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# Cell Press Summary, Highlights, and eTOC Examples
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Examples of Cell Press-specific elements including Summary (abstract), Highlights, and eTOC blurb.
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---
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## Complete Example 1: Senescence and Aging
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### Summary (150 words max)
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```
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Cellular senescence is a stress response that prevents damaged cell
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proliferation but can drive tissue dysfunction through the senescence-
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associated secretory phenotype (SASP). How senescent cells resist
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apoptosis despite expressing pro-apoptotic p53 has remained unclear.
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Here, we identify FOXO4 as a pivotal mediator of senescent cell viability.
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FOXO4 is highly expressed in senescent cells and directly interacts with
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p53, retaining it in the nucleus and preventing p53-mediated apoptosis.
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A cell-permeable peptide that disrupts FOXO4-p53 interaction selectively
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induces p53 nuclear exclusion and apoptosis in senescent cells without
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affecting proliferating cells. In vivo, this FOXO4 peptide neutralizes
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doxorubicin-induced senescent cells and restores fitness, fur density,
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and renal function in naturally aged mice. These findings establish
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FOXO4-mediated p53 sequestration as a senescence-specific survival
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pathway and demonstrate the therapeutic potential of targeted senescent
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cell elimination.
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```
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### Highlights (≤85 characters each)
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```
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• FOXO4 is selectively upregulated in senescent cells and binds p53
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• FOXO4-p53 interaction retains p53 in the nucleus, preventing apoptosis
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• A FOXO4-targeting peptide induces apoptosis specifically in senescent cells
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• FOXO4 peptide treatment restores fitness and organ function in aged mice
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```
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### eTOC Blurb (30-50 words)
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```
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Baar et al. identify FOXO4 as a critical mediator of senescent cell survival
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through p53 sequestration. A peptide disrupting FOXO4-p53 interaction
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selectively eliminates senescent cells and restores tissue function in
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aged mice, establishing proof-of-concept for targeted senolytic therapy.
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```
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### In Brief (1 sentence)
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```
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A FOXO4-targeting peptide selectively eliminates senescent cells by
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releasing p53, restoring tissue function in aged mice.
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```
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---
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## Complete Example 2: Genome Organization
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### Summary (150 words max)
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```
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The three-dimensional organization of chromosomes within the nucleus
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influences gene expression, DNA replication, and genome stability.
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Phase separation has emerged as a potential mechanism for organizing
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nuclear contents, but whether condensates can shape chromosome
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structure in vivo remains unknown. Here, we show that the transcriptional
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coactivator BRD4 forms liquid-like condensates at super-enhancers that
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organize associated chromatin into hub structures. Optogenetic induction
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of BRD4 condensates is sufficient to remodel chromosome topology and
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activate transcription within minutes. Conversely, disruption of BRD4
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condensates with the small molecule JQ1 dissolves chromatin hubs and
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rapidly silences super-enhancer-controlled genes. Single-molecule
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tracking reveals that condensate formation increases the local
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concentration of transcription machinery 100-fold, explaining the
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transcriptional potency of super-enhancers. These results establish
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phase separation as a mechanism for chromatin organization and
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transcriptional control with implications for understanding and
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targeting oncogenic super-enhancers.
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```
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### Highlights
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```
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• BRD4 forms liquid condensates at super-enhancers in living cells
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• BRD4 condensates organize chromatin into transcriptionally active hubs
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• Optogenetic condensate induction rapidly remodels chromatin topology
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• Condensates concentrate transcription machinery 100-fold locally
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```
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### eTOC Blurb
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```
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Sabari et al. demonstrate that BRD4 forms phase-separated condensates
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at super-enhancers that organize chromatin into hub structures and
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concentrate transcription machinery. Optogenetic manipulation reveals
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that condensate formation directly drives chromatin remodeling and
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transcriptional activation.
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```
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---
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## Complete Example 3: Metabolism and Immunity
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### Summary (150 words max)
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```
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Immune cells undergo dramatic metabolic reprogramming upon activation,
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switching from oxidative phosphorylation to aerobic glycolysis. This
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metabolic shift is thought to support the biosynthetic demands of
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rapid proliferation, but whether specific metabolites directly regulate
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immune cell function remains largely unexplored. Here, we show that
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the glycolytic metabolite phosphoenolpyruvate (PEP) sustains T cell
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receptor signaling by inhibiting sarco/endoplasmic reticulum Ca²⁺-ATPase
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(SERCA) activity. PEP accumulates in activated T cells and directly
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binds SERCA, preventing calcium reuptake and prolonging store-operated
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calcium entry. Genetic or pharmacological enhancement of PEP levels
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augments T cell effector function and anti-tumor immunity in vivo.
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Conversely, tumor-derived lactate suppresses PEP levels and impairs
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T cell calcium signaling, contributing to tumor immune evasion. These
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findings reveal an unexpected signaling role for a glycolytic
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intermediate and suggest metabolic strategies to enhance T cell
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responses in cancer immunotherapy.
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```
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### Highlights
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```
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• Phosphoenolpyruvate (PEP) accumulates during T cell activation
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• PEP directly binds and inhibits SERCA to sustain calcium signaling
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• Enhancing PEP levels augments anti-tumor T cell immunity
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• Tumor lactate suppresses T cell PEP levels and calcium signaling
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```
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### eTOC Blurb
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```
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Ho et al. discover that the glycolytic metabolite phosphoenolpyruvate
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directly regulates T cell calcium signaling by inhibiting SERCA. This
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metabolic-signaling link is exploited by tumors through lactate
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secretion and offers new targets for cancer immunotherapy.
