Avian influenza is a significant threat to the poultry industry, and it has become an outbreak in many countries because of its mortality and morbidity. Concerns about the history of avian influenza outbreaks has prompted all countries to enhance their independence in pharmaceutical and biological components as a preparedness measure for any potential occurrences. The production of antibodies such as IgY is a potential alternative. The potential of IgY biomaterials can be utilized as bioreceptors in diagnostic tools. This research aimed to purify IgY anti-avian influenza H5N1 produced from chicken eggs. IgY anti-avian influenza H5N1 can be obtained from chickens vaccinated at 0.5 ml per bird dose using inactivated H5N1 vaccine with a purification stage from graded PEG, hydrophobic interaction chromatography, and concentrated using an ultrafilter. The present study shows that the IgY anti-avian influenza H5N1 can be obtained from chickens vaccinated with an inactivated H5N1 vaccine, from 4 chicken eggs with a total protein content of 40.01 mg, it has biological activity with HI titer activity of 4,915.2 HI units and a recovery value of 24 % with hydrophobic interaction chromatography. Therefore, the purification process with hydrophobic interaction chromatography does not remove the IgY biological antibody. Further development will be required to perform.

The most prevalent fatal disease in poultry that can result in high morbidity and mortality is highly pathogenic avian influenza (HPAI), subtype H5N1. A vaccination program is the most frequent way to prevent HPAI cases in poultry, especially against the H5 subtype of HPAI. There are currently a number of avian influenza vaccines available, including recombinant and inactivated whole virus vaccines. The foundation of a recombinant vaccine is possible by the expression of an avian influenza gene of interest following insertion into a carrier vector (no pathogenic virus). A recombinant HPAI vaccine is required to further challenge avian influenza cases in poultry. As a recombinant vaccine inserted into a carrier vector, the hemagglutinin (HA) gene has proven effective. The recombinant Herpes Virus Turkey (rHVT) vector vaccine for avian influenza has been discovered and is commercially available. The rHVT vaccine was developed using a hemagglutinin insert from the HPAI virus clade 2.2. Overall, studies in this review aimed to determine the efficacy of any developed recombinant avian influenza vaccine that uses the HA gene from different clades challenged with any avian influenza virus (AIV) isolate. It was found that the efficacy of hemagglutinin as a recombinant vaccine could be promising for future HPAI vaccine development. In addition, it is possible to design a recombinant vaccine using local isolates to protect poultry farms, particularly in endemic regions.

This research presents design and implementation of internet of things (IoT) based monitoring and predictive system for evaluating chicken manure weight and environmental conditions in poultry housing. The proposed system integrates MQ-137 sensor for ammonia detection, DHT22 sensor for temperature and humidity measurement, and load cell modules for manure weight monitoring. All sensor data are transmitted in real time to cloud platform, enabling continuous environmental assessment. A 30-day experimental study was conducted using two controlled chicken drum models, each containing 15 broiler chickens and provided with different feed types to observe variations in manure production and air quality. Sensor calibration results indicate high accuracy, with average error of 0.31% for ammonia readings and 0.10% for manure weight measurement. Experimental findings show that feed type A generates lower manure weight, reduced ammonia concentration, and more stable temperature conditions compared to feed type B, suggesting improved feed efficiency and better overall chicken health. A genetic algorithm (GA) was employed to optimize regression model predicting manure weight using ammonia concentration and temperature as input features. The GA-optimized model achieved strong predictive performance, with root mean square error (RMSE) of 0.358 g and coefficient of determination (R2) value of 0.992. The results demonstrate that proposed system provides reliable, scalable, and data-driven solution for smart poultry monitoring and early health detection.

Ayam petelur memainkan peran penting dalam industri perunggasan di Indonesia, seiring dengan meningkatnya permintaan konsumen. Namun, produktivitas mereka seringkali terancam oleh penyakit infeksius, seperti Infeksi Coryza, yaitu penyakit pernapasan akut yang disebabkan oleh Avibacterium paragallinarum. Studi ini meneliti temuan patologis mikroskopis dan makroskopis dari penyakit Coryza pada ayam petelur di Peternakan Tanjungsari, Sumedang, guna meningkatkan pemahaman terhadap ciri khas penyakit ini dan menentukan langkah pengendalian yang efektif. Hasil nekropsi pada ayam yang terinfeksi menunjukkan lesi khas, termasuk pembengkakan wajah, eksudasi sinus infraorbital, konjungtivitis, peradangan trakea dan paru-paru, serta atresia folikel. Secara mikroskopis, ditemukan adanya kondisi hemoragi dan kongesti pada trakea dan paru-paru. Metode diagnostik yang digunakan merupakan isolasi kultur bakteri dan uji biokimia. Isolasi bakteri pada media agar coklat mengkonfirmasi keberadaan A. paragallinarum, yang semakin memperkuat diagnosis.