FBL Human

What is FBL and how does it relate to human biological research?

Frontiers in Bioscience-Landmark (FBL) is an international peer-reviewed open access journal dedicated to publishing advances in all aspects of cellular and molecular biology of eukaryotic and prokaryotic cells. The journal serves as a critical platform for disseminating cutting-edge human biological research, encompassing studies related to biochemistry, biophysics, physiology, pathology, biotechnology, and bioinformatics .

For researchers studying human biological systems, FBL provides high visibility through indexing in major scientific databases including SCIE (Web of Science), MEDLINE (PubMed), Scopus, and DOAJ . This visibility ensures that published human studies reach a wide academic audience, making FBL a strategic venue for maximizing research impact. The journal's membership in the Committee on Publication Ethics (COPE) also ensures that published human studies adhere to rigorous ethical standards, which is particularly important for research involving human subjects or samples.

What types of human cellular and molecular studies are typically published in FBL?

FBL publishes a diverse range of human cellular and molecular studies, with recent publications highlighting the breadth of research areas applicable to human biology. Based on current indexing, these include:

• Epigenetic and genome stability studies - such as research on linker histones and their role in maintaining genome stability and cellular aging processes

• Immunological research - including studies examining psychic stress effects on contact sensitivity, cell proliferation, and cytokine production

• Signaling pathway investigations - exemplified by research on the Nrf2 Signaling Pathway in relation to Mycoplasma infections

• Neurodegenerative disease research - particularly studies identifying novel plasma biomarkers for conditions like Alzheimer's Disease using advanced techniques such as organotypic brain slice and microcontact printing

The journal's focus emphasizes molecular mechanisms underlying human health and disease, with particular attention to translational aspects that bridge fundamental research with clinical applications. This makes FBL particularly valuable for researchers investigating human cellular and molecular processes with potential medical implications.

How should researchers design robust experiments for human cell studies?

Designing robust experiments for human cell studies requires careful consideration of methodology and experimental design principles. Two primary approaches should be considered:

• Laboratory-Controlled Experiments:

  These experiments offer precise control of variables and are ideal for mechanistic studies of human cells. Key methodological considerations include:

  • Implementing strict randomization protocols for sample allocation

  • Establishing appropriate controls (positive, negative, vehicle)

  • Standardizing protocols to enable replication by other researchers

  • Determining appropriate sample sizes through power analysis

  Strengths: Laboratory experiments provide precise control of external and internal factors, enable random assignment of samples, and allow identification of cause-effect relationships with high accuracy .

  Limitations: These approaches may lack ecological validity as lab environments don't reflect natural conditions, and observer effects might bias results (Hawthorne Effect) .

• Field Experiments:

  Field experiments are conducted in natural settings with manipulation of the "cause" aspect but limited control over other variables . These are particularly valuable for validating laboratory findings in more realistic contexts.

  Applications: Often used for validating laboratory protocols or collecting broader feedback on methodological approaches that were initially tested under controlled conditions .

Selection between these approaches should be guided by research questions, available resources, and the balance needed between control and ecological validity. For most human cell studies, a hybrid approach starting with controlled experiments followed by validation in more naturalistic settings often yields the most comprehensive results.

What techniques are available for analyzing protein localization in human cells?

Analysis of protein localization in human cells, such as fibrillarin, requires specialized techniques that combine molecular biology with advanced microscopy. Based on established methodologies, researchers should consider the following approach:

• Fusion Protein Constructs:

  • Generate constructs of full-length protein and truncated mutants fused to fluorescent reporters like GFP

  • Use standard cloning techniques with appropriate restriction sites (e.g., XbaI) to fuse the protein in frame to the GFP coding sequence

  • Create deletion mutants to identify specific targeting domains within the protein

• Cell Culture and Transfection:

  • Maintain human cell lines (e.g., U-2 OS or HeLa cells) in appropriate media

  • Perform transient transfections at ~60% confluence using lipid-based reagents such as DOTAP

  • For stable expression, grow cells in selective media (e.g., with 400 μg/ml G418)

• Imaging Methodology:

  • For fixed cells: Standard immunofluorescence with appropriate antibodies

  • For live-cell imaging: Culture cells on glass-bottom dishes in appropriate media without phenol red

  • Use temperature control (37°C) during imaging to maintain physiological conditions

• Validation Approaches:

  • Compare localization patterns between tagged proteins and endogenous proteins

  • Verify that expression of fusion proteins does not alter cellular structures

  • Use co-localization with established markers (e.g., p80 coilin for Cajal bodies)

For dynamic studies of protein mobility, time-lapse confocal microscopy remains the gold standard, allowing researchers to track processes such as the fusion and splitting of nuclear bodies over time, though these events typically occur at low frequencies .