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```
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---
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## Graphical Abstract Description Examples
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### For Senescence Paper
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```
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"Graphical abstract for Cell paper on FOXO4 and senescence:
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Left panel: Senescent cell (enlarged, irregular shape) with FOXO4 (blue
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oval) binding p53 (green oval) in nucleus, preventing apoptosis. Label:
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'FOXO4 sequesters p53 → Senescent cell survival'
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Center panel: Same senescent cell with FOXO4 peptide (red wedge)
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disrupting FOXO4-p53 interaction. p53 moves to mitochondria (orange
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organelles). Label: 'FOXO4 peptide disrupts interaction'
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Right panel: Senescent cell undergoing apoptosis (fragmenting). Label:
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'Selective senescent cell death'
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Bottom: Aged mouse (grey, hunched) → Treatment arrow → Rejuvenated mouse
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(brown, active). Label: 'Restored fitness in aged mice'
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Color scheme: Blue for FOXO4, green for p53, red for peptide, grey
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background for cells."
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```
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### For Chromatin Paper
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```
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"Graphical abstract for Cell paper on BRD4 condensates:
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Top row: Diagram showing BRD4 molecules (purple dots) clustering at
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super-enhancer (yellow region on DNA strand), forming condensate
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(purple droplet). Transcription factors (orange, green, blue small
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circles) accumulate inside condensate.
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Middle: Chromatin fibers (grey) being pulled into hub structure around
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condensate. Arrow showing '100× local concentration increase'
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Bottom: Two panels - Left shows 'JQ1' treatment dissolving condensate
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and chromatin hub dispersing. Right shows 'Optogenetic activation'
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creating new condensate with chromatin reorganization. Gene expression
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indicators (up arrow, down arrow) for each condition."
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```
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---
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## Writing Tips for Cell Elements
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### Summary Tips
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1. **First sentence**: Establish the biological context
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2. **Second sentence**: State what was unknown (the gap)
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3. **"Here, we show/identify/demonstrate"**: Clear transition to your work
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4. **Middle sentences**: Key findings with mechanism
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5. **Final sentence**: Significance and implications
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### Highlights Tips
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- **Start with a noun or verb**: "FOXO4 forms..." or "Activation of..."
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- **One finding per bullet**: Don't combine multiple points
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- **Be specific**: Include the protein/gene/pathway name
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- **Check character count**: Strictly ≤85 characters including spaces
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- **Cover different findings**: Don't repeat the same point
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### eTOC Blurb Tips
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- **Start with author names**: "Smith et al. show that..."
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- **One or two sentences only**: Keep it punchy
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- **Include the key mechanism**: Not just the finding
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- **End with significance**: Why readers should care
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---
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## Character Counting for Highlights
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Use this to check your highlights:
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```
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• This highlight is exactly 52 characters long including sp
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↑ Count: 52 characters ✓ (under 85)
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• This highlight is getting close to the maximum allowed character limit
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↑ Count: 73 characters ✓ (under 85)
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• This highlight demonstrates what happens when you try to include way too much info
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↑ Count: 88 characters ✗ (over 85 - need to shorten)
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```
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---
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## See Also
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- `cell_press_style.md` - Comprehensive Cell Press writing guide
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- `nature_abstract_examples.md` - Compare with Nature abstract style
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313
assets/examples/medical_structured_abstract.md
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# Medical Journal Structured Abstract Examples
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Examples of structured abstracts for NEJM, Lancet, JAMA, and BMJ showing the labeled section format expected at medical journals.
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---
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## NEJM Style (250 words max)
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### Example 1: Clinical Trial
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```
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BACKGROUND
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Sodium-glucose cotransporter 2 (SGLT2) inhibitors reduce cardiovascular
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events in patients with type 2 diabetes and established cardiovascular
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disease. Whether these benefits extend to patients with heart failure and
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reduced ejection fraction, regardless of diabetes status, is unknown.
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METHODS
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We randomly assigned 4,744 patients with heart failure and an ejection
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fraction of 40% or less to receive dapagliflozin (10 mg once daily) or
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placebo, in addition to recommended therapy. The primary outcome was a
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composite of worsening heart failure (hospitalization or urgent visit
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requiring intravenous therapy) or cardiovascular death.
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RESULTS
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Over a median of 18.2 months, the primary outcome occurred in 386 of
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2,373 patients (16.3%) in the dapagliflozin group and in 502 of 2,371
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patients (21.2%) in the placebo group (hazard ratio, 0.74; 95% confidence
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interval [CI], 0.65 to 0.85; P<0.001). A first worsening heart failure
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event occurred in 237 patients (10.0%) in the dapagliflozin group and
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in 326 patients (13.7%) in the placebo group (hazard ratio, 0.70; 95%
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CI, 0.59 to 0.83). Death from cardiovascular causes occurred in 227
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patients (9.6%) and 273 patients (11.5%), respectively (hazard ratio,
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0.82; 95% CI, 0.69 to 0.98). Effects were similar in patients with and
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without diabetes. Serious adverse events were similar between groups.
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CONCLUSIONS
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Among patients with heart failure and a reduced ejection fraction,
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dapagliflozin reduced the risk of worsening heart failure or
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cardiovascular death, regardless of the presence of diabetes.
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```
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**Key Features**:
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- Four labeled sections (BACKGROUND, METHODS, RESULTS, CONCLUSIONS)
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- Background: 2 sentences (problem + gap)
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- Methods: Study design, population, intervention, primary outcome
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- Results: Primary outcome with HR and 95% CI, key secondary outcomes
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- Conclusions: Clear, measured statement of findings
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---
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### Example 2: Observational Study
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```
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BACKGROUND
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Long-term use of proton-pump inhibitors (PPIs) has been associated with
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adverse outcomes in observational studies, but causality remains uncertain.
