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The diagnostics of ruminant parasites remains one of the cornerstones for parasite control best practices. Field veterinarians have several techniques at their disposal (fecal egg count, coproculture, FAMACHA®, plasma pepsinogen, ELISA- Ostertagia , ELISA- Fasciola , Baermann and ELISA-Lungworm) for the identification and/or quantification of gastrointestinal nematodes, lungworms and liver fluke infecting small ruminants and cattle. Each of these diagnostic tools has its own strengths and weaknesses and is more appropriate for a specific production operation and/or age of the animal (young and adults). This review focuses on the usability and interpretation of the results of these diagnostic tools. The most advanced technical information on sampling, storage, advantages and limitations of each tool for different types of production operations and animal categories is provided. Graphical abstract

Aim: A trial was conducted to assess the influence of parasitic load on the lambs reared under the intensive system, continuous grazing, and rotational grazing systems of management. Materials and Methods: A total of thirty numbers of the undetermined breed of ewe lambs around 4-5 months of age were randomly selected and allotted to three treatment groups: T1 (intensive system - control), T2 (rotational grazing), and T3 (continuous grazing). The T1 group lambs were raised under a stall-fed system of management, the T2 group lambs were grazed under rotational grazing strategy in four paddocks of plot-A, while the T3 group lambs were continuously grazed in plot-B. Results: At the end of the study, there was a highly significant difference (p=0.01) in the fortnightly strongyle egg count per gram (EPG) of feces among the lambs pertaining to the three treatment groups; the lambs in T3 had a higher strongyle EPG compared to T2 lambs. With regard to the overall reduction in EPG from the initial count, lambs under rotational grazing showed the maximum decrease of 54.52% compared to lambs under T3 (continuous grazing). There was a strong positive correlation noticed between the mean temperature of the day at each fortnight and the subsequent EPG at each fortnight with R2=0.87. There was a strong positive correlation noticed between mean FAMACHA® scores and the EPG with R2=0.84, R2=0.83, and R2=0.83 for T1, T2, and T3, respectively. Conclusion: The grazing management with pasture rotation should be considered as a viable option for sustainable parasitic control in case of grazing-dependent livestock husbandry in India.

South American camelids (SACs) play an increasing role in veterinary care in Europe. Many alpacas or llamas presented to veterinarians suffer from anaemia, regularly with a packed cell volume (PCV) below 0.10 l/l, which is a life-threatening condition for the animals. This review article presents clinical and laboratory diagnostic tools for the diagnosis of anaemia in SACs. Clinical identification of anaemic animals can be performed by assessing the FAMACHA© score and the Body Condition Score (BCS), since anaemia in alpacas and llamas correlates with pale mucous membranes and a lowered BCS. Haematological examination of a blood sample can provide a more differentiated diagnosis of anaemia in SACs. A common finding is regenerative anaemia with an increased number of reticulocytes that is often caused by blood loss due to Haemonchus contortus. Changes in a blood smear from an alpaca or llama with regenerative anaemia may include normoblasts (nucleated red blood cells), anisocytosis, poikilocytosis, polychromasia, Howell-Jolly bodies or basophilic stippling. Furthermore, non-regenerative anaemia, often caused by trace element deficiency or cachexia, can also occur.

Gastrointestinal parasitism is a health issue in livestock, particularly in non-intensive farming systems. This research evaluated the prevalence and risk factors associated with gastrointestinal helminths in goats from three ecosystems in Peru: the Andean shrubland (Ancash), dry forest (Lambayeque), and coastal valley (Lima). The study used a cross-sectional design, with random sampling of goats from extensive production systems in each ecosystem. A total of 819 fecal samples were collected and analyzed using qualitative and quantitative parasitological methods. Additionally, coproculture was performed to identify infective larvae of nematodes. The FAMACHA© index was used to assess anemia levels, while body condition scores were recorded to evaluate the nutritional status of the animals. The highest prevalence was recorded in the Andean shrubland (74.2%), followed by the dry forest (63.1%), whereas the coastal valley had the lowest prevalence (59.3%). The most frequently identified helminths were strongyle-type eggs (49.9%) and Skrjabinema sp. (33.7%), while Moniezia sp. (5.4%) and Fasciola hepatica (1.1%) were detected at lower frequencies. The identification of L3 infective larvae of Haemonchus sp., Trichostrongylus sp., Cooperia sp., Strongyloides sp., Oesophagostomum sp., Bunostomum sp., and Teladorsagia sp. highlighted the diversity of gastrointestinal nematodes affecting goats in Peru. Multivariable analysis revealed that anemia (FAMACHA ≥ 3; PR = 1.14), poor body condition (BCS 1–2; PR = 1.03), and age (2–6 teeths or full dentition; PR = 1.12 and 1.08, respectively) were associated with increased infection risk. Males had lower prevalence than females (PR = 0.80), and goats raised in the dry forest and coastal valley had lower risk than those from the Andean shrubland. These findings highlight the influence of physiological status and environmental conditions on parasite burden in goat herds.

