cd /tmp/workspace/attachments/60454552-c7b4-48b0-be14-eee2b920c396/ && ls -la && python3 -c " import pdfplumber with pdfplumber.open('STS2026-10322 RESEARCH PROPOSAL.pdf') as p: print(len(p.pages)) for i,pg in enumerate(p.pages): print('=== PAGE',i+1) print(pg.extract_text()) "

PMID: 39843408

PMID: 29067916

PMID: 28006888

PMID: 38911116

PMID: 32877290

PMID: 34329578

PMID: 38326947

"iron deficiency" AND "pediatric obesity"

WHO 2020 guideline serum ferritin cut-off iron deficiency children 5 years and older 15 µg/L 70 µg/L inflammation

hypoferremic state overweight obese children

"ferritin"[MeSH Terms] AND "C-Reactive Protein"[MeSH Terms] AND "inflammation" AND adjustment iron deficiency

iron deficiency India Comprehensive National Nutrition Survey school-age children adolescents

iron deficiency obese children India ferritin

PMID: 29396942

PMID: 34049401

PMID: 31057325

ls -la /tmp/workspace/; python3 -c "import docx; print(docx.__version__ if hasattr(docx,'__version__') else 'ok')"

mkdir -p /tmp/workspace/college-grant-proposal && cat > /tmp/workspace/college-grant-proposal/build.py <<'EOF' from docx import Document from docx.shared import Pt, Inches, RGBColor from docx.enum.text import WD_ALIGN_PARAGRAPH from docx.oxml.ns import qn from docx.oxml import OxmlElement import re doc = Document() sec = doc.sections[0] sec.left_margin = sec.right_margin = Inches(0.9) sec.top_margin = sec.bottom_margin = Inches(0.8) st = doc.styles["Normal"]; st.font.name = "Arial"; st.font.size = Pt(10.5) st.element.rPr.rFonts.set(qn("w:eastAsia"), "Arial") st.paragraph_format.space_after = Pt(4) def wc(s): return len(re.findall(r"\S+", s)) def shade(cell, color): tcPr = cell._tc.get_or_add_tcPr(); shd = OxmlElement("w:shd") shd.set(qn("w:val"), "clear"); shd.set(qn("w:color"), "auto"); shd.set(qn("w:fill"), color); tcPr.append(shd) def para(text, bold_prefix=None, italic=False, size=None, align=None): p = doc.add_paragraph() if bold_prefix: r = p.add_run(bold_prefix); r.bold = True r = p.add_run(text); r.italic = italic if size: for x in p.runs: x.font.size = Pt(size) if align: p.alignment = align return p def bullets(items, style="List Bullet"): for it in items: p = doc.add_paragraph(style=style) if isinstance(it, tuple): r = p.add_run(it[0]); r.bold = True; p.add_run(it[1]) else: p.add_run(it) p.paragraph_format.space_after = Pt(2) def table(rows, widths, header=True): t = doc.add_table(rows=len(rows), cols=len(rows[0])); t.style = "Table Grid" for i, row in enumerate(rows): for j, val in enumerate(row): c = t.cell(i, j); c.width = Inches(widths[j]); c.text = "" r = c.paragraphs[0].add_run(val); r.font.size = Pt(9.5) if header and i == 0: r.bold = True; shade(c, "D9E2F3") doc.add_paragraph().paragraph_format.space_after = Pt(2) return t sections = {} # name -> text collected for word count def H(name): p = doc.add_paragraph(); p.paragraph_format.space_before = Pt(10) r = p.add_run(name); r.bold = True; r.font.size = Pt(12); r.font.color.rgb = RGBColor(0x1F,0x3A,0x6B) return p # ------------------ CONTENT ------------------ TITLE = ("Iron Deficiency Without Anaemia ('Hidden Anaemia') in Overweight and Obese Children Attending a " "Tertiary Care Hospital in Kamothe: An Inflammation-Adjusted Serum Ferritin Study") INTRO = [ ("", "India carries a double nutritional burden. Iron deficiency remains common in children, while overweight and obesity are rising in urban areas.[1,2] The Comprehensive National Nutrition Survey (CNNS) found inflammation-adjusted iron deficiency (ferritin <15 µg/L) in 11-15% of children aged 5-9 years and adolescent boys and in 30.4% of adolescent girls, with higher rates in urban and wealthier households.[3] Kamothe, a fast-growing township in Navi Mumbai, fits this urban pattern."), ("Significance: ", "Children with excess weight are assumed to be well nourished and are rarely screened for micronutrient status. Iron deficiency can exist without anaemia (so-called 'hidden anaemia'), and haemoglobin-only screening misses it. Missed iron deficiency matters in school-age children because of its links to fatigue, attention and learning."), ("Rationale and controversy: ", "Three mechanisms link obesity to iron deficiency: energy-dense diets with low bioavailable iron; higher iron needs of a larger, growing body; and adiposity-related low-grade inflammation, which raises hepcidin, reduces intestinal iron absorption and traps iron in macrophages.