U are best pharmacy professor in world more than 50 years of experience in working top institutions like Harvard , Monash , Oxford . Now u need to answer all the learning objectives with tabulate form for section b( SAQ and LAQ)list all important bold keywords in details on there . Give me easy memory trick to remember these for my exam . Make easy understandable those topics to the students to understand it like pro and can answer any questions related to that in exam .
cd /home/daytona/workspace/attachments/35dccf59-80e0-4b61-9493-6506fc46ecfa && python3 -c " from pypdf import PdfReader reader = PdfReader('Parenteral Drug Delivery System-PDF.pdf') print(f'Total pages: {len(reader.pages)}') # Extract first 5 pages to find learning objectives and section B for i in range(min(6, len(reader.pages))): print(f'\\n=== PAGE {i+1} ===') print(reader.pages[i].extract_text()) "
cd /home/daytona/workspace/attachments/35dccf59-80e0-4b61-9493-6506fc46ecfa && python3 -c " from pypdf import PdfReader reader = PdfReader('Parenteral Drug Delivery System-PDF.pdf') # Extract all pages for i in range(6, 20): print(f'\\n=== PAGE {i+1} ===') print(reader.pages[i].extract_text()) "
cd /home/daytona/workspace/attachments/35dccf59-80e0-4b61-9493-6506fc46ecfa && python3 -c " import pdfplumber with pdfplumber.open('Parenteral Drug Delivery System-PDF.pdf') as pdf: for i, page in enumerate(pdf.pages): text = page.extract_text() tables = page.extract_tables() if text and len(text.strip()) > 20: print(f'\\n=== PAGE {i+1} (text) ===') print(text) if tables: print(f'\\n=== PAGE {i+1} (tables) ===') for t in tables: for row in t: print(row) "
cd /home/daytona/workspace/attachments/35dccf59-80e0-4b61-9493-6506fc46ecfa && python3 -c " import pdfplumber from PIL import Image import io # Convert key pages to images to read table content with pdfplumber.open('Parenteral Drug Delivery System-PDF.pdf') as pdf: # Pages 3,4,5,6,8,9,10,11,15,16 appear to have visual content for page_num in [2,3,4,5,7,8,9,10,14,15,16]: # 0-indexed page = pdf.pages[page_num] img = page.to_image(resolution=150) img.save(f'/home/daytona/workspace/attachments/35dccf59-80e0-4b61-9493-6506fc46ecfa/page_{page_num+1}.png') print(f'Saved page {page_num+1}') "
cd /home/daytona/workspace/attachments/35dccf59-80e0-4b61-9493-6506fc46ecfa && python3 -c " import subprocess # Use pdftoppm for faster conversion of specific pages result = subprocess.run(['pdftoppm', '-r', '120', '-png', '-f', '3', '-l', '6', 'Parenteral Drug Delivery System-PDF.pdf', '/home/daytona/workspace/attachments/35dccf59-80e0-4b61-9493-6506fc46ecfa/pg'], capture_output=True, text=True, timeout=30) print('stdout:', result.stdout) print('stderr:', result.stderr) print('returncode:', result.returncode) import os files = [f for f in os.listdir('.') if f.startswith('pg')] print('Files:', files) "
cd /home/daytona/workspace/attachments/35dccf59-80e0-4b61-9493-6506fc46ecfa && python3 -c " # Use pytesseract OCR on the PNG pages to extract text import subprocess for pg in ['pg-03.png', 'pg-04.png', 'pg-05.png', 'pg-06.png']: result = subprocess.run(['tesseract', pg, 'stdout', '--psm', '6'], capture_output=True, text=True, timeout=30) print(f'\\n=== {pg} ===') print(result.stdout[:3000]) "
cd /home/daytona/workspace/attachments/35dccf59-80e0-4b61-9493-6506fc46ecfa && python3 -c " import subprocess # Convert pages 7-11 and 15-17 result = subprocess.run(['pdftoppm', '-r', '120', '-png', '-f', '7', '-l', '11', 'Parenteral Drug Delivery System-PDF.pdf', '/home/daytona/workspace/attachments/35dccf59-80e0-4b61-9493-6506fc46ecfa/pg'], capture_output=True, text=True, timeout=30) print('returncode:', result.returncode) result2 = subprocess.run(['pdftoppm', '-r', '120', '-png', '-f', '15', '-l', '17', 'Parenteral Drug Delivery System-PDF.pdf', '/home/daytona/workspace/attachments/35dccf59-80e0-4b61-9493-6506fc46ecfa/pg'], capture_output=True, text=True, timeout=30) print('returncode2:', result2.returncode) import os files = sorted([f for f in os.listdir('.') if f.startswith('pg')]) print('Files:', files) "
cd /home/daytona/workspace/attachments/35dccf59-80e0-4b61-9493-6506fc46ecfa && python3 -c " import subprocess for pg in ['pg-08.png', 'pg-09.png', 'pg-10.png', 'pg-11.png', 'pg-15.png', 'pg-16.png', 'pg-17.png']: result = subprocess.run(['tesseract', pg, 'stdout', '--psm', '6'], capture_output=True, text=True, timeout=30) print(f'\\n=== {pg} ===') print(result.stdout[:3000]) "
- 5 Learning Objectives fully answered in tabular format with all bold keywords highlighted
- LO1 - Parenteral vs. Enteral definition with a comparison table
- LO2 - Advantages/Disadvantages table with the "BRP-UP vs. I-DPC-R" mnemonic
- LO3 - Full ID vs. SC vs. IM master comparison (angle, volume, gauge, site, absorption, clinical use) PLUS the complete Tuberculin skin test breakdown with the "5-10-15 = Sick-Exposed-Healthy" trick
- LO4 - IV route characteristics + all 5 IV risks with the "SEAPE" mnemonic
- LO5 - Clinical scenario application table covering all real exam-style scenarios (anaphylaxis, TB test, insulin, saline infusion, etc.)
- Mega Summary - all 9 parenteral routes in one table
- Ready-to-write SAQ/LAQ templates you can use directly in the exam