Diabetes mellitus (zkratka DM), česky úplavice cukrová, krátce cukrovka, je souhrnný název pro skupinu závažných autoimunitních chronických onemocnění, která se projevují poruchou metabolismu sacharidů. Rozlišují se dva základní typy: diabetes I. typu a diabetes II. typu, které vznikají důsledkem absolutního nebo relativního nedostatku inzulinu (hormon…
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Diabetes mellitus topic overview
The analysis highlights Historie, Klasifikace a příčiny diabetu and Současná léčba diabetu as prominent areas in the source structure around Diabetes mellitus.
Related topics
118
Source areas
10
Connected nodes
128
Extracted relationships
82
Concept neighborhoods
21
Bridge connections
128
What this topic covers Research coverage
Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.
Klasifikace a příčiny diabetu · 29 topics
Historie · 20 topics
Současná léčba diabetu · 18 topics
Komplikace diabetu · 11 topics
Etymologie · 9 topics
Overview · 9 topics
Manifestační příznaky a projevy diabetu · 8 topics
Budoucí léčba diabetu · 6 topics
Epidemiologie diabetu · 4 topics
Odkazy · 4 topics
Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.
Key facts & relationships
High-confidence facts extracted from structured source data. Use them as anchors for further research.
MKN-10
E10–E14
Postižený systém
metabolismus cukru
Explore all related topics Closing gaps
A structured outline of related entities, concepts and subtopics. Open any item to build a new map centered on it.
Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
How Diabetes mellitus connects Entity context
Entity context groups extracted connections by relationship type. Counts show recurrence in the analyzed source; the examples are research signals, not claims about importance.
The extracted context around Diabetes mellitus shows recurring relationship patterns in the source. For example, Diabetes mellitus → Antigen, Autoimunitní, B-lymfocyty, Diabetes, Diabetická, Hypotyreóza, IDDM, Imunita, Ke, Konkrétně, Onemocnět, Označovaný, PP-buněk, Spouštěcím, T-lymfocyty, Takto, Tvorba, Vzhledem, Zůstává Another extracted example is Diabetes mellitus → Diabetes, DM1, Glykemie, Jako, Jediným, Katabolismem, Ledviny, Od, Po, Protože, Přebytky, Remise, Současně, Tomuto, Tím, Základní. Use these groups to spot repeated connection types before inspecting the individual relationships.
Relationships are represented as Subject → Predicate → Object triples. Confidence describes extraction strength, not the real-world importance of a relationship; lower-confidence rows may be contextual rather than explicit.
TTTA extracted 82 structured relationships around Diabetes mellitus. Examples in this analysis include Diabetes mellitus → MKN-10 → E10–E14 and Diabetes mellitus → Postižený systém → metabolismus cukru. The table shows each extracted connection, where it came from and its confidence.
Subject
Predicate
Object
Confidence
Src
Diabetes mellitus
MKN-10
E10–E14
1.00
infobox
Diabetes mellitus
Postižený systém
metabolismus cukru
1.00
infobox
Diabetes mellitus
related to Diabetes mellitus 1. typu
Označovaný
0.60
section
Diabetes mellitus
related to Diabetes mellitus 1. typu
IDDM
0.60
section
Diabetes mellitus
related to Diabetes mellitus 1. typu
Diabetes
0.60
section
Diabetes mellitus
related to Diabetes mellitus 1. typu
Ke
0.60
section
Diabetes mellitus
related to Diabetes mellitus 1. typu
Autoimunitní
0.60
section
Diabetes mellitus
related to Diabetes mellitus 1. typu
Imunita
0.60
section
Diabetes mellitus
related to Diabetes mellitus 1. typu
B-lymfocyty
0.60
section
Diabetes mellitus
related to Diabetes mellitus 1. typu
Antigen
0.60
section
Diabetes mellitus
related to Diabetes mellitus 1. typu
Takto
0.60
section
Diabetes mellitus
related to Diabetes mellitus 1. typu
T-lymfocyty
0.60
section
No triples extracted yet.
Related concept clusters Concept neighborhoods
Concept neighborhoods group terms that sit near one another in the extracted graph. They are useful for discovering adjacent language and context, but proximity does not by itself imply a factual relationship.
The concept neighborhoods around Diabetes mellitus bring nearby vocabulary together. In this analysis, examples include Mellitus, Typu and Gestační. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
Diabetes mellitus
Mellitus
Typu
Gestační
Jako
Inzulinu
Diabetu
Mohou
Což
Buněk
Dm
Onemocnění
Slinivky
diabetes mellitus
Mellitus
Gestační
Typu
Jako
Inzulinu
Diabetu
Onemocnění
Mohou
Pankreatu
Buněk
Glukózy
Což
diabetes i. typu
Mellitus
Typu
Gestační
Jako
Inzulinu
Diabetu
Mohou
Což
Buněk
Riziko
Dm
Onemocnění
diabetes ii. typu
Mellitus
Typu
Gestační
Jako
Inzulinu
Diabetu
Mohou
Což
Buněk
Riziko
Dm
Onemocnění
diabetes insipidus
Mellitus
Typu
Gestační
Jako
Inzulinu
Diabetu
Mohou
Což
Buněk
Dm
Onemocnění
Slinivky
světový den diabetu
Typu
Pankreatu
Inzulin
Jsou
Jako
Diabetická
Inzulinu
Mohou
Diabetika
Buněk
Aktivita
Buňky
klasifikace a příčiny diabetu
Typu
Pankreatu
Inzulin
Jsou
Jako
Diabetická
Inzulinu
Mohou
Diabetika
Buněk
Aktivita
Buňky
manifestační příznaky a projevy diabetu
Typu
Pankreatu
Inzulin
Jsou
Jako
Diabetická
Inzulinu
Mohou
Diabetika
Buněk
Aktivita
Buňky
Connections between topic areas Semantic bridges
Semantic bridges connect parts of the graph that would otherwise be more separate. A bridge is a structural signal in this analysis, not a claim that the two topic areas are equivalent.
For Diabetes mellitus, one of the stronger structural bridges in this analysis connects Diabetes mellitus with Klasifikace a příčiny diabetu. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
Number of nodes, edges, triples, density and central hubs. Use it to gauge the size and connectivity of the map.
Diabetes mellitus
Nodes129
Edges128
Triples82
Avg. degree1.98
Density0.015504
Components1
Source & methodology
TTTA analyzes the structure around Diabetes mellitus to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Historie, Klasifikace a příčiny diabetu & Současná léčba diabetu, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