Gastrointestinal nematodes, most notably trichostrongylids, are known to cause significant losses in sheep production. Previous studies have shown that monitoring parameters (e.g., FAMACHA©, BCS, dag score) change with increasing egg excretion. These parameters are well known and frequently used for targeted selective treatment. Based on the willingness to participate in this study (based on a previous questionnaire distribution among sheep farmers in Austria) we investigated the associations between faecal egg counts and the FAMACHA©, BCS, and dag scores of 1195 dairy ewes. Faecal samples were analysed using the Mini-FLOTAC technique I and larval culture. Three raters assessed the FAMACHA©, BCS, and dag scores in sheep to calculate the inter-rater agreement and intraclass correlation coefficient. The responses to the questionnaire of 23 farms were used for the evaluation, of which 16 farms were visited. Trichostrongylid eggs were detected in 95% of the faecal samples. The BCS was negatively correlated with the eggs per gram of faeces (EpG) (r = −0.156; p < 0.001) and the FAMACHA© score was slightly positively correlated with EpG (r = 0.196; p < 0.001). A small proportion of sheep (25%) shed the majority of eggs (47% to 84%). A moderate to good agreement for the parameters was found between the raters. In conclusion, the clinical parameters showed only weak correlations with faecal egg counts, and we confirmed that a minority of the flock is responsible for the majority of the pasture contamination with trichostrongylid eggs. Clinical raters should be trained before a study to increase the agreement between them.

Background and Aim: Previous studies recorded the prevalence of gastrointestinal nematodes (GIN) in Limpopo Province. However, the studies did not address the seasonal patterns of infection and did not cover all districts of Limpopo Province, namely; Capricorn, Sekhukhune, Waterberg, Mopani, and Vhembe. It is, therefore, important to provide up to date information on the prevalence and seasonal occurrence data of GIN in all districts of Limpopo province. The present study was conducted to determine the occurrence of anthelmintic resistance (AR) and document the prevalence of GIN infecting sheep in five districts of Limpopo Province, South Africa. Materials and Methods: Forty animals in each district were used for fecal egg count reduction test (FECRT) to determine AR against ivermectin (0.2 mg/kg), levamisole (LEV) (5 mg/kg), and albendazole (7.5 mg/kg). Egg hatch test (EHT) was used to determine AR against thiabendazole (TBZ) and micro-agar larval development test (MALDT) was used for both TBZ and LEV. Naturally, infected sheep (n=780) were sampled for prevalence across five districts of Limpopo. FAMACHA© eye-color score estimations were also performed for each study animal. Results: FECRT showed occurrence of AR in most of the districts and a few with suspected resistance. EHT results showed AR development against TBZ for all districts, while the MALDT showed no AR against LEV in all districts, but detected AR against TBZ in Sekhukhune, Capricorn, and Waterberg. Haemonchus contortus was the most resistant species. A high nematode prevalence (88-100%) and 1210-1861 eggs per gram (EPG) was observed in all districts during the hot wet season, decreasing to 75-80% (453-1202 EPG) during the cold dry season. The sheep revealed a FAMACHA© mean score of 3, indicating mild anemia during the hot wet season except for Vhembe district that revealed a FAMACHA© mean score of 4 during the hot wet season, indicating anemia. Conclusion: AR recorded in Limpopo Province may be due to under-dosing caused by lack of weighing equipment and high treatment frequencies due to lack of proper training on anthelmintic use. The detection of AR in Limpopo is an important finding because it will help in outlining effective management systems against GIN.

