A novel kit for enrichment of fecal helminth eggs.

We developed a new concentration kit, called the ParaEgg (PE), for easy detection trematode eggs from fecal samples in endemic areas of clonorchiasis and metagonimiasis in Korea. To create a standard of detection efficiency, 120 fecal samples were examined using the water-ether concentration method (WECM). The PE kit and Mini ParaSep (PS) kit were used to compare the detection sensitivity of 100 egg-positive and 20 egg-negative samples in WECM. Additionally, stool samples, which were intentionally spiked with 10, 20, and 30 Clonorchis sinensis eggs, were evaluated to assess the sensitivity in lowinfection cases. The PE and PS kits showed detection rates of 100% and 92%, respectively, from 100 egg-positive samples in WECM. Meanwhile, eggs were detected in 3 (PE) and 2 (PS) out of 20 egg-negative samples in WECM. The PE kit detected the highest number of eggs per gram of feces (727 on average), followed by the WECM (524) and PS kit (432). In fecal samples that were intentionally spiked with 10, 20, and 30 C. sinensis eggs, PE only detected eggs 2 out of 5 samples in 10 eggs spiked (40%), and the detection rates were 80% and 100%, respectively. The PE kit enabled a more accurate identification of trematode eggs because of the clearance of small fecal debris in the microscopic field. In conclusion, the PE kit is obviously helpful to detect and identify trematode eggs in stool examinations especially in endemic areas of clonorchiasis and metagonimiasis.

Automatic detection of Opisthorchis viverrini egg in stool examination using convolutional-based neural networks.

Background: Human opisthorchiasis is a dangerous infectious chronic disease distributed in many Asian areas in the water-basins of large rivers, Siberia, and Europe. The gold standard for human opisthorchiasis laboratory diagnosis is the routine examination of Opisthorchis spp. eggs under a microscope. Manual detection is laborious, time-consuming, and dependent on the microscopist's abilities and expertise. Automatic screening of Opisthorchis spp. eggs with deep learning techniques is a useful diagnostic aid. Methods: Herein, we propose a convolutional neural network (CNN) for classifying and automatically detecting O. viverrini eggs from digitized images. The image data acquisition was acquired from infected human feces and was processed using the gold standard formalin ethyl acetate concentration technique, and then captured under the microscope digital camera at 400x. Microscopic images containing artifacts and O.viverrini egg were augmented using image rotation, filtering, noising, and sharpening techniques. This augmentation increased the image dataset from 1 time to 36 times in preparation for the training and validation step. Furthermore, the overall dataset was subdivided into a training-validation and test set at an 80:20 ratio, trained with a five-fold cross-validation to test model stability. For model training, we customized a CNN for image classification. An object detection method was proposed using a patch search algorithm to detect eggs and their locations. A performance matrix was used to evaluate model efficiency after training and IoU analysis for object detection. Results: The proposed model, initially trained on non-augmented data of artifacts (class 0) and O. viverrini eggs (class 1), showed limited performance with 50.0% accuracy, 25.0% precision, 50.0% recall, and a 33.0% F1-score. After implementing data augmentation, the model significantly improved, reaching 100% accuracy, precision, recall, and F1-score. Stability assessments using 5-fold cross-validation indicated better stability with augmented data, evidenced by an ROC-AUC metric improvement from 0.5 to 1.00. Compared to other models such as ResNet50, InceptionV3, VGG16, DenseNet121, and Xception, the proposed model, with a smaller file size of 2.7 MB, showed comparable perfect performance. In object detection, the augmented data-trained model achieved an IoU score over 0.5 in 139 out of 148 images, with an average IoU of 0.6947. Conclusion: This study demonstrated the successful application of CNN in classifying and automating the detection of O. viverrini eggs in human stool samples. Our CNN model's performance metrics and true positive detection rates were outstanding. This innovative application of deep learning can automate and improve diagnostic precision, speed, and efficiency, particularly in regions where O. viverrini infections are prevalent, thereby possibly improving infection sustainable control and treatment program.

From sieve to microscope: An efficient technique for sample transfer in the process of microplastics' quantification.

In the field of microplastics' quantification, efficient and reproducible methodology is still needed. Procedures of sample fractionation and transfer are often insufficiently reported, although fractionating a sample in similarly sized particles is a crucial prerequisite for the subsequent detection and identification process. At the same time, fractionation is error-prone as particles can be lost during transfer between different vessels. This article presents a four-step technique of sample preparation and microscopic examination, suited for different kind of environmental samples (e.g., water, sediment, soil): The sample is size-fractionated in a sieve cascade (I), rinsed from the sieve and vacuum-filtrated onto a filter (II), rinsed from the filter into a glass petri dish with a low amount of water (III), and examined under the microscope in wet or dry condition (IV). The technique manages on standard laboratory equipment and is reliable for fragments > 300 µm: In a validation experiment with polypropylene, the average recovery was 94 ± 13.5% (arithmetic mean ± standard deviation) and 100% (median), respectively.•Reliable sample transfer after wet-sieving.•Concentration of the pretreated sample in a very small amount of water.•Usage of transmitted light in microscopy.