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The relationship between PPI use and chronic kidney disease is unclear.
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METHODS
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We conducted a prospective cohort study using data from 10,482 participants
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in the Atherosclerosis Risk in Communities study who were free of kidney
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disease at baseline. PPI use was ascertained at baseline and follow-up
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visits. The primary outcome was incident chronic kidney disease, defined
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as an estimated glomerular filtration rate less than 60 ml per minute per
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1.73 m² of body-surface area.
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RESULTS
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Over a median follow-up of 13.9 years, incident chronic kidney disease
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occurred in 56.0 per 1000 person-years among PPI users and in 42.0 per
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1000 person-years among non-users (adjusted hazard ratio, 1.50; 95%
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confidence interval [CI], 1.14 to 1.96). The association persisted after
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adjustment for potential confounders, including indication for PPI use
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and baseline kidney function. Sensitivity analyses using propensity-score
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matching yielded similar results. No association was observed for
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histamine H2-receptor antagonist use (hazard ratio, 1.08; 95% CI, 0.87
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to 1.34).
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CONCLUSIONS
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PPI use was associated with an increased risk of incident chronic kidney
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disease in this community-based cohort. These findings warrant cautious
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use of PPIs and further investigation to establish causality.
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```
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**Key Features**:
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- Appropriate hedging for observational study ("associated with")
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- Incidence rates provided (per 1000 person-years)
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- Sensitivity analyses mentioned
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- Negative control (H2-receptor antagonists)
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- Cautious conclusion acknowledging limitation
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---
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## Lancet Style (300 words max)
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### Example 3: Clinical Trial with Summary Box
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```
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BACKGROUND
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Dexamethasone has been shown to reduce mortality in hospitalized patients
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with COVID-19 requiring respiratory support. We aimed to evaluate whether
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higher doses of corticosteroids would provide additional benefit in
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patients with severe COVID-19 pneumonia.
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METHODS
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In this randomized, controlled, open-label trial conducted at 18 hospitals
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in Brazil, we assigned patients with moderate-to-severe COVID-19 (PaO2/FiO2
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≤200 mm Hg) to receive high-dose dexamethasone (20 mg once daily for 5
|
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days, then 10 mg once daily for 5 days) or standard dexamethasone (6 mg
|
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once daily for 10 days). The primary outcome was ventilator-free days
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at 28 days.
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FINDINGS
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Between June 17, 2020, and September 20, 2021, we enrolled 299 patients
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(151 assigned to high-dose dexamethasone and 148 to standard
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dexamethasone). The mean number of ventilator-free days at 28 days was
|
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14·2 (SD 10·8) in the high-dose group and 15·5 (SD 10·4) in the standard
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group (difference, −1·3 days; 95% CI, −3·9 to 1·3; P=0·32). There was
|
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no significant difference in 28-day mortality (high dose 35·8% vs
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standard 31·8%; hazard ratio 1·16; 95% CI, 0·79 to 1·70). Hyperglycemia
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requiring insulin was more frequent with high-dose dexamethasone (66·0%
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vs 53·4%; P=0·027).
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INTERPRETATION
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In patients with moderate-to-severe COVID-19 pneumonia, high-dose
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dexamethasone did not improve ventilator-free days and was associated
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with increased hyperglycemia compared with standard-dose dexamethasone.
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These findings do not support the use of high-dose corticosteroids in
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COVID-19.
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FUNDING
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Ministry of Health of Brazil.
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```
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**Key Features**:
|
||||
- Lancet uses "Findings" instead of "Results"
|
||||
- Lancet uses "Interpretation" instead of "Conclusions"
|
||||
- Includes funding statement in abstract
|
||||
- Decimal point (·) instead of period in numbers (Lancet style)
|
||||
|
||||
---
|
||||
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||||
## JAMA Style (350 words max)
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||||
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### Example 4: Diagnostic Study
|
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```
|
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IMPORTANCE
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||||
Lung cancer screening with low-dose computed tomography (CT) reduces
|
||||
mortality but identifies many indeterminate pulmonary nodules, leading
|
||||
to unnecessary invasive procedures. Improved risk prediction could
|
||||
reduce harms while preserving benefits.
|
||||
|
||||
OBJECTIVE
|
||||
To develop and validate a deep learning model for predicting malignancy
|
||||
risk of lung nodules detected on screening CT.
|
||||
|
||||
DESIGN, SETTING, AND PARTICIPANTS
|
||||
This retrospective cohort study included 14,851 participants with
|
||||
lung nodules from the National Lung Screening Trial (NLST) for model
|
||||
development and 5,402 participants from an independent multi-site
|
||||
validation cohort (2016-2019). Data analysis was performed from
|
||||
January to November 2022.
|
||||
|
||||
EXPOSURES
|
||||
Deep learning model prediction of malignancy risk based on CT imaging.
|
||||
|
||||
MAIN OUTCOMES AND MEASURES
|
||||
The primary outcome was lung cancer diagnosis within 2 years. Model
|
||||
performance was assessed by area under the receiver operating
|
||||
characteristic curve (AUC), sensitivity, specificity, and comparison
|
||||
with radiologist assessments.