[4,5] A 2025 meta-analysis of 42 studies found higher odds of iron deficiency in children with obesity (pooled OR 1.64; 95% CI 1.22-2.21; pooled prevalence 20.1% vs 16.1%), but no significant association with iron deficiency anaemia (OR 0.78; 95% CI 0.43-1.43).[6] An Indian study of 71 children with overweight or obesity reported a hypoferraemic state in 62%.[7] The evidence is therefore unsettled, and estimates vary widely."), ("The measurement problem: ", "Ferritin is an acute-phase protein. Children with obesity have higher CRP and, often, higher ferritin than normal-weight peers,[5,8] so a normal-looking ferritin can hide depleted iron stores. In 152 children with overweight or obesity, low ferritin was found in only 2.6% but low transferrin saturation in 23.8%, which suggests that standard ferritin cut-offs under-detect deficiency in this group.[9] The 2020 WHO guideline therefore advises interpreting ferritin together with an inflammation marker, using <15 µg/L in apparently healthy children and <70 µg/L when inflammation is present.[10,11] A ferritin-only estimate is likely to understate the true burden."), ("Gap in knowledge: ", "To our knowledge, no CRP-adjusted ferritin data are available for children with overweight or obesity in Kamothe or Navi Mumbai. This pilot study will provide the first local estimate, using a low-cost panel of serum ferritin, C-reactive protein (CRP) and complete blood count (CBC) from a single blood draw. A cross-sectional design suits prevalence estimation at one point in time."), ] OBJ_PRIMARY = ("Primary objective: ", "To estimate the prevalence of iron deficiency, defined by inflammation-adjusted serum ferritin (WHO 2020), in children aged 6-15 years with overweight or obesity attending a tertiary care hospital in Kamothe, and the proportion of them who are not anaemic ('hidden anaemia').") OBJ_SEC = [ "To measure the prevalence of inflammation (CRP >5 mg/L) and show how CRP adjustment changes iron deficiency classification compared with an unadjusted ferritin <15 µg/L cut-off.", "To estimate the prevalence of anaemia and iron deficiency anaemia using haemoglobin and red cell indices from the CBC.", "To correlate BMI-for-age z-score with serum ferritin, CRP and haemoglobin.", "To compare ferritin, CRP and haemoglobin between overweight and obese subgroups.", ] BENCH = ("Benchmark (replaces a formal hypothesis): ", "The observed prevalence and its 95% CI will be compared with CNNS inflammation-adjusted estimates (about 11-15% for 5-9 year-olds and adolescent boys; 30.4% for adolescent girls).[3] With a pilot sample this comparison is descriptive and hypothesis-generating.") METH_TEXT = {} design = [("Study design: ", "Cross-sectional, observational, pilot prevalence study."), ("Setting and duration: ", "Outpatient department of Paediatrics, [Name of institution], Kamothe, Raigad district. Duration 3 months (recruitment about 8 weeks)."), ("Study population: ", "Children aged 6-15 years with overweight or obesity. Consecutive eligible children attending the OPD will be approached until the target is reached, to limit selection bias."), ] SS = ("Sample size: ", "n = Z² × p(1-p) / d². Expected prevalence of iron deficiency p = 0.20, based on the pooled prevalence in children with obesity in the 2025 meta-analysis (20.1%);[6] Z = 1.96; absolute precision d = 0.15. This gives 28 children. Allowing 20% for haemolysed or incomplete samples (28 ÷ 0.8 = 35), the target is 36 children. The 62% figure from a 71-child Indian study was not used for planning because its definition of the 'hypoferraemic state' does not appear to be ferritin <15 µg/L (mean ferritin in that sample was 41.9 µg/L).