Escherichia coli (E. coli) remains a major concern in poultry production due to its ability to incite foodborne illness and public health crisis, zoonotic potential, and the increasing prevalence of antibiotic-resistant strains. The contamination of poultry products with pathogenic E. coli, including avian pathogenic E. coli (APEC) and Shiga toxin-producing E. coli (STEC), presents risks at multiple stages of the poultry production cycle. The stages affected by E. coli range from, but are not limited to, the hatcheries to grow-out operations, slaughterhouses, and retail markets. While traditional detection methods such as culture-based assays and polymerase chain reaction (PCR) are well-established for E. coli detection in the food supply chain, their time, cost, and high infrastructure demands limit their suitability for rapid and field-based surveillance—hindering the ability for effective cessation and handling of outbreaks. Biosensors have emerged as powerful diagnostic tools that offer rapid, sensitive, and cost-effective alternatives for E. coli detection across various stages of poultry development and processing where detection is needed. This review examines current biosensor technologies designed to detect bacterial biomarkers, toxins, antibiotic resistance genes, and host immune response indicators for E. coli. Emphasis is placed on field-deployable and point-of-care (POC) platforms capable of integrating into poultry production environments. In addition to enhancing early pathogen detection, biosensors support antimicrobial resistance monitoring, facilitate integration into Hazard Analysis Critical Control Points (HACCP) systems, and align with the One Health framework by improving both animal and public health outcomes. Their strategic implementation in slaughterhouse quality control and marketplace testing can significantly reduce contamination risk and strengthen traceability in the poultry value chain. As biosensor technology continues to evolve, its application in E. coli surveillance is poised to play a transformative role in sustainable poultry production and global food safety.

Mumps virus (MuV) is a single-stranded, negative-sense RNA virus of the Family Paramyxoviridae. MuV is a highly contagious human pathogen that causes primarily mild symptoms, including hallmark swelling of the parotid glands. Severe cases can occur, leading to neurological complications, including deafness, meningitis, and encephalitis. The mumps vaccine, now included in combination with measles and rubella vaccines (MMR), was first made available in the 1960s. After its introduction, mumps incidence dropped dramatically to less than 500 cases annually in the US. However, even with long-standing vaccination programs, MuV continues to challenge the landscape of public health due to a resurgence of cases in the past several decades and a still present lack of approved antiviral drugs and treatments available for the disease. This review will explore the biology of MuV, focusing on how MuV replicates and interacts with the host immune system. Recent studies have also shed light on the role of protein phosphorylation in regulating viral RNA synthesis—particularly the dynamic interactions between the nucleoprotein (NP) and phosphoprotein (P)—offering new insights into how the virus controls its replication machinery both mechanistically and through utilizing host cell advantages. We also examine how the immune system responds to mumps infection and vaccination, and how those responses may vary across viral genotypes. Although the Jeryl Lynn vaccine strain has played a key role in controlling mumps for decades, outbreaks among vaccinated individuals have raised questions about the present vaccine’s efficacy against circulating and emerging genotypes and if novel strategies will be required to prevent future outbreaks. We review current epidemiological data, highlighting shifts in MuV transmission and genotype distribution, and discuss the need for updated or genotype-matched vaccines. By connecting molecular virology with real-world trends in disease spread and vaccine performance, this review aims to support ongoing efforts to strengthen mumps control strategies and inform the development of next-generation vaccines.

Avian influenza (AI), particularly highly pathogenic avian influenza (HPAI), represents a serious and growing threat to global poultry production, international trade, and human health security. Control of AI is complicated by the high evolutionary rate of influenza A viruses, which drives antigenic diversity and ongoing emergence of novel strains. Effective surveillance and disease management therefore depend on timely and accurate diagnostics. While conventional methods—including virus isolation, reverse transcription-quantitative polymerase chain reaction (RT-qPCR), and enzyme-linked immunosorbent assays (ELISAs)—remain effective and widely used, they are limited by long turnaround times, the need for specialized equipment, and reliance on highly trained personnel. In addition, strict state and federal regulatory requirements restrict testing to a limited number of authorized laboratories. Although these regulations are essential for maintaining diagnostic accuracy and quality assurance, they place substantial strain on laboratory capacity during outbreaks and delay actionable results. The need for rapid, on-site decision making has driven interest in alternative diagnostic approaches, including biosensor technologies. A major limitation of current diagnostic strategies is the lack of robust DIVA (Differentiating Infected from Vaccinated Animals) capability. In countries such as the United States, where poultry vaccination against AI is not routinely practiced, the absence of DIVA-compatible diagnostics has hindered adoption of vaccination as a disease management tool, as seropositive birds and products face significant trade restrictions. Biosensor platforms capable of enabling DIVA strategies offer a potential pathway to support vaccination while preserving surveillance integrity. This review examines the current landscape of AI and HPAI diagnostics, emphasizing the limitations of traditional approaches and the opportunities presented by biosensor platforms. We evaluate electrochemical, optical, piezoelectric, and nucleic-acid-based biosensors, with particular attention to biorecognition strategies, performance metrics, field deployability, and applications supporting subtype discrimination, DIVA implementation, and One Health surveillance.