№ files_lp_3_process_9_26228
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This document contains statistical data on the stratification of age-adjusted incidence rates of various types of malignant lymphomas based on histology, as part of a broader oncological research study.
Year:
2020
Region / City:
United States
Topic:
Oncology, Hematology
Document Type:
Research data
Organization / Institution:
N/A
Author:
N/A
Target Audience:
Researchers, medical professionals
Period of Validity:
N/A
Approval Date:
N/A
Date of Changes:
N/A
Price: 8 / 10 USD
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The product description is provided for reference. Actual content and formatting may differ slightly.
Note:
Year
Contextual description:
A research paper providing advanced imaging analysis methods for refining risk assessment in high-risk neuroblastoma patients.
Year:
2025
Type of document:
Supplementary digital content
Topic:
Computational preoperative risk stratification in pancreatic surgery
Methods:
Radiomics, feature extraction, hierarchical clustering, machine learning models
Patient cohorts:
Dresden (Development), Heidelberg (External Test)
Imaging modality:
CT
Software/Tools:
TotalSegmentator v1.5.6, MIRP v2.0.0
Number of extracted features:
654
Cluster analysis:
Hierarchical agglomerative clustering
Comparison:
AutoFRS vs traditional FRS risk stratification
Content sections:
Patient selection, CT imaging characteristics, Totalsegmentator configuration, MIRP feature extraction, Feature clustering, Radiomics and clinical signatures, Evaluation of models, Web application, Glossary
Year:
2023
Region / city:
Wuhan, China
Topic:
Medical Imaging, Artificial Intelligence, Gastrointestinal Stromal Tumors
Document Type:
Research Article
Organization / Institution:
Renmin Hospital of Wuhan University
Authors:
Chenxia Zhang, Wei Tan, Xun Li, Xiao Tao, Bing Xiao, Wei Zhou, Honggang Yu
Target Audience:
Medical professionals, researchers in gastroenterology and artificial intelligence
Period of Application:
Not specified
Date of Approval:
Not specified
Date of Changes:
Not specified
Keywords:
Gastrointestinal Stromal Tumors, Endoscopic Ultrasound, Artificial Intelligence, Risk Stratification, Deep Learning
Methodology:
Image Classification, Image Segmentation, Deep Convolutional Neural Network (DCNN), ResNet, Unet++
Evaluation Metrics:
Accuracy, Intersection over Union (IoU), Precision, Recall, F1 score
Conclusion:
The use of AI-based risk stratification and image analysis improves GIST diagnosis accuracy
References:
Li J, Zhu Y, Dong Z, et al., Yao L, Zhang J, Liu J, et al., Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J
Risk Stratification in Febrile Neutropenic Episodes in Adolescent / Young Adult Patients with Cancer
Year:
2023
Region / city:
United Kingdom, Canada, Switzerland, The Netherlands, Germany
Subject:
Risk Stratification in Medical Research
Document Type:
Research Article
Organization / Institution:
University of York, Leeds Teaching Hospitals, University of Leicester, The Hospital for Sick Children, University of Bern, University Medical Center Groningen, Johann Wolfgang Goethe University
Author(s):
Robert S. Phillips, Kaljit Bhuller, Lillian Sung, Roland A. Ammann, Wim J. E. Tissing, Thomas Lehrnbecher, Lesley A Stewart
Target Audience:
Medical Researchers, Oncologists, Pediatricians, Healthcare Professionals
Period of Validity:
Not specified
Approval Date:
Not specified
Date of Changes:
Not specified
Year:
2021
Region / City:
N/A
Subject:
Tuberculosis, Diagnostic Methods, Treatment Stratification
Document Type:
Research Supplement
Institution:
N/A
Authors:
Daniel J. Grint, Jasvir Dhillon, Philip D. Butcher, Jack Adams, Tulika Munshi, Adam Witney, Kate Gould, Ken Laing, Christopher Cousins, Sean Wasserman, Katherine Fielding, Tom Harrison, Amina Jindani
Target Audience:
Healthcare professionals, researchers, clinicians
Period of Effectiveness:
N/A
Date of Approval:
N/A
Date of Changes:
N/A
Year:
2016
Region / city:
UK
Topic:
Cellular pathology, Thyroid cytology
Document type:
Audit template
Organization / institution:
Royal College of Pathologists, British Association of Endocrine and Thyroid Surgeons (BAETS)
Author:
Not specified
Target audience:
Pathologists, Surgeons
Period of validity:
Not specified
Approval date:
Not specified
Date of changes:
Not specified
Note:
Year
Subject:
Cellular pathology, thyroid cytology
Document type:
Audit template
Organization / Institution:
The Royal College of Pathologists
Target audience:
Pathologists, endocrinologists, surgeons
Contextual description:
A clinical audit template for evaluating the accuracy of thyroid cytology reports in relation to histological findings, aimed at improving reporting practices in cellular pathology.
