IDE Human

What is IDE in the context of human research?

IDE has two primary meanings in research contexts: Insulin-Degrading Enzyme, a metalloprotease involved in the clearance of insulin and amyloid-β peptides, and Industrial Design Engineering, a discipline focused on human-centered design research. Insulin-Degrading Enzyme plays a critical role in degrading various peptide hormones and has implications for conditions like Alzheimer's disease and diabetes. Industrial Design Engineering research at institutions like TU Delft focuses on themes of Health, Mobility, and Sustainability with perspectives on People, Technology, and Organisation .

How does human IDE structure relate to its function?

Human Insulin-Degrading Enzyme consists of amino- and carboxy-terminal domains (IDE-N and IDE-C) that form an enclosed cage just large enough to encapsulate insulin and other peptide substrates. This unique structural arrangement creates a degradation chamber that remains inaccessible to substrates until the IDE domains reposition. The size and charge distribution of the substrate-binding cavity enable IDE to selectively entrap structurally diverse polypeptides. This explains IDE's remarkable capacity to cleave certain hormones selectively without degrading related family members .

What are the primary research themes in IDE human studies?

For Insulin-Degrading Enzyme research, primary themes include investigating its role in:

• Insulin clearance and glucose homeostasis

• Amyloid-β degradation and Alzheimer's disease pathology

• Processing of other bioactive peptides (amylin, glucagon)

• Structural biology of substrate recognition and enzymatic action

For Industrial Design Engineering human research, key themes include:

• Health: Human-centered design for healthcare solutions

• Mobility: Design for sustainable transportation and movement

• Sustainability: Environmentally conscious design approaches

• Designing Design: Meta-research on design methodologies

How should researchers address selective reporting in IDE-related experiments?

• Define inclusion and exclusion criteria for experiments before seeing the results

• Preregister hypotheses and methods when conducting confirmatory research

• Report all conducted experiments, even those with negative or inconclusive results

• Clearly mark and disclose any post-hoc decisions for considering experiments as unreliable

• Never use agreement with predictions or previous findings as criteria for including an experiment

This approach helps prevent the "file drawer effect" where inconvenient results remain unpublished and helps mitigate HARKing (Hypothesizing After Results are Known) .

What experimental validation approaches are recommended for IDE structural studies?

When conducting structural studies of human IDE complexed with substrates, researchers should:

• Implement predefined validation criteria for experimental outcomes

• Establish clear parameters for what constitutes a successful experiment versus a methodological failure

• Document all experimental attempts with both successful and unsuccessful outcomes

• Validate findings across multiple substrates (e.g., insulin B chain, amyloid-β peptide, amylin, glucagon)

• Correlate structural findings with functional assays to confirm biological relevance

These approaches ensure reproducibility and prevent bias in structural biology research on complex enzymes like IDE .

How can researchers effectively map interconnections within IDE research communities?

Based on successful approaches at TU Delft's Faculty of Industrial Design, researchers can map interconnections through:

• Creating comprehensive surveys asking researchers to:

  • List expertise using specific keywords

  • Associate their research with fundamental platforms identified in the field

  • Identify collaborative networks

• Developing visualizations of research interconnectedness showing:

  • Overlapping research interests

  • Cross-disciplinary collaborations

  • Thematic clusters within the broader research community

This mapping approach has proven valuable for both organizing research content and identifying collaborative opportunities within IDE research communities .

What IRB considerations apply to human research involving IDE?

Human research involving IDE (either enzyme studies or industrial design engineering) requires careful consideration of:

• Determination of whether the research requires IRB review (not all IDE research needs IRB approval)

• Proper submission procedures through institutional HRPP (Human Research Protection Program)

• Addressing data security and confidentiality concerns, particularly with sensitive biological samples or personal design research data

• Understanding the distinctions between quality improvement activities and formal research

• Ensuring appropriate consent processes when human participants are involved

Researchers should consult their institution's Investigator Manual or equivalent resource for specific guidance on navigating IRB requirements .

How should researchers approach data and specimen activities in IDE human research?

When working with human data or specimens in IDE research:

• Determine whether secondary use research requires consent:

  • Consider the identifiability of samples/data

  • Assess the original consent parameters

  • Evaluate regulatory requirements for different specimen types

• Establish protocols for sharing identifiable data or specimens with collaborators:

  • Material Transfer Agreements (MTAs) may be required

  • Data sharing agreements should address confidentiality

• For biospecimens sent outside the institution:

  • Follow institutional policies for transfer

  • Ensure proper documentation and tracking

  • Address any intellectual property considerations

These considerations apply to both biological samples containing Insulin-Degrading Enzyme and human data collected during Industrial Design Engineering research .

How can researchers develop robust data and safety monitoring plans for IDE studies?

Effective data and safety monitoring plans for IDE research should:

• Establish clear validation criteria before experiments begin

• Document all experimental attempts, even unsuccessful ones

• Implement consistent data preprocessing pipelines, particularly for high-throughput studies

• Set objective criteria for defining outliers or problematic data points

• Create independent monitoring mechanisms when appropriate

These measures help prevent post-hoc rationalization of experimental failures and reduce researcher bias in data interpretation and reporting .

What approaches are recommended for integrating artificial intelligence tools in IDE human research?

When incorporating AI tools in IDE human research, researchers should consider:

• Potential issues during submission:

  • Data security concerns

  • Limitations of AI data entry

  • Copyright considerations

• For intervention-based research with AI tools:

  • Provide detailed information about AI mechanisms

  • Address potential biases in AI algorithms

  • Document validation procedures for AI outputs

• Participant information requirements:

  • Clear disclosure about AI involvement

  • Explanation of data usage and privacy safeguards

  • Description of limitations in AI-based interventions

These considerations ensure ethical integration of AI tools while maintaining scientific rigor in IDE research .

How does the structure of human IDE contribute to its substrate selectivity?

Human IDE's unique structural features contribute to its remarkable substrate selectivity through:

• Formation of an enclosed catalytic chamber:

  • IDE-N and IDE-C domains create a cage-like structure

  • The chamber size is precisely calibrated to fit insulin and similar-sized peptides

• Substrate access mechanism:

  • Repositioning of IDE domains controls substrate entry

  • Extensive contacts between domains regulate access to the degradation chamber

• Selective substrate entrapment:

  • Size and charge distribution within the binding cavity enable recognition of diverse polypeptides

  • This explains IDE's ability to selectively cleave certain hormones while sparing related family members

This structural arrangement allows IDE to perform its biological functions with specificity despite the diversity of its substrates .

What methodological approaches are recommended for studying IDE-substrate interactions?

To effectively study IDE-substrate interactions, researchers should:

• Employ multiple complementary techniques:

  • X-ray crystallography for static structural analysis

  • Molecular dynamics simulations for understanding conformational changes

  • Biochemical assays for enzyme kinetics and substrate preference

• Analyze multiple substrate types:

  • Include diverse peptides (insulin B chain, amyloid-β, amylin, glucagon)

  • Compare binding and cleavage patterns across substrate families

• Investigate both binding and catalytic events:

  • Study initial substrate recognition

  • Analyze intermediate steps in the degradation process

  • Examine product release mechanisms

This comprehensive approach provides deeper insights into IDE's selective yet broad substrate recognition capacity .