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DNA barcoding and morphological characters were used to identify adult snails belonging to the genus from 17 municipalities in the state of São Paulo, Brazil. The DNA barcode analysis also included twenty-nine sequences retrieved from GenBank. The final data set of 104 sequences of the mitochondrial cytochrome oxidase I (COI) gene was analyzed for K2P intraspecific and interspecific divergences, through tree-reconstruction methods (Neighbor-Joining, Maximum Likelihood and Bayesian inference), and by applying different models (ABGD, bPTP, GMYC) to partition the sequences according to the pattern of genetic variation. Twenty-seven morphological parameters of internal organs were used to identify specimens. The molecular taxonomy of agreed with the morphological identification of specimens from the same collection locality. DNA barcoding may therefore be a useful supporting tool for identifying snails in areas at risk for schistosomiasis.
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http://dx.doi.org/10.3897/zookeys.668.10562 | DOI Listing |
Cell Physiol Biochem
September 2025
Department of Histology and Embryology and Vascular Biology Student Research Club, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, 85-092 Bydgoszcz, Poland, E-Mail:
Migrasomes are newly discovered, migration-dependent organelles that mediate the release of cellular contents into the extracellular environment through a process known as migracytosis. Since their identification in 2014, growing evidence has highlighted their critical roles in intercellular communication, organ development, mitochondrial quality control, and disease pathogenesis. Migrasome biogenesis is a complex, multi-step process tightly regulated by lipid composition, tetraspanin-enriched microdomains, and molecular pathways involving sphingomyelin synthase 2, Rab35, and integrins.
View Article and Find Full Text PDFJ Hazard Mater
September 2025
Mining and Minerals Engineering, Virginia Tech, Blacksburg, VA, USA. Electronic address:
Occupational lung disease remains a serious concern among miner workers, underscoring the need for improved characterization of respirable dust. Scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDX) enables high-resolution analysis of filter samples, but accurate identification of complex, multi-constituent particles like agglomerates during direct-on-filter (DOF) analysis remains challenging. This is because standard tools for automated SEM-EDX treat each dust entity as an independent particle.
View Article and Find Full Text PDFActa Trop
September 2025
Department of Biology, College of Natural Sciences, Kyungpook National University, Daegu, 41566, Republic of Korea; School of Life Sciences, BK21 FOUR KNU Creative BioResearch Group, Kyungpook National University, Daegu, 41566, Republic of Korea; G-LAMP Project Group, Kyungpook National University,
Culicoides spp. (Diptera: Ceratopogonidae) are vectors of livestock diseases, including bluetongue, Akabane, and African horse sickness. Accurate species identification is a crucial first step in effective vector management.
View Article and Find Full Text PDFJ Forensic Leg Med
September 2025
China People's Police University, Langfang, 065000, China.
Forensic identification at fire scenes faces three core challenges: distinguishing cause of death (antemortem burning versus postmortem corpse burning), reconstructing criminal behavior (arson versus accident), and preserving evidence (thermal destruction versus artificial tampering). This case study systematically demonstrates the application value of burn trace characteristics in arson investigation through a typical intentional homicide and corpse burning case. Based on a three-dimensional analytical framework of human burn-behavioral characteristics, a systematic pathway incorporating reconstruction of arson/corpse burning processes and identification of body relocation behavior was established.
View Article and Find Full Text PDFPLoS Comput Biol
September 2025
Arbovirus and Entomology Department, Bernhard Nocht Institute for Tropical Medicine, Hamburg, Germany.
This study addresses the pressing global health burden of mosquito-borne diseases by investigating the application of Convolutional Neural Networks (CNNs) for mosquito species identification using wing images. Conventional identification methods are hampered by the need for significant expertise and resources, while CNNs offer a promising alternative. Our research aimed to develop a reliable and applicable classification system that can be used under real-world conditions, with a focus on improving model adaptability to unencountered devices, mitigating dataset biases, and ensuring usability across different users without standardized protocols.
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