Year:
2026
Region / city:
Multiple centers
Topic:
Medical research, Crohn’s disease
Document type:
Research study
Organization / institution:
Various medical institutions
Author:
Not specified
Target audience:
Medical professionals, researchers
Period of validity:
N/A
Approval date:
N/A
Modification date:
N/A
Year:
2026
Region / city:
Not specified
Topic:
Toxicity, drug efficacy
Document type:
Scientific figure
Organization / institution:
Not specified
Author:
Not specified
Target audience:
Researchers, scientists
Period of validity:
Not specified
Approval date:
Not specified
Date of changes:
Not specified
Year:
N/A
Region / City:
N/A
Topic:
Histology, Lung Damage
Document Type:
Table
Organization / Institution:
N/A
Author:
N/A
Target Audience:
Researchers, Medical Professionals
Period of validity:
N/A
Approval Date:
N/A
Date of changes:
N/A
Year:
2016-2017
Department:
Anatomy & Histology
Author:
Dr. Rajaa Ali
Topic:
Histology of the eye and related structures
Document Type:
Educational Text
Target Audience:
Medical students, researchers in anatomy
Date of Publication:
2016-2017
Date of Last Update:
Not specified
Region:
Not specified
Institution:
Department of Anatomy & Histology
Year:
2022
Region / City:
Not specified
Topic:
Cancer research, genomics, histology
Document Type:
Supplementary information
Organization / Institution:
The Jackson Laboratory for Genomic Medicine, Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), and others
Author:
Brian S White, Xing Yi Woo, Soner Koc, Todd Sheridan, Steven B Neuhauser, et al.
Target Audience:
Researchers, clinicians, and institutions in genomics and cancer research
Period of validity:
Not specified
Approval Date:
Not specified
Date of Changes:
Not specified
Contextual description:
Supplementary information for a genomic and histological image repository enabling deep learning analysis in cancer research.