|
||||
|
||||
RESULTS
|
||||
In the validation cohort (median age, 65 years; 57% male), 312 nodules
|
||||
(5.8%) were diagnosed as lung cancer within 2 years. The deep learning
|
||||
model achieved an AUC of 0.94 (95% CI, 0.92-0.96), compared with 0.85
|
||||
(95% CI, 0.82-0.88) for the Lung-RADS categorization used by radiologists
|
||||
(P<0.001). At 95% sensitivity, the model achieved 68% specificity compared
|
||||
with 38% for Lung-RADS, corresponding to a 49% reduction in false-positive
|
||||
nodules requiring follow-up. The model's performance was consistent across
|
||||
subgroups defined by nodule size, location, and patient demographics.
|
||||
|
||||
CONCLUSIONS AND RELEVANCE
|
||||
A deep learning model for lung nodule malignancy prediction outperformed
|
||||
current clinical standards and could substantially reduce false-positive
|
||||
findings in lung cancer screening, decreasing unnecessary surveillance
|
||||
and invasive procedures.
|
||||
```
|
||||
|
||||
**Key Features**:
|
||||
- JAMA-specific sections (IMPORTANCE, OBJECTIVE, DESIGN...)
|
||||
- "Importance" section required (2-3 sentences on why this matters)
|
||||
- Detailed design section
|
||||
- "Exposures" clearly stated
|
||||
- "Main Outcomes and Measures" explicit
|
||||
|
||||
---
|
||||
|
||||
## BMJ Style (300 words max)
|
||||
|
||||
### Example 5: Cohort Study
|
||||
|
||||
```
|
||||
OBJECTIVE
|
||||
To examine the association between statin use and risk of Parkinson's
|
||||
disease in a large population-based cohort.
|
||||
|
||||
DESIGN
|
||||
Prospective cohort study.
|
||||
|
||||
SETTING
|
||||
UK Biobank, 2006-2021.
|
||||
|
||||
PARTICIPANTS
|
||||
402,251 adults aged 40-69 years without Parkinson's disease at baseline.
|
||||
|
||||
MAIN OUTCOME MEASURES
|
||||
Incident Parkinson's disease identified through hospital admissions,
|
||||
primary care records, and death certificates. Hazard ratios were
|
||||
estimated using Cox regression, adjusted for age, sex, education,
|
||||
smoking, alcohol, physical activity, body mass index, and comorbidities.
|
||||
|
||||
RESULTS
|
||||
Over a median follow-up of 12.3 years, 2,841 participants developed
|
||||
Parkinson's disease (incidence rate 5.7 per 10,000 person-years).
|
||||
Statin use at baseline was not associated with incident Parkinson's
|
||||
disease (adjusted hazard ratio 0.95, 95% confidence interval 0.87 to
|
||||
1.04). Results were consistent across analyses stratified by statin
|
||||
type (lipophilic vs hydrophilic), dose, and duration of use, and in
|
||||
sensitivity analyses accounting for reverse causation. No protective
|
||||
association was observed in analyses restricted to participants with
|
||||
high cardiovascular risk or in propensity-score matched cohorts.
|
||||
|
||||
CONCLUSIONS
|
||||
In this large prospective cohort, statin use was not associated with
|
||||
reduced risk of Parkinson's disease, contrary to findings from some
|
||||
previous observational studies. The null findings were robust across
|
||||
multiple sensitivity analyses. These results do not support a
|
||||
neuroprotective effect of statins against Parkinson's disease.
|
||||
|
||||
WHAT IS ALREADY KNOWN ON THIS TOPIC
|
||||
Previous observational studies have yielded inconsistent results
|
||||
regarding statin use and Parkinson's disease risk.
|
||||
|
||||
WHAT THIS STUDY ADDS
|
||||
This large prospective study with long follow-up found no evidence
|
||||
that statin use protects against Parkinson's disease.
|
||||
```
|
||||
|
||||
**Key Features**:
|
||||
- BMJ uses abbreviated section headers
|
||||
- Includes "What is already known" and "What this study adds" boxes
|
||||
- Design, Setting, and Participants as separate sections
|
||||
- Clear Main Outcome Measures section
|
||||
|
||||
---
|
||||
|
||||
## Key Differences Between Journals
|
||||
|
||||
| Element | NEJM | Lancet | JAMA | BMJ |
|
||||
|---------|------|--------|------|-----|
|
||||
| **Word limit** | 250 | 300 | 350 | 300 |
|
||||
| **Results label** | RESULTS | FINDINGS | RESULTS | RESULTS |
|
||||
| **Conclusions label** | CONCLUSIONS | INTERPRETATION | CONCLUSIONS AND RELEVANCE | CONCLUSIONS |
|
||||
| **Unique sections** | — | Funding in abstract | IMPORTANCE | What is known/adds |
|
||||
| **Decimal style** | Period (.) | Centered dot (·) | Period (.) | Period (.) |
|
||||
|
||||
---
|
||||
|
||||
## Essential Elements for All Medical Abstracts
|
||||
|
||||
### Background/Context
|
||||
- Disease burden or clinical problem (1 sentence)
|
||||
- Knowledge gap or rationale for study (1 sentence)
|
||||
|
||||
### Methods
|
||||
- Study design (RCT, cohort, case-control)
|
||||
- Setting (number of sites, country/region)
|
||||
- Participants (N, key inclusion criteria)
|
||||
- Intervention or exposure
|
||||
- Primary outcome with definition
|
||||
|
||||
### Results
|
||||
- Number enrolled and analyzed
|
||||
- Primary outcome with effect size and 95% CI
|
||||
- Key secondary outcomes
|
||||
- P-values for primary comparisons
|
||||
- Adverse events (if applicable)
|
||||
|
||||
### Conclusions
|
||||
- Clear statement of main finding
|
||||
- Appropriate hedging based on study design
|
||||
- Clinical implication (optional, 1 sentence)
|
||||
|
||||
---
|
||||
|
||||
## Common Mistakes in Medical Abstracts
|
||||
|
||||
❌ **Missing confidence intervals**: "HR 0.75, P=0.02" → include 95% CI
|
||||
❌ **Relative risk only**: Add absolute risk reduction, NNT
|
||||
❌ **Causal language for observational studies**: "PPIs cause kidney disease"
|
||||
❌ **Overstated conclusions**: Claims exceeding evidence
|
||||
❌ **Missing sample sizes**: Always include N for each group
|
||||
❌ **Vague outcomes**: "Improved outcomes" without specific definition
|
||||
|
||||
---
|
||||
|
||||
## See Also
|
||||
|
||||
- `medical_journal_styles.md` - Comprehensive medical writing guide
|
||||
- `venue_writing_styles.md` - Style comparison across venues
|
||||
|
||||
213
assets/examples/nature_abstract_examples.md
Normal file
213
assets/examples/nature_abstract_examples.md
Normal file
@@ -0,0 +1,213 @@
|
||||
# Nature/Science Abstract Examples
|
||||
|
||||
Examples of well-crafted abstracts for high-impact multidisciplinary journals. These demonstrate the flowing paragraph style with broad accessibility expected at Nature, Science, and related venues.
