Give me detailed notes for semester preparation every single information is important and also explain the pages which show diagrammatic presentation do not leave any information cover all the detailes page by page
cd /home/daytona/workspace/attachments/735684ec-704c-4bfa-a3f1-1777851f1c33/ && python3 -c " from pypdf import PdfReader reader = PdfReader('AN INTRODUCTION TO IMMUNOBIOLOGY.pdf') print(f'Total pages: {len(reader.pages)}') # Print first 3 pages to understand structure for i in range(min(3, len(reader.pages))): print(f'\n=== PAGE {i+1} ===') print(reader.pages[i].extract_text()) "
cd /home/daytona/workspace/attachments/735684ec-704c-4bfa-a3f1-1777851f1c33/ && python3 -c " from pypdf import PdfReader reader = PdfReader('AN INTRODUCTION TO IMMUNOBIOLOGY.pdf') # Extract ALL pages for i in range(len(reader.pages)): text = reader.pages[i].extract_text() print(f'\n====== PAGE {i+1} ======') if text: print(text) else: print('[Image/diagram page - no extractable text]') " > /home/daytona/workspace/immuno_full_text.txt 2>&1 echo "Done. File size: $(wc -c < /home/daytona/workspace/immuno_full_text.txt) bytes, $(wc -l < /home/daytona/workspace/immuno_full_text.txt) lines"
cd /home/daytona/workspace/attachments/735684ec-704c-4bfa-a3f1-1777851f1c33/ && python3 -c " import pdfplumber with pdfplumber.open('AN INTRODUCTION TO IMMUNOBIOLOGY.pdf') as pdf: for i, page in enumerate(pdf.pages): text = page.extract_text() print(f'--- PAGE {i+1} ---') if text: print(text) else: print('[no text]') "
which tesseract && tesseract --version 2>&1 | head -3 python3 -c "import pytesseract; print('pytesseract ok')" 2>&1 python3 -c "from pdf2image import convert_from_path; print('pdf2image ok')" 2>&1
cd /home/daytona/workspace/attachments/735684ec-704c-4bfa-a3f1-1777851f1c33/ && python3 -c " from pdf2image import convert_from_path import pytesseract import os pdf_path = 'AN INTRODUCTION TO IMMUNOBIOLOGY.pdf' images = convert_from_path(pdf_path, dpi=200) print(f'Converted {len(images)} pages') output = [] for i, img in enumerate(images): text = pytesseract.image_to_string(img, config='--psm 6') output.append(f'\n====== PAGE {i+1} ======\n{text}') print(f'Page {i+1} done, chars: {len(text)}') with open('/home/daytona/workspace/immuno_ocr.txt', 'w') as f: f.write('\n'.join(output)) print('OCR complete') " 2>&1
cd /home/daytona/workspace/attachments/735684ec-704c-4bfa-a3f1-1777851f1c33/ && python3 -c " from pdf2image import convert_from_path import pytesseract pdf_path = 'AN INTRODUCTION TO IMMUNOBIOLOGY.pdf' # Process in small batches to avoid OOM import sys output_file = '/home/daytona/workspace/immuno_ocr.txt' for batch_start in range(0, 56, 10): batch_end = min(batch_start + 10, 56) images = convert_from_path(pdf_path, dpi=150, first_page=batch_start+1, last_page=batch_end) with open(output_file, 'a') as f: for j, img in enumerate(images): page_num = batch_start + j + 1 text = pytesseract.image_to_string(img, config='--psm 6') f.write(f'\n====== PAGE {page_num} ======\n{text}\n') print(f'Batch {batch_start+1}-{batch_end} done') del images print('All done') " 2>&1
wc -l /home/daytona/workspace/immuno_ocr.txt && wc -c /home/daytona/workspace/immuno_ocr.txt
- The three pillars of immunity (recognition, elimination, homeostasis)
- Pathogen size classification and why it matters
- Lymphoid organ anatomy (primary vs. secondary organs, Peyer's patches, M cells)
- Innate vs. adaptive comparison table with key historical figures
- PAMPs, PRRs, and the signaling consequence
- Antibody structure vs. TCR structure
- Antigen presentation via MHC (3-step diagram explained)
- Clonal selection theory (James Gowans)
- Three mechanisms of antibody action (neutralization, opsonization, complement)
- CD4+ vs. CD8+ T cells
- MHC Class I vs. Class II pathways
- Four compartments and their defenders
- Physical barriers (skin, respiratory, GI tract)
- Cystic fibrosis as a clinical correlate of mucociliary clearance failure
- Lysozyme mechanism (beta-1,4 glycosidic bond hydrolysis)
- Defensins and electrostatic membrane targeting
- Complete complement system: all three pathways (classical, lectin, alternative)
- C3 convertase amplification
- Opsonization via C3b and complement receptors
- MAC (C5b-6789) lysis
- Complement regulation proteins (Factor H, DAF/CD55, CD59) and PNH
- Macrophages vs. neutrophils comparison
- Pattern recognition receptor map (TLRs, NLRs, RLRs, phagocytic receptors)
- Phagocytosis and destruction cycle (phagolysosome)
- TLR signaling cascades (MyD88, TRIF, NF-κB, IRF3 → IFN-α/β)
- Inflammasome (NLRP3, two-signal model, IL-1β, pyroptosis)
- Key cytokines table
- Acute phase response (fever, CRP, MBL, hepcidin)
- Dendritic cells and the two-signal T cell activation model
- NK cells (missing self hypothesis, perforin/granzyme, ADCC)
- The kinetic gap diagram and NK cell timeline
- The innate-to-adaptive hand-off via dendritic cells