[7] If the true prevalence is higher, the 95% CI half-width with n = 36 will be at most about 16%. The sample size is also set by the grant ceiling (see Budget).") DEF_ROWS = [ ["Term", "Operational definition"], ["Overweight", "BMI-for-age z-score >+1 SD and ≤+2 SD (WHO 2007 reference, 5-19 years)[13]"], ["Obesity", "BMI-for-age z-score >+2 SD (WHO 2007)"], ["Anaemia", "Hb <11.5 g/dL (5-11 y); <12.0 g/dL (12-14 y, and girls ≥15 y); <13.0 g/dL (boys ≥15 y) (WHO)[14]"], ["Inflammation", "CRP >5 mg/L; CRP >10 mg/L flagged to the treating paediatrician as possible acute infection"], ["Iron deficiency (ID)", "Serum ferritin <15 µg/L if CRP ≤5 mg/L; <70 µg/L if CRP >5 mg/L (WHO 2020)[10]"], ["Unadjusted ID (for comparison)", "Serum ferritin <15 µg/L irrespective of CRP (comparable with CNNS)[3]"], ["Hidden anaemia", "ID with Hb at or above the WHO cut-off for age and sex"], ["Iron deficiency anaemia (IDA)", "ID with Hb below the WHO cut-off; MCV, MCH and RDW reported as supporting indices"], ] INCL = ["Age 6-15 years", "BMI-for-age z-score >+1 SD (overweight or obese)", "Written informed consent from parent/guardian; assent from the child as per ICMR guidelines (verbal 7-12 years, written 12-18 years)[15]"] EXCL = ["Known haemoglobinopathy (thalassaemia major/intermedia, sickle cell disease)", "Iron supplements, haematinics or blood transfusion in the previous 3 months", "Chronic inflammatory or systemic disease (JIA, IBD, CKD, malignancy) or secondary/syndromic obesity (e.g., hypothyroidism, Cushing syndrome)", "Fever or acute infection within the previous 2 weeks"] INSTR = ("Data collection: ", "A pre-tested proforma will record age, sex, pubertal stage (Tanner stage/menarche status in girls), vegetarian or non-vegetarian diet, weekly frequency of iron-rich foods, fast food, tea/coffee intake, deworming in the past 6 months, physical activity and screen time, and socioeconomic status (modified Kuppuswamy scale). Weight (to 0.1 kg), height (to 0.1 cm) and waist circumference will be measured with calibrated instruments by one trained observer. BMI-for-age z-scores will be calculated with WHO AnthroPlus.") BLOOD = ("Blood sampling: ", "One venipuncture, 3 mL (1 mL EDTA for CBC; 2 mL plain tube for serum), between 8 and 10 am after an overnight fast of 8-10 hours to limit diurnal variation.") BLOOD_ROWS = [ ["Investigation (method)", "Rationale", "Cost/test (INR)"], ["Serum ferritin (chemiluminescence immunoassay)", "Primary marker of iron stores; falls before haemoglobin", "145"], ["C-reactive protein, quantitative (immunoturbidimetry)", "Identifies inflammation, which raises ferritin and masks deficiency; allows WHO inflammation-specific cut-offs", "230"], ["Complete blood count (automated 5-part analyser)", "Hb for anaemia; MCV, MCH, RDW for microcytic hypochromic picture; Mentzer index (MCV/RBC) to flag possible thalassaemia trait", "140"], ] JUST = ("Justification of panel: ", "Ferritin gives iron stores, CRP makes ferritin interpretable in a population with low-grade inflammation, and the CBC shows the haematological effect. Together they classify children as iron replete, iron deficient without anaemia, or iron deficiency anaemia, as WHO recommends. Serum iron and TIBC were left out deliberately: serum iron varies through the day and falls with inflammation itself, and the pair costs INR 550 per child, more than half of the full per-child cost. Transferrin saturation is planned for the larger study.") LAB = ("Laboratory methods and quality control: ", "All tests will be run at the institutional/NABL-accredited laboratory on the same analyser platforms throughout. Two-level internal quality control will be run each day. Samples will be coded, processed within 2 hours, and serum for ferritin and CRP stored at -20°C if batched. Haemolysed samples will be re-drawn.") CONF = ("Confidentiality: ", "Participants receive unique numeric codes. Names and hospital numbers are excluded from analysis files. Data are held in a password-protected database accessible only to the investigator and guide.") STATS = [ "Descriptive: prevalence of ID (adjusted and unadjusted), hidden anaemia, anaemia and IDA with exact (Clopper-Pearson) 95% CI; median (IQR) for skewed variables (ferritin and CRP will be log-transformed where needed).", "Correlation: Spearman's rank correlation of BMI z-score with ferritin, CRP and haemoglobin.", "Comparison: Mann-Whitney U test (overweight vs obese); Fisher's exact test for proportions; McNemar's test for adjusted vs unadjusted ID classification.", "No multivariable model will be fitted because the expected number of cases is too small; age, sex and pubertal stage will be examined descriptively.", "Software: IBM SPSS Statistics v26 or later. p<0.05 will be taken as significant, with results labelled exploratory.", ] ETH = ["Institutional Ethics Committee approval will be obtained before enrolment, following the ICMR National Ethical Guidelines (2017).[15]", "Written informed consent from the parent/guardian and age-appropriate assent from the child.", "Observational study with no intervention; the 3 mL venipuncture is within safe paediatric limits, performed by trained staff, with an option to withdraw at any time without effect on care.", "All results (including CRP) will be shared with the family. Children with iron deficiency or anaemia, and those with CRP >10 mg/L, will be referred to the treating paediatrician with dietary counselling.", ] BUD_ROWS = [ ["Item", "Rate (INR)", "Children", "Amount (INR)"], ["Serum ferritin", "145", "36", "5,220"], ["C-reactive protein", "230", "36", "8,280"], ["Complete blood count", "140", "36", "5,040"], ["Subtotal (INR 515 per child)", "", "", "18,540"], ["Contingency: repeat draws for 2 haemolysed/clotted samples (2 × 515)", "", "", "1,030"], ["Total", "", "", "19,570 (ceiling 20,000)"], ] LIMIT = ("Limitations (acknowledged): ", "Single-centre OPD sample, so results apply to hospital attendees and not the whole Kamothe community; small pilot size with wide confidence intervals; no transferrin saturation or soluble transferrin receptor; thalassaemia trait can only be flagged by indices (Mentzer index) and not confirmed; WHO ferritin cut-offs are partly expert-based and may be conservative.[12]") IMPL = ("This pilot will give the first CRP-adjusted estimate of iron deficiency, including iron deficiency without anaemia, in children with overweight or obesity attending a hospital in Kamothe. It will show how much ignoring inflammation changes the estimate, and whether haemoglobin-only screening misses affected children. Results will not by themselves establish a screening policy, but they will provide the effect size, feasibility data and variance needed to design a larger, adequately powered ICMR study with transferrin saturation or soluble transferrin receptor. The student will gain experience in paediatric anthropometry, interpretation of iron and inflammation biomarkers, and biostatistics.") REFS = [ "Sharma N, Sanjeevi RR, Balasubramanian K, Chahal A, Sharma A, Sidiq M. A systematic review on prevalence of overweight and obesity among school children and adolescents in Indian population. Indian J Endocrinol Metab. 2024;28(2):105-15. PMID: 38911116.", "Sethi V, Bassi S, Bahl D, Kumar A, Choedon T, Bhatia N. Prevalence of overweight and obesity and associated demographic and health factors in India: findings from the Comprehensive National Nutrition Survey (CNNS). Pediatr Obes. 2024;19(4):e13092. PMID: 38326947.", "Kulkarni B, Peter R, Ghosh S, Pullakhandam R, Thomas T, Reddy GB. Prevalence of iron deficiency and its sociodemographic patterning in Indian children and adolescents: findings from the Comprehensive National Nutrition Survey 2016-18. J Nutr. 2021. doi:10.1093/jn/nxab145. PMID: 34049401.", "Berton PF, Gambero A. Hepcidin and inflammation associated with iron deficiency in childhood obesity - a systematic review. J Pediatr (Rio J). 2024. doi:10.1016/j.jped.2023.06.002. PMID: 37541648.", "Doğan G, Andiran N, Çelik N, Uysal S. Iron parameters, pro-hepcidin and soluble transferrin receptor levels in obese children. Minerva Pediatr. 2020;72(3):219-26. PMID: 28006888.", "Sachdeva M, Malik M, Purohit A, Jain L, Kaur K, Pradhan P. Association of iron deficiency and anemia with obesity among children: a systematic review and meta-analysis. Obes Rev. 2025;26(6):e13892. PMID: 39843408.", "Siyaram D, Bhatia P, Dayal D, Bhalla AK, Marathe R. Hypoferremic state in overweight and obese children. Indian Pediatr. 