Year:
2016/2017
Institution:
Maasai Mara University
School:
School of Science and Information Sciences
Degree Program:
Bachelor of Education (Science) & Science
Course Code:
ZOO 311
Course Title:
Physiology and Histology
Semester:
Third Year, First Semester
Exam Date:
24 January 2017
Exam Duration:
1100-1300 hrs
Exam Type:
Written University Examination
Audience:
Undergraduate students in Science Education
Sections:
Section A – All Questions, Section B – Any Two Questions
Marks Allocation:
Section A – 30 marks, Section B – 40 marks
Document type:
Supplementary methods section
Scientific field:
Molecular medicine and tumor immunology
Techniques described:
Immunofluorescence histology, multiplex antibody staining, next-generation sequencing
Biological material:
Tumor biopsy FFPE tissue sections
Immune markers analyzed:
CD8+, CD4+, Granzyme B
Sequencing platforms:
Illumina NovaSeq 6000
Bioinformatics tools:
trim_galore, STAR, featureCounts, DESeq2, GATK HaplotypeCaller, MuTect, Vardict
Genome reference:
Human genome hg38 with appended ONCOS-102 genome
Imaging equipment:
Panoramic 250 FLASH whole-slide scanner
Institutions mentioned:
Institute for Molecular Medicine Helsinki; Personalis Inc., California, USA
Sample preparation:
3.5 μm FFPE tissue sections
Staining reagents:
TSA 647, TSA 750, DAPI nuclear stain
Analysis focus:
Detection and quantification of immune cell infiltrates and differential gene expression in tumor biopsies
Year:
2026
Subject:
Human Anatomy and Histology
Type of Document:
Examination / Question Paper
Target Audience:
Medical and Health Science Students
Format:
Multiple Choice, Fill-in-the-Blank, True/False, Essay
Topics Covered:
Cells, Tissues, Muscles, Nervous System, Connective Tissue, Epithelium, Cartilage, Genetic Disorders
Institution:
Medical Faculty / Health Sciences Department
Language:
English
Research Field:
Immunology
Subfield:
Host Defense and Sepsis
Experimental Model:
C57BL/6 and C57BL/6J mice
Biological Focus:
Macrophage function and bacterial clearance
Bacterial Species:
Pseudomonas aeruginosa
Laboratory Techniques:
CFU determination, ELISA, flow cytometry, Western blot, siRNA interference, fluorescence microscopy
Cell Type:
Mouse peritoneal macrophages (PMφs)
Tissues Examined:
Lung, liver, spleen, kidney
Biochemical Markers:
ALT, AST, LDH, creatinine
Cytokines and Chemokines Measured:
Prokineticin-2, TNF-α, IL-6, IL-10, IL-17A
Experimental Interventions:
rPK2 stimulation, PKR1/PKR2 siRNA transfection, macrophage depletion with clodronate liposomes
Time Points of Analysis:
24 hours and 48 hours after modeling
Detection Methods:
Hematoxylin-eosin staining, fluorescence microscopy, FACScan flow cytometry, ELISA assays
Software for Analysis:
FCM Express
Referenced Studies:
Song et al., 2016; Song et al., 2015
Animal Age:
Six- to eight-week-old mice
Document type:
Supplementary scientific material
Section:
Supplement 9
Scientific field:
Microbiome research
Research area:
Gynecologic pathology
Methodology:
Random forest classifier models
Analytical approach:
Microbiome signature identification
Validation method:
Receiver operating characteristic curves
Cohorts analyzed:
Cohort A; Cohort C
Histological comparisons:
Serous histology vs benign; Endometrioid histology vs benign
Subject:
Biomarker discovery based on histological classification
Year:
2025
Academic Degree:
Bachelor
Field of Study:
60910200 – Medical treatment
Course Name:
Histology, Cytology, Embryology
Course Code:
GS12-308
Form of Education:
Full-time
Course Type:
Compulsory
Language of Discipline:
English
Module Duration:
15 weeks
Credits:
ECTS 4
Total Academic Hours:
120
Lecture Hours:
12
Practical Hours:
48
Independent Study Hours:
60
Semester:
3
Prerequisite Course:
Human Anatomy (AN11-312)
Assessment Forms:
Oral assessment; practical assignments; final test
Approving Authority:
Vice-Rector for Academic Affairs
Approved By:
I. G. Mamajonov
Institutional Context:
Medical higher education program
Year:
2016-2017
Department:
Anatomy & Histology
Instructor:
Dr. Rajaa Ali
Topic:
Endocrine System Part II
Covered Organs:
Hypothalamus, Pineal Gland, Thyroid Gland, Parathyroid Glands
Document Type:
Lecture Notes
Audience:
Medical Students
Anatomical Focus:
Human Endocrine System
Educational Level:
University
Figures Included:
Photomicrographs of glands and cells
Content Scope:
Structure, function, and cellular composition of endocrine glands