|
||||
|
||||
---
|
||||
|
||||
## Example 1: Molecular Biology / Cell Biology
|
||||
|
||||
**Topic**: CRISPR gene editing discovery
|
||||
|
||||
```
|
||||
The ability to precisely edit DNA sequences in living cells has transformed
|
||||
biological research and holds promise for treating genetic diseases. However,
|
||||
current genome editing tools can introduce unwanted mutations at off-target
|
||||
sites, limiting their clinical potential. Here we describe prime editing, a
|
||||
versatile and precise genome editing method that directly writes new genetic
|
||||
information into a specified DNA site using a reverse transcriptase fused to a
|
||||
CRISPR nickase. Prime editing can make all 12 types of point mutations, as
|
||||
well as small insertions and deletions, with minimal off-target editing and
|
||||
without requiring double-strand breaks or donor DNA templates. In human cells,
|
||||
we used prime editing to correct the primary genetic causes of sickle cell
|
||||
disease and Tay-Sachs disease, and to install protective mutations that
|
||||
reduce risk of prion disease. Prime editing expands the scope and capabilities
|
||||
of genome editing and may address approximately 89% of known human genetic
|
||||
disease variants.
|
||||
```
|
||||
|
||||
**Why this works**:
|
||||
- Opens with broad significance (genetic disease treatment)
|
||||
- States the problem clearly (off-target mutations)
|
||||
- Describes the approach accessibly ("writes new genetic information")
|
||||
- Includes specific results (all 12 point mutations, specific diseases)
|
||||
- Ends with quantified impact (89% of variants)
|
||||
|
||||
---
|
||||
|
||||
## Example 2: Neuroscience
|
||||
|
||||
**Topic**: Memory consolidation mechanism
|
||||
|
||||
```
|
||||
Sleep is essential for memory consolidation, yet how the sleeping brain
|
||||
transforms labile memories into stable long-term representations remains
|
||||
poorly understood. We used multi-site electrophysiology in freely behaving
|
||||
mice to record the activity of thousands of neurons across hippocampus and
|
||||
cortex during learning and subsequent sleep. We discovered that specific
|
||||
neurons that encode a newly learned memory reactivate in precisely timed
|
||||
sequences during slow-wave sleep, with hippocampal reactivation preceding
|
||||
cortical reactivation by 10-15 milliseconds. Optogenetic disruption of this
|
||||
temporal coordination impaired memory retention by 78%, whereas artificial
|
||||
enhancement of the temporal relationship strengthened memories beyond normal
|
||||
levels. These results reveal that the temporal ordering of hippocampal-cortical
|
||||
replay is not merely correlative but causally necessary for memory
|
||||
consolidation. Our findings suggest new therapeutic approaches for memory
|
||||
disorders based on optimizing the temporal dynamics of sleep.
|
||||
```
|
||||
|
||||
**Why this works**:
|
||||
- Connects to well-known phenomenon (sleep and memory)
|
||||
- States what was unknown
|
||||
- Describes approach (multi-site recordings)
|
||||
- Key finding with specific number (10-15 ms)
|
||||
- Causal evidence (disruption and enhancement experiments)
|
||||
- Broader implications (therapeutic approaches)
|
||||
|
||||
---
|
||||
|
||||
## Example 3: Climate Science
|
||||
|
||||
**Topic**: Carbon cycle feedback
|
||||
|
||||
```
|
||||
Arctic permafrost contains approximately 1,500 billion tonnes of organic
|
||||
carbon—twice the amount currently in the atmosphere. As the Arctic warms,
|
||||
this carbon may be released to the atmosphere, accelerating global warming
|
||||
through a positive feedback loop. However, the magnitude and timing of this
|
||||
feedback remain highly uncertain because microbial decomposition rates in
|
||||
thawing permafrost are poorly constrained. Here we present a 15-year
|
||||
field experiment across 25 sites spanning the Arctic, tracking carbon
|
||||
fluxes in warming permafrost under natural conditions. We find that
|
||||
microbial respiration increases exponentially with temperature until soils
|
||||
reach 3°C, then plateaus due to substrate limitation—a threshold effect
|
||||
not captured by current Earth system models. Our results suggest that
|
||||
permafrost carbon feedback will be 30-50% lower than current projections
|
||||
during this century, providing more time to limit warming, but will
|
||||
accelerate dramatically if deep permafrost begins to thaw.