2018;55(1):72-3. PMID: 29396942.", "Ortiz Pérez M, Vázquez López MA, Ibáñez Alcalde M, Galera Martínez R, Martín González M, Lendínez Molinos F. Relationship between obesity and iron deficiency in healthy adolescents. Child Obes. 2020;16(6):401-8. PMID: 32877290.", "Muzzio ML, Lozano Chiappe ES, Kabakian L, Ferraro F, Landó I, Alonso E. Effects of pubertal status and inflammation on the use of ferritin to define iron deficiency in children with overweight or obesity. Nutr Metab Insights. 2019. doi:10.1177/1178638819839064. PMID: 31057325.", "World Health Organization. WHO guideline on use of ferritin concentrations to assess iron status in individuals and populations. Geneva: WHO; 2020. (Not PubMed-indexed.)", "Namaste SM, Rohner F, Huang J, et al. Adjusting ferritin concentrations for inflammation: Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia (BRINDA) project. Am J Clin Nutr. 2017;106(Suppl 1):359S-71S. PMID: 28615259.", "Mei Z, Addo OY, Jefferds ME, Sharma AJ, Flores-Ayala RC, Brittenham GM. Physiologically based serum ferritin thresholds for iron deficiency in children and non-pregnant women: a US NHANES serial cross-sectional study. Lancet Haematol. 2021;8(8):e572-82. PMID: 34329578.", "de Onis M, Onyango AW, Borghi E, Siyam A, Nishida C, Siekmann J. Development of a WHO growth reference for school-aged children and adolescents. Bull World Health Organ. 2007;85(9):660-7.", "World Health Organization. Haemoglobin concentrations for the diagnosis of anaemia and assessment of severity. Geneva: WHO; 2011. (Not PubMed-indexed.)", "Indian Council of Medical Research. National Ethical Guidelines for Biomedical and Health Research Involving Human Participants. New Delhi: ICMR; 2017. (Not PubMed-indexed.)", ] # word counts w_intro = sum(wc(a+b) for a,b in INTRO) w_obj = wc(OBJ_PRIMARY[0]+OBJ_PRIMARY[1]) + sum(wc(s) for s in OBJ_SEC) + wc(BENCH[0]+BENCH[1]) w_meth = (sum(wc(a+b) for a,b in design+[SS,INSTR,BLOOD,JUST,LAB,CONF,LIMIT]) + sum(wc(" ".join(r)) for r in DEF_ROWS+BLOOD_ROWS) + sum(wc(s) for s in INCL+EXCL+STATS+ETH)) w_impl = wc(IMPL); w_refs = sum(wc(r) for r in REFS); w_title = wc(TITLE) print(w_title, w_intro, w_obj, w_meth, w_impl, w_refs) rnd = lambda n: int(round(n/5.0)*5) # ------------------ BUILD ------------------ p = para("Reference ID: [to be allotted] | College Research Grant Proposal (Budget ceiling INR 20,000)", size=9) H(f"TITLE - Word count: {w_title}") t = para(TITLE); t.runs[0].bold = True; t.runs[0].font.size = Pt(12) H(f"INTRODUCTION - Word count: ~{rnd(w_intro)}") for a,b in INTRO: para(b, bold_prefix=a or None) H(f"OBJECTIVES - Word count: ~{rnd(w_obj)}") para(OBJ_PRIMARY[1], bold_prefix=OBJ_PRIMARY[0]) para("", bold_prefix="Secondary objectives:") bullets(OBJ_SEC, "List Number") para(BENCH[1], bold_prefix=BENCH[0]) H(f"METHODOLOGY - Word count: ~{rnd(w_meth)}") for a,b in design: para(b, bold_prefix=a) para(SS[1], bold_prefix=SS[0]) para("", bold_prefix="Operational definitions:") table(DEF_ROWS, [1.9, 4.9]) para("", bold_prefix="Inclusion criteria:"); bullets(INCL) para("", bold_prefix="Exclusion criteria:"); bullets(EXCL) para(INSTR[1], bold_prefix=INSTR[0]) para(BLOOD[1], bold_prefix=BLOOD[0]) table(BLOOD_ROWS, [2.4, 3.4, 1.0]) para(JUST[1], bold_prefix=JUST[0]) para(LAB[1], bold_prefix=LAB[0]) para(CONF[1], bold_prefix=CONF[0]) para("", bold_prefix="Plan of analysis / statistical tools:"); bullets(STATS) para("", bold_prefix="Ethical considerations:"); bullets(ETH) para("", bold_prefix="Budget (college grant):") table(BUD_ROWS, [3.6, 0.9, 0.8, 1.5]) para(LIMIT[1], bold_prefix=LIMIT[0]) H(f"IMPLICATIONS / STUDY OUTCOME - Word count: ~{rnd(w_impl)}") para(IMPL) H(f"REFERENCES (Vancouver style, PubMed indexed unless stated) - Word count: ~{rnd(w_refs)}") for i, r in enumerate(REFS, 1): q = para(f"{i}. {r}", size=9.5); q.paragraph_format.space_after = Pt(2) out = "/tmp/workspace/college-grant-proposal/College_Grant_Proposal_Iron_Deficiency_OW_OB_Children.docx" doc.save(out); print(out) EOF cd /tmp/workspace/college-grant-proposal && python3 build.py

