Occurrence of haemoparasites in cattle slaughtered at Jalingo abattoir, north-eastern Nigeria

Livestock plays a significant role in the economy of a nation but its productivity can be hampered by numerous haemoparasites thereby leading to economic losses to the livestock industry. The prevalence of haemoparasite in cattle slaughtered at Jalingo abattoir was investigated. A total of four hundred blood samples were collected at the point of slaughter, processed, and screened for haemoparasites by examining Giemsa-stained thin blood smears. An overall prevalence of 12.25% was recorded. Four haemoparasites of cattle with prevalence rates of 5.0%, 6.75%, 0.25%, and 0.25% for Anaplasma, Babesia, Microfilaria and Trypanosoma respectively were observed. The prevalence of haemoparasite in relation to sex, revealed higher infection in females (13.75%) than in males (10.0%) which were not found statistically different (P > 0.05). All breeds encountered during the study were infected with haemoparasites with the highest prevalence of 13.91 % recorded in White Fulani, Red Bororo (10.94%), and Sokoto Gudali (10.00 %), while Adamawa Gudali had the least prevalence of 0.5%. There was no statistically significant difference in the prevalence of haemoparasite in relation to breeds (P > 0.05). High prevalence was observed in the young (14.29%) more than the adult (11.59%) and older (12.14%). The current study has revealed the haemoparasites status in cattle slaughtered at Jalingo abattoir. Therefore, there is a need for effective preventive and control policy of these haemoparasites to enhance livestock productivity. Keywords: Abattoir, cattle, haemoparasite, prevalence, slaughter

Automated detection and staging of malaria parasites from cytological smears using convolutional neural networks

Microscopic examination of blood smears remains the gold standard for diagnosis and laboratory studies with malaria. Inspection of smears is, however, a tedious manual process dependent on trained microscopists with results varying in accuracy between individuals, given the heterogeneity of parasite cell form and disagreement on nomenclature. To address this, we sought to develop an automated image analysis method that improves accuracy and standardisation of cytological smear inspection but retains the capacity for expert confirmation and archiving of images. Here we present a machine-learning method that achieves red blood cell (RBC) detection, differentiation between infected and uninfected RBCs and parasite life stage categorisation from raw, unprocessed heterogeneous images of thin blood films. The method uses a pre-trained Faster Region-Based Convolutional Neural Networks (R-CNN) model for RBC detection that performs accurately, with an average precision of 0.99 at an intersection-over-union threshold of 0.5. A residual neural network (ResNet)-50 model applied to detect infection in segmented RBCs also performs accurately, with an area under the receiver operating characteristic curve of 0.98. Lastly, using a regression model our method successfully recapitulates intra-erythrocytic developmental cycle (IDC) stages with accurate categorisation (ring, trophozoite, schizont), as well as differentiating asexual stages from gametocytes. To accelerate our method’s utility, we have developed a mobile-friendly web-based interface, PlasmoCount, which is capable of automated detection and staging of malaria parasites from uploaded heterogeneous input images of Giemsa-stained thin blood smears. Results gained using either laboratory or phone-based images permit rapid navigation through and review of results for quality assurance. By standardising the assessment of parasite development from microscopic blood smears, PlasmoCount markedly improves user consistency and reproducibility and thereby presents a realistic route to automating the gold standard of field-based malaria diagnosis.Significance StatementMicroscopy inspection of Giemsa-stained thin blood smears on glass slides has been used in the diagnosis of malaria and monitoring of malaria cultures in laboratory settings for >100 years. Manual evaluation is, however, time-consuming, error-prone and subjective with no currently available tool that permits reliable automated counting and archiving of Giemsa-stained images. Here, we present a machine learning method for automated detection and staging of parasite infected red cells from heterogeneous smears. Our method calculates parasitaemia and frequency data on the malaria parasite intraerythrocytic development cycle directly from raw images, standardizing smear assessment and providing reproducible and archivable results. Developed into a web tool, PlasmoCount, this method provides improved standardisation of smear inspection for malaria research and potentially field diagnosis.

Brightfield and fluorescence in-channel staining of thin blood smears generated in a pumpless microfluidic

Effective staining of peripheral blood smears which enhances the contrast of intracellular components and biomarkers is essential for the accurate characterization, diagnosis, and monitoring of various diseases such as malaria.

