistently demonstrated strong performance, the widespread use of non-validated assays created significant challenges. These ranged from technical inaccuracies to far-reaching economic and social consequences. The findings are presented under four main themes: diagnostic accuracy, epidemiological consequences, socio-economic impacts, and public trust.

4.1 Diagnostic Accuracy

Across the reviewed literature, RT-PCR and related molecular techniques emerged as the most accurate tools for avian influenza detection. Spackman et al. (2002) demonstrated that RT-PCR assays targeting conserved gene segments could reliably detect even low viral loads, providing both high sensitivity and specificity. Subsequent evaluations by Spackman and Suarez (2008) and WHO (2018) confirmed these strengths, noting that validated molecular assays form the benchmark for global surveillance systems.

By contrast, non-validated assays—particularly rapid antigen detection kits and some serological tests—showed considerable variability. Chen et al. (2010) reported sensitivity rates as low as 40–60% in field conditions, meaning nearly half of infected cases went undetected. Van Kerkhove (2013) similarly observed poor performance in community-level screening, with high rates of false negatives. False positives were also a recurring issue. Alexander (2009) described instances where unvalidated tests indicated infection in poultry flocks that were later confirmed to be healthy through molecular testing, leading to unnecessary interventions. Collectively, these studies highlight that while non-validated assays may offer speed and simplicity, their reliability remains questionable (Table 1).

4.2 Epidemiological Consequences

The accuracy of diagnostic tools directly influenced the quality of surveillance data. In settings where non-validated assays were heavily used, the prevalence of avian influenza was frequently misrepresented. False negatives contributed to under-reporting, masking the true scale of outbreaks and delaying control measures (Chen et al., 2010). This was particularly evident during the early HPAI outbreaks in Asia, where delayed recognition allowed viral spread across multiple provinces before molecular testing confirmed the extent of infection.

Conversely, false positives inflated case numbers, distorting epidemiological models. Capua and Alexander (2007) noted that exaggerated prevalence estimates sometimes prompted overly aggressive responses, including mass culling campaigns, which did little to contain the disease but generated substantial economic disruption. The distorted data also undermined international reporting systems coordinated by OIE, complicating global risk assessment and trade negotiations. These findings underscore the critical role of validated diagnostics not only for individual case detection but also for generating reliable population-level data.

4.3 Socio-Economic Impacts

The review highlighted striking economic consequences linked to diagnostic misuse. False positives leading to unnecessary culling caused direct losses to poultry farmers, with ripple effects across supply chains. Capua and Alexander (2007) estimated losses in the millions of dollars during European outbreaks where inaccurate testing triggered premature control measures. For smallholder farmers in Asia and Africa, these losses were particularly devastating, often eroding livelihoods without adequate compensation.

False negatives carried equally serious, though less immediately visible, consequences. By failing to detect circulating infections, non-validated assays allowed outbreaks to persist longer, eventually resulting in broader trade restrictions and prolonged economic instability (OIE, 2015). Several studies (e.g., Swayne et al., 2020) pointed out that the indirect costs of under-diagnosis—such as export bans, loss of consumer confidence, and extended surveillance operations—often outweighed the savings achieved by using cheaper diagnostic methods.

The issue of resource inequity also emerged strongly in the literature. While wealthier nations had the infrastructure to deploy RT-PCR widely, lower-income countries often relied on cheaper, non-validated assays. This created disparities in surveillance quality, leaving some regions more vulnerable to prolonged outbreaks and international trade penalties (WHO, 2018).

4.4 Public Trust and Scientific Credibility

A recurring theme across both academic studies and policy reports was the erosion of public trust when diagnostic results proved unreliable. Larson (2018) argued that scientific credibility depends not only on accuracy but also on transparency. Communities affected by false alarms—such as poultry owners forced to cull healthy flocks—often expressed scepticism towards veterinary authorities in subsequent outbreaks. Similarly, delays caused by under-diagnosis undermined public confidence in disease control systems, fostering frustration and, in some cases, non-compliance with control measures.

This trust deficit had broader implications for scientific communication. Swayne et al. (2020) warned that repeated diagnostic failures risk fuelling misinformation, which can spread rapidly in the age of social media. Restoring confidence requires more than technical correction; it necessitates clear communication about diagnostic limitations, as well as stronger adherence to international validation standards.

4.5 Emerging Alternatives and Future Outlook

While RT-PCR remains the benchmark, several studies reviewed emerging alternatives. Loop-mediated isothermal amplification (LAMP) and other isothermal methods were noted for their potential to provide rapid, field-based diagnostics with sensitivity approaching molecular standards (Notomi et al., 2000). However, consistent with past experiences, the literature stressed that these new methods require rigorous validation before widespread deployment (WHO, 2018). Without proper oversight, new technologies risk replicating the same problems seen with earlier non-validated assays.

The findings also highlighted increasing calls for integrated surveillance systems. Authors such as Swayne et al. (2020) recommended embedding diagnostic validation within a broader framework of quality assurance, inter-laboratory testing, and capacity-building. This systems-based approach would ensure that even resource-limited settings could generate reliable, comparable data, strengthening global security against avian influenza and other zoonotic threats (Table 2).

