The transition of arthropods, other invertebrates and microorganisms to the nest-burrow habitats was accompanied (if presented in a historical aspect) by an intensive speciation resulting in a rise of a peculiar life form, nidicoles. Now nidicoles are reported in such large taxons as Protophyta, Mycophyta, Protozoa, Nematoda and Arthropoda. The main part of nidicoles is represented by arthropods, members of the orders Acariformes, Parasitiformes, Blattoidea, Saltatoria, Mallophaga, Anoplura, Heteroptera, Coleoptera, Diptera and Siphonaptera.
Download full-text PDF |
Source |
|---|
Publication Analysis
Top Keywords
Similar Publications
Abdominal and Pelvic MRI Protocol Prediction Using Natural Language Processing.
J Imaging Inform Med
January 2025
Department of Radiology, Mayo Clinic, Rochester, MN, USA.
Exam protocoling is a significant non-interpretive task burden for radiologists. The purpose of this work was to develop a natural language processing (NLP) artificial intelligence (AI) solution for automated protocoling of standard abdomen and pelvic magnetic resonance imaging (MRI) exams from basic associated order information and patient metadata. This Institutional Review Board exempt retrospective study used de-identified metadata from consecutive adult abdominal and pelvic MRI scans performed at our institution spanning 2.
View Article and Find Full Text PDFMulti-omics sequencing of gastroesophageal junction adenocarcinoma reveals prognosis-relevant key factors and a novel immunogenomic classification.
Gastric Cancer
January 2025
Department of Biochemistry and Molecular Biology, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China.
Background: Gastroesophageal junction adenocarcinoma (GEJAC) exhibits distinct molecular characteristics due to its unique anatomical location. We sought to investigate effective and reliable molecular classification of GEJAC to guide personalized treatment.
Methods: We analyzed the whole genomic, transcriptomic, T-cell receptor repertoires, and immunohistochemical data in 92 GEJAC patients and delineated the landscape of genetic and immune alterations.
Risk prediction models for depression in patients with coronary heart disease: a systematic review and meta-analysis.
Front Cardiovasc Med
January 2025
School of Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Background: Risk prediction models for depression in patients with coronary heart disease are increasingly being developed. However, the quality and applicability of these models in clinical practice remain uncertain.
Objective: To systematically evaluate depression risk prediction models in patients with coronary heart disease (CHD).
Fully automated segmentation and classification of renal tumors on CT scans via machine learning.
BMC Cancer
January 2025
Department of Urology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.
Background: To develop and test the performance of a fully automated system for classifying renal tumor subtypes via deep machine learning for automated segmentation and classification.
Materials And Methods: The model was developed using computed tomography (CT) images of pathologically proven renal tumors collected from a prospective cohort at a medical center between March 2016 and December 2020. A total of 561 renal tumors were included: 233 clear cell renal cell carcinomas (RCCs), 82 papillary RCCs, 74 chromophobe RCCs, and 172 angiomyolipomas.
Mining versatile feruloyl esterases: phylogenetic classification, structural features, and deep learning model.
Bioresour Bioprocess
January 2025
Key Laboratory of Pollution Exposure and Health Intervention of Zhejiang Province, College of Biological and Environment Engineering, Zhejiang Shuren University, Hangzhou, 310015, China.
Feruloyl esterases (FEs, EC 3.1.1.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!