How can deep learning be applied to human biological data analysis?

Deep learning approaches offer powerful tools for analyzing complex human biological data. Implementing these approaches for human studies requires a systematic methodology:

• Data Preparation and Preprocessing:

  • Clean and standardize input data (e.g., image scaling for visual data)

  • Address class imbalance issues using techniques like SMOTE

  • Split data appropriately into training, validation, and test sets

• Model Architecture Selection:
  When analyzing human full-body images or similar biometric data, three primary architectures have demonstrated effectiveness:

  Architecture	Characteristics	Performance for Human Data
  Convolutional Neural Network (CNN)	Basic architecture with convolutional layers	Serves as baseline model
  ResNet-50	50-layer network with residual connections	Highest accuracy: 79.18% for age, 95.43% for gender, 85.60% for height, 81.91% for weight
  VGG-16	16-layer network with uniform architecture	Effective when transfer learning from pre-trained models

  The ResNet-50 architecture has demonstrated superior performance in various human biometric estimation tasks .

• Training Implementation:

  • Select appropriate loss functions based on the parameter being estimated

  • Implement transfer learning by initializing with weights pre-trained on large datasets

  • Apply regularization techniques to prevent overfitting, especially with limited data

• Validation Strategy:

  • Implement cross-validation to ensure robustness

  • Compare performance across different architectures

  • Analyze model performance across demographic subgroups to identify potential biases

This methodological framework provides a structured approach for applying deep learning to human biological data analysis, allowing researchers to select and optimize techniques appropriate for their specific research questions.

How can researchers detect and address contradictions in human research data?

Addressing contradictions in human research data requires sophisticated approaches combining traditional analysis with newer computational techniques:

• Contradiction Detection Frameworks:
  Modern approaches leverage large language models (LLMs) and linguistic rules to identify contradicting patterns in research data . This involves:

  • Creating prototype contradiction datasets for training detection systems

  • Developing contradiction typologies specific to human research domains

  • Implementing linguistic rules to detect simple contradictions arising from negation, antonymy, and numeric mismatches

• Structured Analysis of Contradiction Types:

  Contradiction Type	Characteristics	Detection Methodology
  Logical Contradiction	Direct opposition in claims	Natural language processing and logical inference
  Statistical Contradiction	Significant differences in values	Meta-analysis and heterogeneity assessment
  Methodological Contradiction	Different methods yielding opposite results	Research design comparison frameworks
  Temporal Contradiction	Findings that diverge over time	Longitudinal analysis of publication patterns

• Resolution Approaches:

  • Meta-analytical techniques: Systematically combine data from multiple studies to identify sources of contradiction and estimate true effect sizes

  • Bayesian methods: Incorporate prior knowledge and update confidence based on new evidence

  • Multilab replication: Implement standardized protocols across multiple labs to verify findings

  • Preregistration: Reduce publication bias by documenting methods and analyses before conducting studies

• Computational Implementation:
  Recent work demonstrates the potential of generating prototype contradiction datasets using large language models with specific instructions to create contradicting statements . These approaches show promise in terms of coherence and variety but require further refinement through manual validation before deployment in machine learning systems .

By systematically applying these techniques, researchers can better identify, categorize, and resolve contradictions in human research data, ultimately improving the reliability and reproducibility of scientific findings.

What methodologies are most effective for studying dynamic protein behavior in human cells?