|
||||
```
|
||||
|
||||
**Why this works**:
|
||||
- Opens with striking number (1,500 billion tonnes)
|
||||
- Clear problem statement (feedback uncertainty)
|
||||
- Specific methodology (15 years, 25 sites)
|
||||
- Novel finding (threshold at 3°C)
|
||||
- Implications both reassuring and cautionary
|
||||
|
||||
---
|
||||
|
||||
## Example 4: Physics / Materials Science
|
||||
|
||||
**Topic**: Room-temperature superconductivity
|
||||
|
||||
```
|
||||
Superconductivity—the flow of electricity without resistance—has been
|
||||
confined to extremely low temperatures since its discovery over a century
|
||||
ago, limiting practical applications. The recent demonstration of
|
||||
superconductivity in hydrogen-rich materials at high pressure has raised
|
||||
hopes for higher transition temperatures, but achieving room-temperature
|
||||
superconductivity at ambient pressure has remained elusive. Here we report
|
||||
superconductivity at 21°C (294 K) in a nitrogen-doped lutetium hydride
|
||||
(Lu-N-H) compound at pressures of approximately 1 GPa—nearly ambient
|
||||
conditions. Electrical resistance drops to zero below the transition
|
||||
temperature with a sharp transition width of 2 K, and we observe the Meissner
|
||||
effect confirming bulk superconductivity. Density functional theory
|
||||
calculations suggest that nitrogen incorporation stabilizes the high-symmetry
|
||||
structure that enables strong electron-phonon coupling. These results
|
||||
establish a pathway toward practical room-temperature superconductors.
|
||||
```
|
||||
|
||||
**Why this works**:
|
||||
- Opens with accessible explanation of significance
|
||||
- Historical context (century-old limitation)
|
||||
- Precise results (21°C, 1 GPa, 2 K transition width)
|
||||
- Multiple lines of evidence (resistance + Meissner effect)
|
||||
- Theoretical explanation briefly included
|
||||
- Forward-looking conclusion
|
||||
|
||||
---
|
||||
|
||||
## Example 5: Evolution / Ecology
|
||||
|
||||
**Topic**: Rapid evolution in response to climate
|
||||
|
||||
```
|
||||
Climate change is driving rapid shifts in the geographic distributions of
|
||||
species, but whether organisms can adapt quickly enough to keep pace with
|
||||
warming remains a critical question for biodiversity conservation. Here we
|
||||
document real-time evolution in wild populations of a widespread forest tree,
|
||||
Scots pine, along a 1,000 km latitudinal gradient in Scandinavia. By combining
|
||||
whole-genome sequencing with phenotypic measurements across 25 common gardens,
|
||||
we detect signatures of selection at 47 loci associated with cold tolerance,
|
||||
phenology, and drought resistance over just 50 years—approximately
|
||||
five tree generations. Alleles conferring warmer-adapted phenotypes have
|
||||
increased in frequency by 4-12% across northern populations, matching
|
||||
predictions from models of climate-driven selection. However, migration of
|
||||
warm-adapted genotypes from the south appears limited by geographic barriers.
|
||||
These results demonstrate that trees can evolve rapidly in response to
|
||||
climate change but suggest that assisted gene flow may be necessary to
|
||||
prevent local maladaptation.
|
||||
```
|
||||
|
||||
**Why this works**:
|
||||
- Opens with pressing question (climate adaptation)
|
||||
- Specific system (Scots pine) and scale (1,000 km)
|
||||
- Methods described briefly (genomics + common gardens)
|
||||
- Quantitative results (47 loci, 4-12% frequency shift, 5 generations)
|
||||
- Mechanism identified (limited migration)
|
||||
- Conservation implications stated
|
||||
|
||||
---
|
||||
|
||||
## Common Elements Across Examples
|
||||
|
||||
### Structure (Implicit)
|
||||
1. **Hook**: Why this matters broadly (1-2 sentences)
|
||||
2. **Gap**: What was unknown or problematic (1 sentence)
|
||||
3. **Approach**: What was done (1 sentence)
|
||||
4. **Findings**: Key results with numbers (2-3 sentences)
|
||||
5. **Significance**: Why this matters going forward (1 sentence)
|
||||
|
||||
### Style Features
|
||||
- **Active voice**: "We discovered," "We find," "We report"
|
||||
- **Specific numbers**: Exact values, not vague quantities
|
||||
- **Accessible language**: Minimal jargon, explained when needed
|
||||
- **Compelling opening**: Broad hook before technical details
|
||||
- **Strong close**: Implications or future directions
|
||||
|
||||
### Word Count
|
||||
- Nature: 150-200 words (examples above: 185-210 words)
|
||||
- Science: ≤125 words (would need tightening)
|
||||
|
||||
---
|
||||
|
||||
## What to Avoid
|
||||
|
||||
❌ **Too technical opening**:
|
||||
> "The CRISPR-Cas9 system with guide RNA targeting PAM sequences..."
|
||||
|
||||
✅ **Better opening**:
|
||||
> "The ability to precisely edit DNA in living cells..."
|
||||
|
||||
---
|
||||
|
||||
❌ **Vague results**:
|
||||
> "Our method significantly outperformed existing approaches..."
|
||||
|
||||
✅ **Better results**:
|
||||
> "Our method reduced off-target editing by 78% compared to standard Cas9..."
|
||||
|
||||
---
|
||||
|
||||
❌ **Weak significance statement**:
|
||||
> "These findings may have implications for the field..."
|
||||
|
||||
✅ **Better significance**:
|
||||
> "These findings suggest new therapeutic approaches for memory disorders..."
