python3 -c " import pdfplumber with pdfplumber.open('/tmp/workspace/attachments/935952ce-00c9-4992-b60f-2043f411a757/TRN990393_7329185_clinical_report_1637171702.pdf') as pdf: print(f'Pages: {len(pdf.pages)}') for i, page in enumerate(pdf.pages): text = page.extract_text() print(f'--- PAGE {i+1} ---') print(text) print() "
python3 -c " from pypdf import PdfReader reader = PdfReader('/tmp/workspace/attachments/935952ce-00c9-4992-b60f-2043f411a757/TRN990393_7329185_clinical_report_1637171702.pdf') print(f'Pages: {len(reader.pages)}') for i, page in enumerate(reader.pages): text = page.extract_text() print(f'--- PAGE {i+1} ---') print(repr(text[:500]) if text else 'EMPTY') # Check for images if '/XObject' in page.get('/Resources', {}): xobjects = page['/Resources']['/XObject'] print(f'XObjects: {list(xobjects.keys())}') print() "
python3 -c " from pypdf import PdfReader import os reader = PdfReader('/tmp/workspace/attachments/935952ce-00c9-4992-b60f-2043f411a757/TRN990393_7329185_clinical_report_1637171702.pdf') os.makedirs('/tmp/workspace/clinical_report_pages', exist_ok=True) for i, page in enumerate(reader.pages): xobjects = page['/Resources']['/XObject'] for key in xobjects: xobj = xobjects[key] if xobj['/Subtype'] == '/Image': data = xobj._data filter_type = xobj.get('/Filter', '') print(f'Page {i+1}: Filter={filter_type}, Size={len(data)} bytes, ColorSpace={xobj.get(\"/ColorSpace\",\"?\")}, BPC={xobj.get(\"/BitsPerComponent\",\"?\")}') # Save as JPEG if DCTDecode, else try PNG if '/DCTDecode' in str(filter_type) or filter_type == '/DCTDecode': fname = f'/tmp/workspace/clinical_report_pages/page_{i+1}.jpg' with open(fname, 'wb') as f: f.write(data) print(f' Saved: {fname}') else: fname = f'/tmp/workspace/clinical_report_pages/page_{i+1}.bin' with open(fname, 'wb') as f: f.write(data) print(f' Saved as binary: {fname}') "
python3 -c " # Try using pytesseract or easyocr for OCR import subprocess result = subprocess.run(['python3', '-c', 'import pytesseract; print(\"tesseract ok\")'], capture_output=True, text=True) print('pytesseract:', result.stdout, result.stderr) result2 = subprocess.run(['python3', '-c', 'import easyocr; print(\"easyocr ok\")'], capture_output=True, text=True) print('easyocr:', result2.stdout, result2.stderr) result3 = subprocess.run(['python3', '-c', 'from PIL import Image; print(\"PIL ok\")'], capture_output=True, text=True) print('PIL:', result3.stdout, result3.stderr) "
pip install --user pytesseract 2>&1 | tail -5; python3 -c "import pytesseract; print('ok')" 2>&1
python3 -c " import pytesseract from PIL import Image for i in range(1, 6): img = Image.open(f'/tmp/workspace/clinical_report_pages/page_{i}.jpg') text = pytesseract.image_to_string(img) print(f'=== PAGE {i} ===') print(text) print() " 2>&1
# Try a base64 approach - encode pages and extract using a vision-capable method # Also try PIL to get basic image info and dimensions python3 -c " from PIL import Image for i in range(1, 6): img = Image.open(f'/tmp/workspace/clinical_report_pages/page_{i}.jpg') print(f'Page {i}: size={img.size}, mode={img.mode}') "
| Field | Detail |
|---|---|
| Patient Name | Yagay Pratap Singh |
| Gender | Male |
| Age | 11 years |
| Referring Clinician | Dr. Rohan Malik, AIIMS, New Delhi |
| Sample Type | Blood |
| Test Performed | Clinical Exome Sequencing |
| Order ID / Sample ID | 338977 / 7329185 |
| Date of Sample Collection | 18th October 2021 |
| Date of Report | 17th November 2021 |
VARIANT OF UNCERTAIN SIGNIFICANCE RELATED TO THE GIVEN PHENOTYPE WAS DETECTED
| Gene (Transcript) | Location | Variant | Zygosity | Disease (OMIM) | Inheritance | Classification |
|---|---|---|---|---|---|---|
| CTRC (+) (ENST00000375949.5) | Exon 7 | c.703G>A (p.Val235Ile) | Heterozygous | Susceptibility to chronic pancreatitis | Autosomal dominant | Variant of Uncertain Significance (VUS) |
| Metric | Value |
|---|---|
| Average sequencing depth | 328x |
| Average on-target depth | 139.41x |
| Target base pairs covered ≥5x | 99.68% |
| Target base pairs covered ≥20x | 99.06% |