For studying dynamic protein behavior in human cells, such as fibrillarin mobility between nucleoli and Cajal bodies (CBs), a comprehensive methodological approach combining molecular techniques with advanced imaging is recommended:

• Molecular Construct Design:

  • Generate fusion proteins combining the target protein with fluorescent tags (e.g., GFP)

  • Create domain-specific mutants to identify functional regions responsible for localization

  • Design constructs with consideration of protein orientation and linker regions to maintain native function

• Live Cell Imaging Setup:

  • Culture cells on glass-bottom dishes in media optimized for fluorescence imaging (HEPES-buffered, phenol red-free)

  • Maintain physiological conditions using temperature-controlled stages (37°C) and appropriate CO2 levels

  • Employ high-sensitivity cameras (e.g., cooled CCDs) for detection of low signal intensities

• Dynamic Analysis Techniques:

  Technique	Application	Information Obtained
  Time-lapse confocal microscopy	Tracking protein movement over time	Movement patterns, fusion/splitting events
  FRAP (Fluorescence Recovery After Photobleaching)	Measuring protein mobility	Diffusion rates, bound vs. mobile fractions
  Single particle tracking	Following individual molecules	Detailed movement patterns, interaction kinetics

• Specific Analyses for Nuclear Bodies:

  • Monitor fusion and splitting events of structures like Cajal bodies, which have been documented to occur at low frequencies

  • Track localization patterns in both nucleoli and Cajal bodies over time

  • Compare distribution with other known markers (e.g., p80 coilin for CBs)

• Validation Controls:

  • Verify that expression of fusion proteins doesn't alter structure or function of target organelles

  • Compare protein dynamics in the presence of inhibitors (e.g., cycloheximide to block protein synthesis)

  • Correlate with other cellular processes (e.g., cell cycle progression)

Implementation example: Studies of fibrillarin in human cells have successfully used this approach to demonstrate that specific structural domains (particularly the second spacer domain and carboxy terminal alpha-helix domain) target fibrillarin to nucleolar transcription centers and Cajal bodies, respectively .

What are the optimal approaches for developing large-scale human biometric datasets?

Developing large-scale human biometric datasets requires careful consideration of collection, processing, and validation methodologies. Based on successful implementations like the Celeb-FBI dataset, the following approach is recommended:

• Dataset Design and Collection:

  • Define clear objectives for the dataset (e.g., biometric estimation tasks)

  • Determine required attributes (age, gender, height, weight, etc.)

  • Ensure demographic diversity to avoid bias in downstream applications

  • Implement standardized collection protocols for consistency

• Data Processing Pipeline:

  • Image cleaning to remove artifacts and standardize quality

  • Scaling procedures to normalize dimensions

  • Application of techniques like Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance issues

  • Implementation of anonymization procedures for ethical compliance

• Quality Control Measures:

  • Manual verification of subset of data points

  • Cross-validation of attributes from multiple sources when possible

  • Outlier detection and handling

  • Assessment of attribute accuracy and precision

• Benchmark Testing Framework:
  The Celeb-FBI approach demonstrates an effective methodology, creating a dataset of 7,211 full-body images with detailed attribute information, then evaluating performance using multiple deep learning approaches:

  Model Architecture	Age Accuracy	Gender Accuracy	Height Accuracy	Weight Accuracy
  Basic CNN	Lower	Moderate	Moderate	Moderate
  ResNet-50	79.18%	95.43%	85.60%	81.91%
  VGG-16	Moderate	High	Moderate	Moderate

  ResNet-50 consistently delivered superior performance across all biometric parameters .

• Documentation and Distribution:

  • Comprehensive documentation of collection methodology

  • Clear description of preprocessing steps

  • Ethical statements regarding data usage

  • Accessibility considerations for research community

By following this methodological framework, researchers can develop robust human biometric datasets that enable advanced analysis while maintaining ethical standards and minimizing potential biases.

How can time-lapse microscopy be optimized for long-term studies of human cell dynamics?

Optimizing time-lapse microscopy for long-term human cell studies requires careful attention to both technical parameters and biological considerations:

• Sample Preparation for Extended Imaging:

  • Select appropriate culture vessels (glass-bottom dishes or chambers)

  • Formulate media specifically for long-term imaging:

    • HEPES-buffered RPMI 1640 without phenol red

    • Supplemented with 2.0 mg/ml glucose, 5% fetal calf serum, 0.03% glutamine

    • Include antibiotics (1,000 U/ml penicillin/streptomycin) to prevent contamination

  • Determine optimal cell density that allows for growth while preventing overcrowding

• Environmental Control Systems:

