What are the most important topics that I should be studying for my exam
What subject or course is your exam for?
- What subject is it (e.g., Anatomy, Biochemistry, History, Statistics, etc.)?
- Is there a syllabus, course outline, or list of topics you've been given?
- What level is the exam (undergrad, med school, board exam like USMLE, etc.)?
My subjects are anatomy biochemistry and physiology
What level are you studying at? · Do you want high-yield topics for all three subjects, or focus on one?
Anatomy - High-Yield Topics
- Thorax: Heart anatomy (chambers, valves, coronary arteries), lungs and pleura, mediastinum divisions
- Abdomen: Peritoneum (retroperitoneal vs. intraperitoneal organs), GI tract blood supply (celiac, SMA, IMA), portal circulation, inguinal canal
- Limbs: Brachial plexus (roots, trunks, divisions, cords, branches - know nerve injuries like Erb's and Klumpke's), lumbosacral plexus, common nerve injury patterns (radial, ulnar, median, femoral, sciatic)
- Head & Neck: Cranial nerves (origin, function, and what happens when each is damaged), carotid arteries, thyroid/parathyroid relations
- Pelvis: Pelvic floor muscles, urogenital triangle, femoral triangle
- Spinal cord tracts (dorsal columns vs. spinothalamic) - know what deficits arise from lesions
- Brain lobes and their functions
- Ventricular system and CSF flow
- Blood-brain barrier
- Cell types in each tissue (epithelium types and where they're found)
- Connective tissue components
- Basic histology of major organs (liver, kidney, lung, skin)
Biochemistry - High-Yield Topics
- Glycolysis & gluconeogenesis: Key enzymes, irreversible steps, regulatory enzymes (hexokinase, PFK-1, pyruvate kinase)
- TCA cycle (Krebs): Inputs, outputs, regulated steps, energy yield
- Oxidative phosphorylation: Electron transport chain complexes, ATP synthase, inhibitors (cyanide, CO, oligomycin)
- Fatty acid metabolism: Beta-oxidation vs. fatty acid synthesis (where each occurs, what cofactors are needed), ketone body formation
- Amino acid metabolism: Essential vs. non-essential, urea cycle disorders, transamination, deamination
- Glycogen metabolism: Glycogen storage diseases (von Gierke, McArdle, Pompe, Cori)
- DNA replication, transcription, translation - enzymes involved at each step
- DNA repair mechanisms
- Mutations (missense, nonsense, frameshift) and their consequences
- Fat-soluble: A, D, E, K - functions and deficiency diseases
- Water-soluble: B1 (thiamine - beriberi, Wernicke's), B2, B3 (niacin - pellagra), B6, B9 (folate - neural tube defects), B12 (pernicious anemia), C (scurvy)
- Michaelis-Menten kinetics (Km, Vmax)
- Competitive vs. non-competitive inhibition
- Allosteric regulation
- LDL, HDL, VLDL, chylomicrons - what they carry, where they go
- Familial hypercholesterolemias
Physiology - High-Yield Topics
- Cardiac action potential (phases, ion channels involved) - especially pacemaker vs. contractile cell differences
- Cardiac output = Heart rate x Stroke volume; Frank-Starling law
- Pressure-volume loops
- Blood pressure regulation (baroreceptors, RAAS)
- ECG interpretation basics
- Lung volumes and capacities (TV, IRV, ERV, RV, FRC, TLC, VC)
- V/Q ratio and V/Q mismatch
- Oxygen-hemoglobin dissociation curve (shifts left/right and why)
- Acid-base disorders (respiratory vs. metabolic acidosis/alkalosis)
- Nephron segments and what each reabsorbs/secretes
- Starling forces (glomerular filtration)
- Renin-angiotensin-aldosterone system
- Acid-base balance - kidneys' role
- Resting membrane potential and action potential (Na+/K+ ATPase, gating)
- Synaptic transmission (neurotransmitters, receptors)
- Neuromuscular junction
- Hormones: gastrin, secretin, CCK, GIP - stimuli and effects
- Phases of digestion (cephalic, gastric, intestinal)
- Insulin vs. glucagon: stimuli for release, effects on metabolism
- Thyroid hormones: synthesis, regulation, effects
- Cortisol and the HPA axis
- Growth hormone and IGF-1
Study Tips for All Three
- Integrate across subjects - e.g., learn the kidney in anatomy (histology of nephron), physiology (what each segment does), and biochemistry (acid-base chemistry) together. This sticks better and reflects how exams test.
- Draw it out - brachial plexus, TCA cycle, and cardiac action potential are all much easier when drawn from scratch repeatedly.
- Use mnemonics for nerve/vitamin lists - there are well-established ones for cranial nerves, brachial plexus, and B vitamins.
- Past questions - if your school provides them, prioritize those. Exam writers often recycle concepts.