Sujidades e fraudes em chocolates / Light filth and adulteration in chocolate

Foram analisadas 140 amostras de produtos de cacau, sendo 67 de chocolate em pó e 73 de chocolate em tablete com a finalidade de verificar as condições higiênicas desses produtos, propor limites de tolerância para sujidades leves e pesquisar fraudes. O método utilizado para pesquisa de sujidades leves foi descrito no AOAC 14.ed. com modificações e, para a pesquisa de fraudes, o método foi desenvolvido na Seção de Microscopia Alimentar do Instituto Adolfo Lutz, baseado em Wallis. Concluiu-se que 92,14% do total das amostras continham de 0 a 10 fragmentos de insetos; 2,14% continham pelos de roedor e 2,50% estavam parasitadas por ácaros. Sugeriu-se modificações na legislação atual, propondo-se um limite de tolerância de até 10 fragmentos de insetos a cada 50g de chocolate em pó ou 100g de chocolate em tabletes. Sugeriu-se também, que seja mantida a exigência que proíbe a presença de ácaros e de pelos de roedor nesses produtos. Quanto a fraudes, verificou-se que 10,07% das amostras estavam fraudadas com amido de milho e que 8,57% continham soja, em desacordo com a formulação. Propôs-se que seja revogado o parecer aprovado pela Câmara Técnica de Alimentos em 14/12/79 que considera lícito o uso de farinha de soja integral pré-cozida nos produtos de cacau, chocolates, bombons e similares (AU).

Avaliação microscópica das condições higiênicas de extrato e purê de tomate e de catchup / Tomato paste, tomato puree and catchup: microscopic evaluation and sanitary conditions

Foram analisadas 152 amostras de extrato de tomate, purê de tomate e catchup, para averiguar sua qualidade, através da pesquisa de sujidades pelo método de extração em frasco de Wildman. As sujidades encontradas foram: fragmentos de insetos, fragmentos de larvas, larvas, ovos e nematóides. Concluiu-se que a maior percentagem de contaminação ocorreu em amostras que apresentavam somente fragmentos de insetos, com 61,2% para extratos de tomate, 58,1% para purês de tomate e 61,8% para catchups. Os resultados obtidos sugerem a necessidade de modificação na legislação brasileira, estabelecendo limite de tolerância para fragmentos de insetos, e maior controle de qualidade, por parte das indústrias, dos produtos à base de tomate (AU).

Partículas metálicas em farinha de trigo / Metal particles in wheat flour / Metal particles in wheat flour

Foram analisadas 460 amostras de farinha de trigo, recolhidas no comércio da cidade de São Paulo, nas diferentes fases de processamento, com a finalidade de pesquisar partículas metálicas. Constatou-se que 43,69% das amostras não apresentavam partículas metálicas, 28,04% apresentavam de 1 a 50 filamentos, 27,61% apresentavam de 1 a 50 grânulos, 3,70% apresentavam de 51 a 100 grânulos, 1,74% de 101 a 150 grânulos e 6,52% das amostras apresen.tavarn número de grânulos metálicos maior que 150. Em 16,09% das amostras foram encontrados tanto grânulos como filamentos metálicos. Concluiu-se que a presença de partículas metálicas na farinha traduz as condições de conservação e tecnologia dos moinhos. Foi enfatizado que a presença de partículas metálicas em alimentos pode causar risco potencial à saúde (AU).

Sujidades em farinhas de trigo, mandioca, milho, e em fubá de milho / Filths in wheat flour, cassava flour, corn flour and corn meal

Foram analisadas 1.045 amostras de farinhas dos tipos trigo, mandioca, milho e fubá de milho, de março de 1980 a outubro de 1985, com a finalidade de verificar, através da pesquisa de sujidades pelo método de digestão com pancreatina, as condições higiênicas desses alimentos. Concluiu-se que 43% das amostras de farinha de trigo, 20/0 das de farinha de mandioca, 90/0 das de farinha de milho e 80/0 das de fubá de milho estavam em condições higiênicas insatisfatórias e 4% das de farinha de trigo, 120/0 das de farinha de mandioca, 50/0 das de farinha de milho e 30/0 das de fubá de milho estavam impróprias para o consumo, de acordo com critérios preestabelecidos de limite de tolerância de defeitos. Foi sugerida modificação no Código Alimentar Brasileiro, substituindo a exigência de ausência de sujidades para uma tolerância de até 10, quando se tratar de fragmentos de insetos (AU).