A novel deep learning-based point-of-care diagnostic method for detecting Plasmodium falciparum with fluorescence digital microscopy

Background Malaria remains a major global health problem with a need for improved field-usable diagnostic tests. We have developed a portable, low-cost digital microscope scanner, capable of both brightfield and fluorescence imaging. Here, we used the instrument to digitize blood smears, and applied deep learning (DL) algorithms to detect Plasmodium falciparum parasites. Methods Thin blood smears (n = 125) were collected from patients with microscopy-confirmed P. falciparum infections in rural Tanzania, prior to and after initiation of artemisinin-based combination therapy. The samples were stained using the 4′,6-diamidino-2-phenylindole fluorogen and digitized using the prototype microscope scanner. Two DL algorithms were trained to detect malaria parasites in the samples, and results compared to the visual assessment of both the digitized samples, and the Giemsa-stained thick smears. Results Detection of P. falciparum parasites in the digitized thin blood smears was possible both by visual assessment and by DL-based analysis with a strong correlation in results (r = 0.99, p < 0.01). A moderately strong correlation was observed between the DL-based thin smear analysis and the visual thick smear-analysis (r = 0.74, p < 0.01). Low levels of parasites were detected by DL-based analysis on day three following treatment initiation, but a small number of fluorescent signals were detected also in microscopy-negative samples. Conclusion Quantification of P. falciparum parasites in DAPI-stained thin smears is feasible using DL-supported, point-of-care digital microscopy, with a high correlation to visual assessment of samples. Fluorescent signals from artefacts in samples with low infection levels represented the main challenge for the digital analysis, thus highlighting the importance of minimizing sample contaminations. The proposed method could support malaria diagnostics and monitoring of treatment response through automated quantification of parasitaemia and is likely to be applicable also for diagnostics of other Plasmodium species and other infectious diseases.

A novel deep learning-based point-of-care diagnostic method for detecting Plasmodium falciparum with fluorescence digital microscopy

Background Malaria remains a major global health problem with a need for improved field-usable diagnostic tests. We have developed a portable, low-cost digital microscope scanner, capable of both brightfield and fluorescence imaging. Here, we used the instrument to digitize blood smears, and applied deep learning (DL) algorithms to detect Plasmodium falciparum parasites. Methods Thin blood smears (n = 125) were collected from patients with microscopy-confirmed P. falciparum infections in rural Tanzania, prior to and after initiation of artemisinin-based combination therapy. The samples were stained using the 4′,6-diamidino-2-phenylindole fluorogen and digitized using the prototype microscope scanner. Two DL algorithms were trained to detect malaria parasites in the samples, and results compared to the visual assessment of both the digitized samples, and the Giemsa-stained thick smears. Results Detection of P. falciparum parasites in the digitized thin blood smears was possible both by visual assessment and by DL-based analysis with a strong correlation in results (r = 0.99, p < 0.01). A moderately strong correlation was observed between the DL-based thin smear analysis and the visual thick smear-analysis (r = 0.74, p < 0.01). Low levels of parasites were detected by DL-based analysis on day three following treatment initiation, but a small number of fluorescent signals were detected also in microscopy-negative samples. Conclusion Quantification of P. falciparum parasites in DAPI-stained thin smears is feasible using DL-supported, point-of-care digital microscopy, with a high correlation to visual assessment of samples. Fluorescent signals from artefacts in samples with low infection levels represented the main challenge for the digital analysis, thus highlighting the importance of minimizing sample contaminations. The proposed method could support malaria diagnostics and monitoring of treatment response through automated quantification of parasitaemia and is likely to be applicable also for diagnostics of other Plasmodium species and other infectious diseases.

Short Communication: Erythrocytes and leukocytes profiles of bottlenose dolphins (Tursiops aduncus) at conservation site

Satyaningtijas AS, Indrawati A, Syarafina RF, Milani TF, Saleema AK. 2020. Short Communication: Erythrocytes and leukocytes profiles of bottlenose dolphins (Tursiops aduncus) at conservation site. Biodiversitas 21: 3359-3363. Health monitoring of dolphins to ensure optimal welfare in human care is important. This study measured erythrocytes and leukocytes in seven bottlenose dolphins (Tursiops aduncus) as a parameter to assess physiological status in PT. Wersut Seguni Indonesia (WSI) Marine Mammals Conservation. Blood was sampled through the superficial veins of each tail fin for examination of erythrocytes and leukocytes using hemocytometer method. Differential leukocytes were observed using thin blood smears stained by Giemza. The purpose of this study to collect hematological value of captive bottlenose dolphins. The results showed values of erythrocytes was (5.14±0.56) x 106/mm3, (13.86±1.68) gr/dl for hemoglobin, and (44.29±2.69)% for hematocrit. The value for leukocytes were (4.2 ± 0,82) x 103/mm3 , 63.14 ± 9.77% for lymphocytes, 3.57 ± 1.72% for monocytes, 31.57 ± 8.43% for neutrophils, 1.29 ± 1.60% for eosinophils and 0.14 ± 0.38% for basophils. The average of the seven bottlenose dolphins Neutrophil to Lymphocyte ratio (N/L) was 0.53.

Malaria Trophozoite

A 71-year-old man living in El Salvador developed anemia, indirect bilirubinemia and intermittent fever for the past several months. Ultrasound of the abdomen revealed mild splenomegaly. Both thick and thin blood smears for malaria failed to show malaria species.