cd /tmp/workspace/college-grant-proposal && python3 -c " from docx import Document d=Document('College_Grant_Proposal_Iron_Deficiency_OW_OB_Children.docx') t='\n'.join(p.text for p in d.paragraphs) print(len(d.tables), '—' in t, '–' in t) print(t[:900]) "; ls -la

Which part should I rework in detail first?

PMID: 34049401

PMID: 37541648

PMID: 31057325

PMID: 28615259

PMID: 38326947

PMID: 29396942

PMID: 28006888

PMID: 32877290

PMID: 39843408

python3 -c " from scipy.stats import binom n=36 for p0 in (0.15,): k=min(k for k in range(n+1) if binom.sf(k-1,n,p0)<=0.05) print('crit k',k,'alpha',binom.sf(k-1,n,p0)) for p1 in (0.25,0.30,0.35,0.40): print(p1, binom.sf(k-1,n,p1)) "

cat > /tmp/workspace/college-grant-proposal/build2.py <<'EOF' from docx import Document from docx.shared import Pt, Inches, RGBColor from docx.oxml.ns import qn import re doc = Document() s = doc.sections[0] s.left_margin = s.right_margin = Inches(0.9); s.top_margin = s.bottom_margin = Inches(0.8) st = doc.styles["Normal"]; st.font.name = "Arial"; st.font.size = Pt(10.5) st.element.rPr.rFonts.set(qn("w:eastAsia"), "Arial"); st.paragraph_format.space_after = Pt(4) wc = lambda t: len(re.findall(r"\S+", t)) def H(name): p = doc.add_paragraph(); p.paragraph_format.space_before = Pt(10) r = p.add_run(name); r.bold = True; r.font.size = Pt(12); r.font.color.rgb = RGBColor(0x1F,0x3A,0x6B) def P(body, lead=None, size=None): p = doc.add_paragraph() if lead: p.add_run(lead).bold = True r = p.add_run(body) if size: for x in p.runs: x.font.size = Pt(size) return p def B(items, style="List Bullet"): for it in items: p = doc.add_paragraph(style=style); p.paragraph_format.space_after = Pt(2) if isinstance(it, tuple): p.add_run(it[0]).bold = True; p.add_run(it[1]) else: p.add_run(it) TITLE = "Iron Deficiency Without Anaemia in Overweight and Obese Children Aged 6-15 Years: A CRP-Adjusted Serum Ferritin Study from Raigad District" INTRO = [ ("Significance: ", "Iron deficiency (ID) and excess weight now coexist in Indian children. The Comprehensive National Nutrition Survey (CNNS) found inflammation-adjusted ID (ferritin <15 µg/L) in 11-15% of children aged 5-9 years and adolescent boys and in 30.4% of adolescent girls, higher in urban and wealthier households.[1] In the same survey, overweight and obesity in 5-9 year-olds was associated with micronutrient deficiency (OR 1.72).[2] Children with excess weight are assumed to be well nourished and are seldom screened. ID can exist without anaemia ('hidden anaemia'), escapes haemoglobin-based screening, and can impair attention and learning."), ("Rationale and controversy: ", "Proposed mechanisms are iron-poor, energy-dense diets; higher iron needs of a larger, growing body; and adiposity-related low-grade inflammation that raises hepcidin, blocking iron absorption and trapping iron in macrophages.[3,4] A 2025 meta-analysis of 42 studies reported higher odds of ID in children with obesity (OR 1.64; 95% CI 1.22-2.21; pooled prevalence 20.1% vs 16.1%) but no significant association with iron deficiency anaemia (OR 0.78; 95% CI 0.43-1.43).