  • Implement temperature regulation using stage heaters maintained at 37°C

  • Use objective heaters to prevent thermal gradients across the sample

  • Control humidity to prevent media evaporation during extended imaging

  • Consider CO₂ regulation for pH stability if not using HEPES-buffered media

• Acquisition Parameter Optimization:

  Parameter	Optimization Strategy	Biological Consideration
  Temporal resolution	Balance information needs with phototoxicity	Match to expected rate of cellular processes
  Exposure settings	Use lowest power yielding acceptable signal	Minimize photobleaching and phototoxicity
  Z-sampling	For 3D structures, optimize step size	Balance completeness with acquisition speed
  Field of view selection	Choose representative regions	Consider cell migration patterns
  Autofocus mechanism	Implement reliable focus maintenance	Prevent data loss due to drift

• Hardware Selection:

  • High-sensitivity cameras (e.g., CCD) for detection of low fluorescence signals

  • Stable illumination sources (preferably LED) to reduce phototoxicity

  • Motorized shutters to minimize light exposure between acquisitions

  • Vibration isolation to prevent mechanical drift

• Experimental Controls and Validation:

  • Include non-imaged control samples to assess phototoxicity effects

  • Verify that fluorescent protein expression doesn't alter normal cellular functions

  • Compare with fixed-cell imaging to confirm that live-cell dynamics reflect natural processes

  • Consider using multiple fluorophores to track different cellular components simultaneously

• Analysis Workflows:

  • Implement drift correction algorithms for extended time series

  • Develop tracking protocols for cellular structures (e.g., Cajal bodies)

  • Quantify dynamic events such as fusion and splitting of nuclear bodies, which occur at low frequencies but provide important biological insights

By systematically implementing these optimizations, researchers can achieve high-quality time-lapse imaging of human cells over extended periods while minimizing artifacts and maintaining cell viability throughout the experiment.

What are the latest advancements in transfection methodologies for difficult-to-transfect human cell lines?

Transfection of difficult-to-transfect human cell lines has seen significant methodological advancements in recent years. Researchers should consider these optimized approaches:

• Lipid-Based Transfection Enhancements:

  • Next-generation lipid formulations with reduced toxicity

  • Optimization of DNA:lipid ratios for cell type-specific protocols

  • Serum-resistant formulations that maintain efficiency in complete media

  • Example implementation: DOTAP transfection at 60% cell confluence followed by analysis 4-48 hours post-transfection

• Physical Methods for Recalcitrant Cell Lines:

  Method	Principle	Best Application Scenario
  Nucleofection	Combination of electroporation with cell-specific solutions	Primary cells, stem cells, neurons
  Microinjection	Direct injection into individual cells	Single-cell studies, precise dosage control
  Acoustic transfection	Ultrasound-mediated membrane permeabilization	Gentle approach for sensitive cell types
  Biolistic delivery	DNA-coated gold particles accelerated into cells	Tissue explants, organoids

• Viral Vector Approaches:

  • Lentiviral systems for stable integration in non-dividing cells

  • Adeno-associated virus (AAV) for long-term expression with low immunogenicity

  • Sendai virus for RNA delivery without genomic integration

  • Hybrid vectors combining advantages of multiple viral systems

• Optimization Protocol for Challenging Cell Lines:

  • Systematic evaluation of cell cycle synchronization before transfection

  • Testing of multiple transfection reagents with the same construct

  • Optimization of DNA quality through endotoxin-free maxiprep purification

  • Incorporation of nuclear localization signals to enhance nuclear entry

• Selection and Validation of Stable Transfectants:

  • Grow transfected cells in selection media (e.g., 400 μg/ml G418) to isolate stable transfectants

  • Analyze approximately 30 stable clones to identify optimal expression patterns

  • Verify that expression doesn't alter cellular structures or functions

  • Implement fluorescence-activated cell sorting (FACS) for enrichment of expressing cells

• Emerging Technologies:

  • CRISPR-based knock-in strategies for endogenous tagging

  • Nanoparticle-mediated delivery systems with cell-targeting capabilities

  • Cell-penetrating peptides conjugated to nucleic acids

  • mRNA transfection for transient expression without nuclear entry requirements

By systematically implementing these advanced methodologies, researchers can achieve successful transfection in traditionally difficult human cell lines, enabling sophisticated studies of protein localization and dynamics.