Honey consumption is increasing worldwide due to its natural qualities, but its safety and authenticity are not always assured. Honey adulteration through mislabeling and fraudulent production significantly damages consumer trust and poses health risks. This study aimed to identify the botanical makeup of Kazakh and imported Honey to assess whether it matches with the declared labeling using melissopalynological analysis and DNA metabarcoding. During our research, melissopalynological analysis showed that 64.7% of Kazakh Honey samples and 62.5% of imported Honey matched the declared labeling. In 6 samples of domestic Honey and 6 samples of imported honey, the plant source differed from what was indicated on the label. According to DNA metabarcoding data, the botanical origin of honey by the ITS2 marker was confirmed in 58.8% of Kazakh and 31.25% of imported honey samples. Using the rbcL marker, only 29.4% of domestic and 31.25% of imported honey samples matched the declared plant origin. Agreement between melissopalynology and ITS2 marker data was observed in 10 samples, and between melissopalynology and rbcL in 8 samples. Eight samples also matched the labeling based on both genetic markers. Thus, combining DNA metabarcoding with melissopalinological analysis enables a more accurate identification of the honey's botanical origin, thereby strengthening product authenticity control, helping to detect counterfeits, facilitating access to international markets, and increasing consumer confidence. Overall, the results highlight the importance of strict monitoring and verification methods to protect consumers and maintain market integrity.

Brucellosis is an infectious disease of animals that can infect humans. The disease causes significant economic losses and threatens human health. A timely and accurate disease diagnosis plays a vital role in the identification of brucellosis. In addition to traditional diagnostic methods, molecular methods allow diagnosis and typing of the causative agent of brucellosis. This review will discuss various methods, such as Bruce-lаdder, Suiladder, high-resolution melt analysis, restriction fragment length polymorphism, multilocus sequence typing, multilocus variable-number tandem repeat analysis, and whole-genome sequencing single-nucleotide polymorphism, for the molecular typing of Brucella and discuss their advantages and disadvantages. Keywords: Brucella, molecular typing, multilocus sequence typing, multilocus variable-number tandem-repeat analysis, single-nucleotide polymorphisms, whole-genome sequencing.

In this study, we analyzed the microbial composition of the rumen contents of cattle from Kazakhstan. Specifically, samples of the liquid and solid fractions of the rumen were collected to determine the quantitative and qualitative composition of methanogenic archaea. Cattle were six steers receiving hay-concentrate feeding. Methane emission was determined by repeated measurements for each animal. Rumen samples were then taken from fistulas and analyzed using 16S metabarcoding via Next-Generation Sequencing (NGS). The difference between the rumen fractions was investigated, resulting in differential distribution of the families Streptococccaceae, Lactobacillaceae, Desulfobulbaceae, and Succinivibrionaceae, which were more abundant in the liquid fraction, while Thalassospiraceae showed a higher presence in the solid fraction. These differences can be explained by the fact that fibrolytic bacteria are associated with the solid fraction compared to the liquid. A relationship between methane emission and methanogenic microbiota was also observed. Steers producing more methane showed microbiota richer in methanogens; specifically, most Mathanobacteriaceae resided in the liquid fraction and solid fraction of animals 1 and 6, respectively. The same animals carried most of the Methanobrevibacter and Methanosphaera genera. On the contrary, animals 2, 3, and 5 hosted a lower amount of methanogens, which also agreed with the data on methane emissions. In conclusion, this study demonstrated a relationship between methane emission and the content of methanogenic archaea in different rumen fractions collected from cattle in Kazakhstan. As a result of the studies, it was found that the solid fraction of the rumen contained more genera of methanogens than the liquid fraction of the rumen. These results prove that taking rumen contents through a fistula is more useful than taking it through a probe. The presented data may be of interest to scientists from all over the world engaged in similar research in a comparative aspect.

The environment significantly impacts the lives of bees and their feeding. This study aimed to investigate bee foraging using melissopalynological analysis and DNA metabarcoding in intensive farming, reserved, and urbanized areas. The highest alpha diversity was observed in the reserved and intensive farming areas. The urbanized area had less diversity. In the intensive farming area, Sinapis, Helianthus, and Fagopyrum predominated; in the reserved area, Melilotus, Helianthus, and Brassica predominated. In the urbanized area, garden plants, namely radish (Raphanus sativus) and cucumber (Cucumis hystrix), and agricultural plants, namely soybean (Glycine max) and melon (Cucumis melo), were often found. The most significant agreement was between the rbcL and the melissopalynological analysis. The ITS2 revealed equal matches with both rbcL and melissopalynology, but this marker missed or underestimated some genera. Trifolium pretense and Brassica nigra were identified simultaneously by the melissopalinology method and two genetic markers in DNA metabarcoding. The species Convolvulus arvensis, Melilotus officinalis, Echium vulgare, Brassica rapa, Helianthus divaricatus, and Onobrychis viciifolia were found in all ecological areas. Imperfect databases impose some limits in the identification of some taxa using metabarcoding. The further research and expansion of plant databases is needed. Studying the food preferences of bees in different environmental conditions and landscapes is necessary to develop measures to preserve their populations.

Viral infections continue to pose major challenges to pig health, farm productivity, and global food security. Early and accurate diagnosis is the cornerstone of disease prevention, surveillance, and control in swine populations. In recent years, remarkable progress has been achieved in molecular, serological, and digital diagnostic technologies, enabling more rapid, sensitive, and field-adaptable detection of important porcine viruses such as African swine fever virus, porcine reproductive and respiratory syndrome virus, and classical swine fever virus. This review summarizes current and emerging diagnostic approaches, highlighting polymerase chain reaction (PCR) and its advanced forms, quantitative PCR and digital PCR, as the gold standards for laboratory confirmation. The advent of next-generation sequencing and metagenomics has revolutionized pathogen discovery and genomic surveillance, providing comprehensive insights into viral evolution and transboundary transmission. Isothermal amplification techniques such as loop-mediated isothermal amplification and recombinase polymerase amplification have shown strong potential for on-farm diagnosis due to their simplicity, rapidity, and minimal equipment requirements. Innovations such as clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated-based assays, biosensors, lab-on-a-chip platforms, and point-of-care testing devices are bridging the gap between laboratory precision and field application, allowing rapid decision-making during outbreaks. The integration of artificial intelligence, machine learning, and geographic information systems has further enhanced diagnostic interpretation, real-time data sharing, and early outbreak prediction under the One Health framework. Despite these advances, challenges remain in ensuring assay standardization, affordability, and equitable access in resource-limited regions. Continued international collaboration, data sharing, and policy harmonization under the guidance of the Food and Agriculture Organization, the World Organization for Animal Health, and the World Health Organization are essential for the global control of swine viral diseases. Ultimately, combining molecular innovation with digital adaptability offers the most promising path toward resilient, cost-effective, and sustainable diagnostic systems for safeguarding animal and public health. Keywords: viral infections, emerging technologies, One Health, molecular diagnostics, artificial intelligence, microfluidic platforms, biosensors.