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Background: Various shapes of gastropod shells have evolved ever since the Cambrian. Although theoretical analyses of morphogenesis exist, the molecular basis of shell development remains unclear. We compared expression patterns of the decapentaplegic (dpp) gene in the shell gland and mantle tissues at various developmental stages between coiled-shell and non-coiled-shell gastropods.
Results: We analyzed the expression patterns of dpp for the two limpets Patella vulgata and Nipponacmea fuscoviridis, and for the dextral wild-type and sinistral mutant lineage of the pond snail Lymnaea stagnalis. The limpets had symmetric expression patterns of dpp throughout ontogeny, whereas in the pond snail, the results indicated asymmetric and mirror image patterns between the dextral and sinistral lineages.
Conclusion: We hypothesize that Dpp induces mantle expansion, and the presence of a left/right asymmetric gradient of the Dpp protein causes the formation of a coiled shell. Our results provide a molecular explanation for shell, coiling including new insights into expression patterns in post-embryonic development, which should aid in understanding how various shell shapes are formed and have evolved in the gastropods.
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http://dx.doi.org/10.1186/2041-9139-4-15 | DOI Listing |
Elife
September 2025
Human Biology and Primate Evolution, Institute of Biology, Freie Universität Berlin, Berlin, Germany.
Evidence indicates that transposable elements (TEs) can contribute to the evolution of new traits, with some TEs acting as deleterious elements while others are repurposed for beneficial roles in evolution. In mammals, some KRAB-ZNF proteins can serve as a key defense mechanism to repress TEs, offering genomic protection. Notably, the family of KRAB-ZNF genes evolves rapidly and exhibits diverse expression patterns in primate brains, where some TEs, including autonomous LINE-1 and non-autonomous Alu and SVA elements, remain mobile.
View Article and Find Full Text PDFJMIR Hum Factors
September 2025
KK Women's and Children's Hospital, Singapore, Singapore.
Background: Breast cancer treatment, particularly during the perioperative period, is often accompanied by significant psychological distress, including anxiety and uncertainty. Mobile health (mHealth) interventions have emerged as promising tools to provide timely psychosocial support through convenient, flexible, and personalized platforms. While research has explored the use of mHealth in breast cancer prevention, care management, and survivorship, few studies have examined patients' experiences with mobile interventions during the perioperative phase of breast cancer treatment.
View Article and Find Full Text PDFEur J Clin Microbiol Infect Dis
September 2025
School of Bioengineering and Biosciences, Department of Biochemistry, Lovely Professional University, Punjab, 144411, India.
Purpose: This study investigates codon usage and amino acid usage bias in the genus Acinetobacter to uncover the evolutionary forces shaping these patterns and their implications for pathogenicity and biotechnology.
Methods: Codon usage patterns were examined in representative genomes of the genus Acinetobacter using standard codon bias indices, including GC content, relative synonymous codon usage (RSCU), effective number of codons (ENC), and codon adaptation index (CAI). Neutrality and parity plots were employed to evaluate the relative influence of mutational pressure and natural selection on codon preferences.
Adv Health Sci Educ Theory Pract
September 2025
Department of Health Professions Education, Department of Medicine, Uniformed Services University of the Health Sciences (USUHS), 4301 Jones Bridge Road, Bethesda, MD, 20814, USA.
Drugs Aging
September 2025
Dalla Lana School of Public Health, University of Toronto, V1 06, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada.
Background And Objectives: Older adults living with dementia are a heterogeneous group, which can make studying optimal medication management challenging. Unsupervised machine learning is a group of computing methods that rely on unlabeled data-that is, where the algorithm itself is discovering patterns without the need for researchers to label the data with a known outcome. These methods may help us to better understand complex prescribing patterns in this population.
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