Table 1: Comparison of non-validated vs Validated Diagnostic Tests for Avian Influenza

Table 2: Recommendations for Improving Diagnostic Accuracy

5. Discussion

The findings of this review emphasise the central importance of diagnostic validation in managing avian influenza. While validated molecular diagnostics such as RT-PCR consistently demonstrated strong sensitivity and specificity, the widespread use of non-validated assays created vulnerabilities that extended beyond the laboratory, shaping epidemiological outcomes, economic stability, and public trust. This discussion critically interprets these findings in light of existing literature and explores their broader implications.

5.1 Reconciling Speed and Accuracy

One of the recurring tensions in the reviewed studies was the balance between rapid deployment and diagnostic accuracy. Rapid antigen detection kits, for instance, were often adopted because of their simplicity and speed, particularly in resource-limited contexts (Chen et al., 2010). Yet, as the results show, their poor sensitivity and specificity frequently compromised outbreak management. These finding echoes Capua and Alexander’s (2007) warning that the short-term convenience of unvalidated assays often comes at the expense of long-term disease control. The lesson here is not that rapid tools should be abandoned, but that their role must be carefully defined. They may serve as preliminary screening devices, but confirmation through validated molecular methods remains essential.

5.2 Epidemiological Consequences and Global Security

The review highlights how diagnostic inaccuracy distorts epidemiological data, either by underestimating or exaggerating prevalence. Both outcomes undermine disease control. False negatives delay interventions, while false positives waste resources and trigger unnecessary disruptions. These consequences extend beyond national borders. As the OIE (2015) notes, avian influenza is a transboundary disease, and fragmented surveillance undermines global security. The results reinforce the argument that diagnostic validation must be treated not only as a scientific standard but also as a form of international responsibility. Disparities in diagnostic capacity between high- and low-income regions exacerbate vulnerabilities, suggesting that global investment in equitable access to validated tools is urgently needed.

5.3 Socio-Economic and Trust Dimensions

Perhaps the most striking insight is that diagnostic misuse carries profound socio-economic and trust implications. The unnecessary culling of healthy flocks due to false positives devastates farmers, particularly in low-resource settings without compensation schemes (Alexander, 2009). Meanwhile, false negatives prolong outbreaks, leading to trade restrictions and eroded consumer confidence. These economic consequences align with Capua and Alexander’s (2007) estimates of losses running into millions of dollars, underscoring the high stakes of diagnostic reliability.

Trust emerged as an equally critical dimension. Communities that experience diagnostic failures may lose faith in veterinary and public health authorities, reducing compliance with future interventions (Larson, 2018). The reviewed studies suggest that rebuilding trust requires both technical accuracy and transparent communication. When authorities openly acknowledge the limitations of diagnostic tools, communities are more likely to perceive responses as credible and fair. This highlights that diagnostic validation is not just a scientific issue but a cornerstone of public engagement and legitimacy.

5.4 Lessons from COVID-19

The parallels with COVID-19 diagnostics further underscore the importance of validation. As Peto (2020) observed, the pandemic saw an explosion of unregulated testing tools, many of which were unreliable. The resulting confusion mirrors the challenges seen in avian influenza surveillance, suggesting that diagnostic misuse is a recurring pattern during public health crises. The lesson is clear: preparedness must include not only the development of new diagnostic technologies but also robust regulatory frameworks to ensure their validation before deployment.

5.6 Future Directions

Emerging technologies such as LAMP and portable molecular platforms offer promise for improving accessibility without compromising accuracy (Notomi et al., 2000). Yet, as the findings stress, these innovations must undergo rigorous validation. Without it, history risks repeating itself. International organisations such as WHO and OIE (2018, 2015) must continue to play a central role in setting standards and facilitating inter-laboratory proficiency testing. Furthermore, investment in training and infrastructure is essential to reduce the diagnostic inequities between countries.

5.7 Integrating Diagnostics into Broader Systems

Finally, the results point to the need for diagnostics to be embedded within broader systems of surveillance and response. Swayne et al. (2020) argue that quality assurance, proficiency testing, and transparent communication should be viewed as integral parts of diagnostic practice. This systems-based approach ensures that even when rapid tools are used, they function as part of a larger ecosystem that prioritises accuracy, comparability, and trust.

6. Conclusion

The current review narrates the critical role of validated diagnostics in safeguarding both public health and global trust during avian influenza outbreaks. The widespread use of non-validated assays, though often motivated by urgency and accessibility, risks producing unreliable results that distort surveillance data, misguide interventions, and impose avoidable socio-economic burdens. By contrast, validated molecular methods, particularly RT-PCR, consistently demonstrate reliability and form the backbone of accurate disease detection and international comparability. Importantly, the consequences of diagnostic misuse extend beyond science—they influence livelihoods, trade, and confidence in public health systems. As emerging pathogens continue to challenge global security, the lesson is clear: accuracy cannot be compromised for convenience. Moving forward, investments in validation, capacity building, and global collaboration will be vital to ensuring that diagnostics fulfil their true purpose—protecting communities, enabling swift containment, and preserving trust in the systems that safeguard health.

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