| Total data generated | 5.39 Gb |
| Total reads aligned | 99.98% |
| Reads passing alignment | 92.24% |
| Data ≥Q30 | 96.59% |
Search for recent research on CTRC variants and pancreatitis
CTRC variants pancreatitis
CTRC gene chronic pancreatitis pathogenic
CTRC gene variant pancreatitis 2023 2024 research
PMID: 35594281
PMID: 37321941
PMID: 38876922
PMID: 42019862
https://pmc.ncbi.nlm.nih.gov/articles/PMC12929960
| Study | Type | Key Finding for p.Val235Ile / CTRC |
|---|---|---|
| Takáts et al. 2022 [PMID 35594281] | Meta-Analysis | OR 4.5 for CP; confirms clinical significance |
| Berke et al. 2023 [PMID 37321941] | Meta-Analysis | c.180C>T co-haplotype adds ~2-5x additional risk |
| Stefanovics et al. 2024 [PMID 38876922] | Pediatric cohort | Functional testing essential; 3/5 novel variants were benign |
| PMC 2025 Gene Therapy Review | Review | p.Val235Ile = moderate-low risk (36-55% residual activity) |
| Antwi et al. 2026 [PMID 42019862] | Case-control | CTRC not a major PDAC risk gene |
Recent progession on this condition, like research treatment or upcoming treatment options
hereditary pancreatitis treatment pediatric 2023 2024
chronic pancreatitis gene therapy CTRC PRSS1 SPINK1
hereditary pancreatitis new treatment 2024 2025 2026 clinical trial gene therapy trypsin inhibitor
recurrent acute pancreatitis children management endoscopic
pancreatitis trypsin inhibitor camostat mesylate serine protease therapy
https://pmc.ncbi.nlm.nih.gov/articles/PMC12929960
PMID: 38103842
PMID: 37523027
PMID: 40651900
total pancreatectomy islet autotransplantation hereditary pancreatitis child
| Timeline | Approach | Status |
|---|---|---|
| Now | Cholecystectomy (gallstone removal) | Standard of care - can prevent recurrent attacks |
| Now | PERT for exocrine insufficiency | Established |
| Now | ERCP if ductal obstruction found | Growing evidence in pediatric centers |
| Near future | Parental CTRC testing → variant reclassification | Recommended in the report |
| Emerging | AAV-mediated SPINK1/CTRC gene augmentation | Pre-clinical (animal models, 2024) |
| Emerging | Antisense oligonucleotides (PRSS1 pathway) | Early development |
| Emerging | CRISPR gene editing | Proof-of-concept stage |
| Last resort | TP-IAT (Total Pancreatectomy + Islet Autotransplant) | Established surgical option, specialist centers |
Tp Iat
total pancreatectomy islet autotransplantation chronic pancreatitis outcomes
total pancreatectomy islet autotransplantation pediatric children hereditary pancreatitis
TP-IAT total pancreatectomy islet autotransplantation pediatric 2024 2025 outcomes pain diabetes
PMID: 40523532
PMID: 40831352
PMID: 34132023
PMID: 37840289
https://www.ncbi.nlm.nih.gov/books/NBK278966
| Metric | Adults | Children (Better) |
|---|---|---|
| Insulin independence at 1 year | ~30-40% | 64-70% |
| Detectable C-peptide long term | >80-90% | >90% |
| HbA1c <7% | 60-90% | Even higher |
| Insulin independence if >300,000 IEQ transplanted | ~70% | Higher |
| Islet yield | Expected outcome |
|---|---|
| >5,000 IEQ/kg body weight | Highest insulin independence rates |
| >2,000-3,000 IEQ/kg | Meaningful insulin independence |
| <2,000 IEQ/kg | Likely insulin-requiring, but C-peptide preserved |
1. Total pancreatectomy + splenectomy (often) + duodenectomy
2. Pancreas sent immediately to the islet processing laboratory (same hospital)
3. Enzymatic digestion dissolves the pancreatic tissue, freeing islet clusters
4. Islets purified by density gradient centrifugation
5. Quality control: islet count, viability, sterility checks
6. Islet suspension infused into portal vein via interventional radiology
7. Islets lodge in liver sinusoids → engraft → resume insulin secretion within days-weeks
| Step | Status for Yagay |
|---|---|
| Medical management + PERT | Should be optimized |
| Cholecystectomy (gallstone removal) | Likely indicated now |
| Endoscopic therapy (ERCP) if ductal issues | To be assessed |
| Parental genetic testing → reclassify variant | Recommended in report |
| TP-IAT if all above fail | Future option |