These are the question asked in the past years .Can you divide it by each subject and how many times a question is repeated
import os, json import pdfplumber base = "/home/daytona/workspace/attachments/501c3aad-f7e1-497c-afd3-7bb8b7978b8d/" pdfs = [f for f in os.listdir(base) if f.endswith(".pdf")] pdfs.sort() all_text = {} for pdf in pdfs: path = os.path.join(base, pdf) text = "" try: with pdfplumber.open(path) as p: for page in p.pages: t = page.extract_text() if t: text += t + "\n" except Exception as e: text = f"ERROR: {e}" all_text[pdf] = text print(f"=== {pdf} ({len(text)} chars) ===") print(text[:500]) print("---")
python3 -c " import os, pdfplumber base = '/home/daytona/workspace/attachments/501c3aad-f7e1-497c-afd3-7bb8b7978b8d/' pdfs = sorted([f for f in os.listdir(base) if f.endswith('.pdf')]) for pdf in pdfs: path = os.path.join(base, pdf) text = '' try: with pdfplumber.open(path) as p: for page in p.pages: t = page.extract_text() if t: text += t + '\n' except Exception as e: text = 'ERROR: ' + str(e) print('=== ' + pdf + ' (' + str(len(text)) + ' chars) ===') print(text[:800]) print('---') "
python3 -c " import subprocess result = subprocess.run(['which', 'tesseract'], capture_output=True, text=True) print('tesseract:', result.stdout.strip()) result2 = subprocess.run(['which', 'pdftoppm'], capture_output=True, text=True) print('pdftoppm:', result2.stdout.strip()) result3 = subprocess.run(['python3', '-c', 'import pytesseract; print(pytesseract.__version__)'], capture_output=True, text=True) print('pytesseract:', result3.stdout.strip(), result3.stderr.strip()) "
python3 -c " import os, subprocess, pytesseract from PIL import Image import io base = '/home/daytona/workspace/attachments/501c3aad-f7e1-497c-afd3-7bb8b7978b8d/' out_dir = '/home/daytona/workspace/ocr_output/' os.makedirs(out_dir, exist_ok=True) pdfs = sorted([f for f in os.listdir(base) if f.endswith('.pdf')]) for pdf in pdfs: path = os.path.join(base, pdf) out_prefix = os.path.join(out_dir, pdf.replace('.pdf','').replace(' ','_')) # convert PDF pages to images subprocess.run(['pdftoppm', '-r', '200', path, out_prefix], check=True) print('Converted: ' + pdf) " 2>&1
BASE="/home/daytona/workspace/attachments/501c3aad-f7e1-497c-afd3-7bb8b7978b8d/" && python3 -c " import subprocess, os base = '/home/daytona/workspace/attachments/501c3aad-f7e1-497c-afd3-7bb8b7978b8d/' pdfs = sorted([f for f in os.listdir(base) if f.endswith('.pdf')]) for pdf in pdfs: r = subprocess.run(['pdfinfo', os.path.join(base, pdf)], capture_output=True, text=True) lines = [l for l in r.stdout.split('\n') if 'Pages' in l] print(pdf[:50], ':', lines) "
mkdir -p /home/daytona/workspace/ocr_output && python3 << 'PYEOF' import os, subprocess, pytesseract from PIL import Image import glob base = '/home/daytona/workspace/attachments/501c3aad-f7e1-497c-afd3-7bb8b7978b8d/' out_dir = '/home/daytona/workspace/ocr_output/' pdfs = sorted([f for f in os.listdir(base) if f.endswith('.pdf')]) # Process first 3 PDFs as a test for pdf in pdfs[:3]: path = os.path.join(base, pdf) safe = pdf.replace('.pdf','').replace(' ','_') img_prefix = out_dir + safe # Convert at low res for speed subprocess.run(['pdftoppm', '-r', '150', '-jpeg', path, img_prefix], check=True, timeout=60) imgs = sorted(glob.glob(img_prefix + '-*.jpg') + glob.glob(img_prefix + '-*.ppm')) text = '' for img in imgs: t = pytesseract.image_to_string(Image.open(img), config='--psm 6') text += t + '\n' txt_path = out_dir + safe + '.txt' with open(txt_path, 'w') as f: f.write(text) print(f'Done: {pdf} -> {len(text)} chars') print('Batch 1 complete') PYEOF
PDF="/home/daytona/workspace/attachments/501c3aad-f7e1-497c-afd3-7bb8b7978b8d/Anatomy Physiology Biochemistry_0002.pdf" && mkdir -p /home/daytona/workspace/ocr_output && pdftoppm -r 120 -jpeg "$PDF" /home/daytona/workspace/ocr_output/test && ls /home/daytona/workspace/ocr_output/
python3 -c " import pytesseract from PIL import Image import time imgs = ['/home/daytona/workspace/ocr_output/test-' + str(i) + '.jpg' for i in range(1,6)] start = time.time() text = '' for img in imgs: t = pytesseract.image_to_string(Image.open(img), config='--psm 6 --oem 1') text += t + '\n' elapsed = time.time() - start print(f'5 pages OCR took {elapsed:.1f}s') print('SAMPLE:') print(text[:2000]) "
cat /home/daytona/workspace/ocr_output/Anatomy_Physiolofy_Biochemistry_0001.txt | head -100
python3 << 'PYEOF' import pytesseract, glob, os, time from PIL import Image out_dir = '/home/daytona/workspace/ocr_output/' # Map prefixes to output txt files sets = { 'Anatomy_Physiology_Biochemistry-': 'APB_main.txt', 'Anatomy_Physiology_Biochemistry_0001_10-': 'APB_10.txt', 'Anatomy_Physiology_Biochemistry_0001_12-': 'APB_12.txt', 'test-': 'APB_0002.txt', # 5-page test PDF } for prefix, outfile in sets.items(): out_path = out_dir + outfile if os.path.exists(out_path): print(f'Already exists: {outfile}') continue imgs = sorted(glob.glob(out_dir + prefix + '*.jpg') + glob.glob(out_dir + prefix + '*.ppm')) if not imgs: print(f'No images for prefix: {prefix}') continue print(f'Processing {len(imgs)} images for {outfile}...') text = '' for img in imgs: t = pytesseract.image_to_string(Image.open(img), config='--psm 6 --oem 1') text += t + '\n' with open(out_path, 'w') as f: f.write(text) print(f' Saved {outfile} ({len(text)} chars)') print('DONE') PYEOF