|
||||
|
||||
---
|
||||
|
||||
## See Also
|
||||
|
||||
- `nature_science_style.md` - Comprehensive Nature/Science writing guide
|
||||
- `venue_writing_styles.md` - Style comparison across venues
|
||||
|
||||
245
assets/examples/neurips_introduction_example.md
Normal file
245
assets/examples/neurips_introduction_example.md
Normal file
@@ -0,0 +1,245 @@
|
||||
# NeurIPS/ICML Introduction Example
|
||||
|
||||
This example demonstrates the distinctive ML conference introduction structure with numbered contributions and technical precision.
|
||||
|
||||
---
|
||||
|
||||
## Full Introduction Example
|
||||
|
||||
**Paper Topic**: Efficient Long-Context Transformers
|
||||
|
||||
---
|
||||
|
||||
### Paragraph 1: Problem Motivation
|
||||
|
||||
```
|
||||
Large language models (LLMs) have demonstrated remarkable capabilities in
|
||||
natural language understanding, code generation, and reasoning tasks [1, 2, 3].
|
||||
These capabilities scale with both model size and context length—longer
|
||||
contexts enable processing of entire documents, multi-turn conversations,
|
||||
and complex reasoning chains that span many steps [4, 5]. However, the
|
||||
standard Transformer attention mechanism [6] has O(N²) time and memory
|
||||
complexity with respect to sequence length N, creating a fundamental
|
||||
bottleneck for processing long sequences. For a context window of 100K
|
||||
tokens, computing full attention requires 10 billion scalar operations
|
||||
and 40 GB of memory for the attention matrix alone, making training and
|
||||
inference prohibitively expensive on current hardware.
|
||||
```
|
||||
|
||||
**Key features**:
|
||||
- States why this matters (LLM capabilities)
|
||||
- Connects to scaling (longer contexts = better performance)
|
||||
- Specific numbers (O(N²), 100K tokens, 10 billion ops, 40 GB)
|
||||
- Citations to establish credibility
|
||||
|
||||
---
|
||||
|
||||
### Paragraph 2: Limitations of Existing Approaches
|
||||
|
||||
```
|
||||
Prior work has addressed attention efficiency through three main approaches.
|
||||
Sparse attention patterns [7, 8, 9] reduce complexity to O(N√N) or O(N log N)
|
||||
by restricting attention to local windows, fixed stride patterns, or learned
|
||||
sparse masks. Linear attention approximations [10, 11, 12] reformulate
|
||||
attention using kernel feature maps that enable O(N) computation, but
|
||||
sacrifice the ability to model arbitrary pairwise interactions. Low-rank
|
||||
factorizations [13, 14] approximate the attention matrix as a product of
|
||||
smaller matrices, achieving efficiency at the cost of expressivity. While
|
||||
these methods reduce theoretical complexity, they introduce approximation
|
||||
errors that compound in deep networks, often resulting in 2-5% accuracy
|
||||
degradation on long-range modeling benchmarks [15]. Perhaps more importantly,
|
||||
they fundamentally change the attention mechanism, making it difficult to
|
||||
apply advances in standard attention (e.g., rotary positional embeddings,
|
||||
grouped-query attention) to efficient variants.
|
||||
```
|
||||
|
||||
**Key features**:
|
||||
- Organized categorization of prior work
|
||||
- Complexity stated for each approach
|
||||
- Limitations clearly identified
|
||||
- Quantified shortcomings (2-5% degradation)
|
||||
- Deeper issue identified (incompatibility with advances)
|
||||
|
||||
---
|
||||
|
||||
### Paragraph 3: Your Approach (High-Level)
|
||||
|
||||
```
|
||||
We take a different approach: rather than approximating attention, we
|
||||
accelerate exact attention by optimizing memory access patterns. Our key
|
||||
observation is that on modern GPUs, attention is bottlenecked by memory
|
||||
bandwidth, not compute. Reading and writing the N × N attention matrix to
|
||||
and from GPU high-bandwidth memory (HBM) dominates runtime, while the GPU's
|
||||
tensor cores remain underutilized. We propose LongFlash, an IO-aware exact
|
||||
attention algorithm that computes attention block-by-block in fast on-chip
|
||||
SRAM, never materializing the full attention matrix in HBM. By carefully
|
||||
orchestrating the tiling pattern and fusing the softmax computation with
|
||||
matrix multiplications, LongFlash reduces HBM accesses from O(N²) to
|
||||
O(N²d/M) where d is the head dimension and M is the SRAM size, achieving
|
||||
asymptotically optimal IO complexity.
|
||||
```
|
||||
|
||||
**Key features**:
|
||||
- Clear differentiation from prior work ("different approach")
|
||||
- Key insight stated explicitly
|
||||
- Technical mechanism explained
|
||||
- Complexity improvement quantified
|
||||
- Method name introduced
|
||||
|
||||
---
|
||||
|
||||
### Paragraph 4: Contributions (CRITICAL)
|
||||
|
||||
```
|
||||
Our contributions are as follows:
|
||||
|
||||
• We propose LongFlash, an IO-aware exact attention algorithm that achieves
|
||||
2-4× speedup over FlashAttention [16] and up to 9× over standard PyTorch
|
||||
attention on sequences from 1K to 128K tokens (Section 3).
|
||||
|
||||
• We provide theoretical analysis proving that LongFlash achieves optimal
|
||||
IO complexity of O(N²d/M) among all algorithms that compute exact
|
||||
attention, and analyze the regime where our algorithm provides maximum
|
||||
benefit (Section 3.3).
