:root {
  /* No design token overrides */
}

:root {
  --code-font-size-inline: var(--font-size-sm);
  --code-font-size-block: var(--font-size-sm);
  --code-border-radius: var(--radius-sm);
  --code-padding-inline: 0.05em 0.25em;
  --code-padding-block: var(--space-4);
  --code-keyword: var(--color-blue-600);
  --code-string: var(--color-green-700);
  --code-comment-line: var(--color-gray-500);
}

:root {
  --code-border-width: var(--space-0);
  --code-padding-inline: 0;
  --code-padding-block: var(--space-1);
  --code-gutter-border-width: var(--space-0);
  --code-line-numbers: none;
  --code-fold-gutter: none;
}

:root {
  --code-font-size-inline: var(--font-size-md);
  --code-font-size-block: var(--font-size-md);
  --code-padding-inline: 0.1em 0.35em;
  --code-line-numbers: none;
  --code-keyword: var(--color-red-400);
  --code-string: var(--color-green-400);
  --code-comment-line: var(--color-gray-400);
  --code-function: var(--color-amber-500);
}

# Statistical analysis for experimental data
import numpy as np
from scipy import stats

def analyze_experiment(data, control_group=None):
    """Calculate p-value for experimental results."""
    mean_effect = np.mean(data)  # Calculate mean effect size
    if control_group is not None:
        t_stat, p_value = stats.ttest_ind(data, control_group)
        return {'mean': mean_effect, 'p_value': p_value, 'significant': p_value < 0.05}
    return mean_effect

library(tidyverse)
library(broom)

# Analyze gene expression data
gene_analysis <- function(expression_matrix, metadata) {
  results <- expression_matrix %>%
    filter(gene_type == "protein_coding") %>%
    mutate(log_expression = log2(count + 1)) %>%
    group_by(sample_id) %>%
    summarise(median_expr = median(log_expression), .groups = "drop")

  return(results)
}

// Data tracking class for research experiments
class ExperimentTracker {
  constructor(studyId, sampleSize = 100) {
    this.studyId = studyId;
    this.sampleSize = sampleSize;
    this.results = []; // Array to store measurements
  }

  async recordMeasurement(participant, measurement) {
    const timestamp = new Date().toISOString(); // ISO timestamp
    this.results.push({ participant, measurement, timestamp });
    return this.calculateStatistics();
  }
}

-- Query to analyze biomarker data for recent patient cohort
SELECT
    p.patient_id,
    p.age_at_diagnosis,
    t.treatment_type,
    AVG(m.biomarker_value) AS mean_biomarker,  -- Average biomarker level
    COUNT(*) AS measurement_count             -- Number of measurements
FROM patients p
    INNER JOIN treatments t ON p.patient_id = t.patient_id
    LEFT JOIN measurements m ON p.patient_id = m.patient_id
WHERE p.diagnosis_date >= '2023-01-01'       -- Recent diagnoses only
    AND m.biomarker_name = 'IL-6'           -- Specific biomarker
GROUP BY p.patient_id, t.treatment_type
HAVING COUNT(*) >= 3;                        -- Minimum measurement threshold