The aim of this study is to evaluate the model performance in the classification of FAMACHA© scores using Support Vector Machines (SVMs) with a focus on the estimation of the FAMACHA© scoring system used for early diagnosis and treatment management of parasitic infections. FAMACHA© scores are a color-based visual assessment system used to determine parasite load in animals, and in this study, the accuracy of the model was investigated. The model’s accuracy rate was analyzed in detail with metrics such as sensitivity, specificity, and positive/negative predictive values. The results showed that the model had high sensitivity and specificity rates for class 1 and class 3, while the performance was relatively low for class 2. These findings not only demonstrate that SVM is an effective method for classifying FAMACHA© scores but also highlight the need for improvement for class 2. In particular, the high accuracy rate (97.26%) and high kappa value (0.9588) of the model indicate that SVM is a reliable tool for FAMACHA© score estimation. In conclusion, this study demonstrates the potential of SVM technology in veterinary epidemiology and provides important information for future applications. These results may contribute to efforts to improve scientific approaches for the management of parasitic infections.

One of the most important activities in animal raising is parasite control. Since parasites share what animals eat on one hand, they have disease-causing effects on the other hand, they are parasites that must be fought. In fact, they can be responsible for the decrease in yield and can also cause death. Haemonchus contortus parasite from the Trichostrongylidae family is a blood parasite that causes anemia in sheep and goats. In this study, using the FAMACHA© card, which is widely used in the determination of anemia caused by Haemonchus contortus in the world, the color of the conjunctiva was scored in Akkaraman sheep and Hair goats raised in the Sızma district of Konya, and the relations between the score and the parasite type and density were determined. Accordingly, while no animals with 1 and 5 scores were observed in sheep, the numbers of animals with scores of 2, 3, and 4 were determined as 7.84%, 40.2%, and 52.96%, respectively. Parasite eggs belonging to the Trichostrongylidae family were observed in 96.07% of the sheep. On the other hand, no animal with score 1 or 2 was observed in goats. The numbers of animals with 3, 4, and 5 scores were determined as 6.12%, 54.08%, and 39.80%, respectively. Eimeria, the most common parasite species, was found in 68.18% of the goats and parasite eggs from the Trichostrongylidae family were observed in 17.05%. As a result, it was concluded that the card in question was a practical, easily available, and cheap tool in the detection of anemia by using the relationships between FAMACHA© card scoring and parasite loads.

Anemia caused by gastrointestinal parasitism is a major constraint to small ruminant productivity, particularly in low-resource production systems where diagnostic tools and veterinary access are limited, with use of FAMACHA as a biological reference This study evaluated the potential of radio-frequency non-destructive technique (RF-NDT) wave-derived features as non-invasive biomarkers for anemia detection in goats, using FAMACHA© scores as a biological reference. Variable clustering of the top ten frequencies revealed distinct patterns across health states. Healthy (FAMACHA© 1) animals were characterized by a single frequency cluster centered at 8.43 GHz, which explained 93.7% of variation, whereas moderately affected animals (FAMACHA© 2) shifted to 9.33 GHz with reduced uniformity (88.7%). Borderline animals (FAMACHA© 3) required two clusters (9.89 and 8.23 GHz), explaining 91.0% of variation, indicating increasing tissue heterogeneity with anemia progression. Regression analysis demonstrated strong predictive power, with Linear Regression achieving R2 = 1.00 and Random Forest R2 = 0.79 (RMSE = 0.07), Support Vector Regression underperformed (R2 = 0.31). Classification models confirmed the feasibility of categorical anemia detection. The Multilayer Perceptron achieved the highest accuracy (0.84), F1-score (0.83), and ROC-AUC (0.94), outperforming Support Vector Machine (accuracy 0.67, F1 = 0.67) and K-Nearest Neighbors (accuracy 0.60, F1 = 0.61). These findings establish proof-of-concept that RF waves capture physiologically meaningful dielectric signatures linked to anemia, reflecting hemoglobin concentration, hydration, and microcirculatory function. The integration of RF sensing with machine learning offers a rapid, and non-invasive scalable diagnostic approach. Future work should expand validation across breeds and environments, optimize sensor design, and embed neural classifiers for field-ready deployment.