|
||||
|
||||
• We introduce sequence parallelism techniques that enable LongFlash to
|
||||
scale to sequences of 1M+ tokens across multiple GPUs with near-linear
|
||||
weak scaling efficiency (Section 4).
|
||||
|
||||
• We demonstrate that LongFlash enables training with 8× longer contexts
|
||||
on the same hardware: we train a 7B parameter model on 128K token
|
||||
contexts using the same memory that previously limited us to 16K tokens
|
||||
(Section 5).
|
||||
|
||||
• We release optimized CUDA kernels achieving 80% of theoretical peak
|
||||
FLOPS on A100 and H100 GPUs, along with PyTorch and JAX bindings, at
|
||||
[anonymous URL] (Section 6).
|
||||
```
|
||||
|
||||
**Key features**:
|
||||
- Numbered/bulleted format
|
||||
- Each contribution is specific and quantified
|
||||
- Section references for each claim
|
||||
- Both methodological and empirical contributions
|
||||
- Code release mentioned
|
||||
- Self-contained bullets (each makes sense alone)
|
||||
|
||||
---
|
||||
|
||||
## Alternative Opening Paragraphs
|
||||
|
||||
### For a Methods Paper
|
||||
|
||||
```
|
||||
Scalable optimization algorithms are fundamental to modern machine learning.
|
||||
Stochastic gradient descent (SGD) and its variants [1, 2, 3] have enabled
|
||||
training of models with billions of parameters on massive datasets. However,
|
||||
these first-order methods exhibit slow convergence on ill-conditioned
|
||||
problems, often requiring thousands of iterations to converge on tasks
|
||||
where second-order methods would converge in tens of iterations [4, 5].
|
||||
```
|
||||
|
||||
### For an Applications Paper
|
||||
|
||||
```
|
||||
Drug discovery is a costly and time-consuming process, with the average new
|
||||
drug requiring 10-15 years and $2.6 billion to develop [1]. Machine learning
|
||||
offers the potential to accelerate this process by predicting molecular
|
||||
properties, identifying promising candidates, and optimizing lead compounds
|
||||
computationally [2, 3]. Recent successes in protein structure prediction [4]
|
||||
and molecular generation [5] have demonstrated that deep learning can
|
||||
capture complex chemical patterns, raising hopes for ML-driven drug discovery.
|
||||
```
|
||||
|
||||
### For a Theory Paper
|
||||
|
||||
```
|
||||
Understanding why deep neural networks generalize well despite having more
|
||||
parameters than training examples remains one of the central puzzles of
|
||||
modern machine learning [1, 2]. Classical statistical learning theory
|
||||
predicts that such overparameterized models should overfit dramatically,
|
||||
yet in practice, large networks trained with SGD achieve excellent test
|
||||
accuracy [3]. This gap between theory and practice has motivated a rich
|
||||
literature on implicit regularization [4], neural tangent kernels [5],
|
||||
and feature learning [6], but a complete theoretical picture remains elusive.
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Contribution Bullet Templates
|
||||
|
||||
### For a New Method
|
||||
|
||||
```
|
||||
• We propose [Method Name], a novel [type of method] that [key innovation]
|
||||
achieving [performance improvement] over [baseline] on [benchmark].
|
||||
```
|
||||
|
||||
### For Theoretical Analysis
|
||||
|
||||
```
|
||||
• We prove that [statement], providing the first [type of result] for
|
||||
[problem setting]. This resolves an open question from [prior work].
|
||||
```
|
||||
|
||||
### For Empirical Study
|
||||
|
||||
```
|
||||
• We conduct a comprehensive evaluation of [N] methods across [M] datasets,
|
||||
revealing that [key finding] and identifying [failure mode/best practice].
|
||||
```
|
||||
|
||||
### For Code/Data Release
|
||||
|
||||
```
|
||||
• We release [resource name], a [description] containing [scale/scope],
|
||||
available at [URL]. This enables [future work/reproducibility].
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Common Mistakes to Avoid
|
||||
|
||||
### Vague Contributions
|
||||
|
||||
❌ **Bad**:
|
||||
```
|
||||
• We propose a novel method for attention
|
||||
• We show our method is better than baselines
|
||||
• We provide theoretical analysis
|
||||
```
|
||||
|
||||
✅ **Good**:
|
||||
```
|
||||
• We propose LongFlash, achieving 2-4× speedup over FlashAttention
|
||||
• We prove LongFlash achieves optimal O(N²d/M) IO complexity
|
||||
• We enable 8× longer context training on fixed hardware budget
|
||||
```
|
||||
|
||||
### Missing Quantification
|
||||
|
||||
❌ **Bad**: "Our method significantly outperforms prior work"
|
||||
✅ **Good**: "Our method improves accuracy by 3.2% on GLUE and 4.1% on SuperGLUE"
|
||||
|
||||
### Overlapping Bullets
|
||||
|
||||
❌ **Bad**:
|
||||
```
|
||||
• We propose a new attention mechanism
|
||||
• We introduce LongFlash attention
|
||||
• Our novel attention approach...
|
||||
```
|
||||
(These say the same thing three times)
|
||||
|
||||
### Buried Contributions
|
||||
|
||||
❌ **Bad**: Contribution bullets at the end of page 2
|
||||
✅ **Good**: Contribution bullets clearly visible by end of page 1
|
||||
|
||||
---
|
||||
|
||||
## See Also
|
||||
|
||||
- `ml_conference_style.md` - Comprehensive ML conference guide
|
||||
- `venue_writing_styles.md` - Style comparison across venues
|
||||
|
||||
Reference in New Issue
Block a user