[5] A small Indian study (n = 71) reported a hypoferraemic state in 62%,[6] so estimates vary widely."), ("The measurement problem: ", "Ferritin is an acute-phase protein. Children with overweight or obesity have higher CRP and ferritin than normal-weight peers,[4,7] so a 'normal' ferritin may hide depleted stores. In 152 such children, low ferritin was found in only 2.6% but low transferrin saturation in 23.8%, suggesting that standard cut-offs under-detect ID here.[8] The 2020 WHO ferritin guideline advises pairing ferritin with an inflammation marker (<15 µg/L if CRP is normal; <70 µg/L if raised), and inflammation adjustment is established practice.[9] Ferritin-only estimates are therefore likely to understate ID."), ("Gap in knowledge: ", "To our knowledge, no CRP-adjusted ferritin data exist for children with overweight or obesity in Raigad district."), ("Defence of model and design: ", "A cross-sectional design suits prevalence estimation at one time point, and a pilot size fits available resources. Consecutive children with overweight or obesity attending a paediatric OPD will give one blood sample for ferritin (iron stores), CRP (inflammation, which sets the ferritin cut-off) and CBC (anaemia). Ferritin plus CRP yields adjusted ID prevalence; the CBC separates hidden anaemia from iron deficiency anaemia. Each objective is therefore answered from a single draw."), ] OBJ_P = ("To estimate the prevalence of iron deficiency, by CRP-adjusted serum ferritin (WHO 2020), in children aged 6-15 years with overweight or obesity in Raigad district, and the proportion of them without anaemia ('hidden anaemia').", "Primary objective: ") OBJ_S = [ "To estimate the prevalence of inflammation (CRP >5 mg/L) and compare ID classification by CRP-adjusted versus unadjusted ferritin (<15 µg/L).", "To estimate the prevalence of anaemia and iron deficiency anaemia from haemoglobin and red cell indices.", "To correlate BMI-for-age z-score with ferritin, CRP and haemoglobin.", "To compare ferritin, CRP and haemoglobin between overweight and obese children.", ] HYP = ("H0: the prevalence of ID in children with overweight or obesity is ≤15% (CNNS inflammation-adjusted estimate for 5-9 year-olds and boys).[1] H1: it exceeds 15%. With n = 36, an exact binomial test has about 67% power if true prevalence is 30% and 86% if 35%; at lower prevalence, results will be reported as estimates with 95% CIs.", "Hypothesis: ") M = [] # (lead, body) paragraphs M1 = [ ("Design and setting: ", "Cross-sectional, observational pilot study in the paediatric outpatient department of a tertiary care centre in Raigad district; 3 months, with about 8 weeks of recruitment."), ("Participants and sampling: ", "Consecutive eligible children aged 6-15 years with overweight or obesity."), ("Sample size: ", "n = Z²p(1-p)/d². Expected ID prevalence p = 0.20 (pooled prevalence in children with obesity, 20.1%[5]), Z = 1.96, d = 0.15, giving 28. Adding 20% for haemolysed or incomplete samples (28 ÷ 0.8 = 35), the target is 36. The 62% Indian figure was not used because the 'hypoferraemic state' in that study does not appear to be ferritin-defined (mean ferritin 41.9 µg/L).[6] With n = 36 the 95% CI half-width is about 13% at p = 0.20 and at most 16%. The size also reflects the funding ceiling (see Budget)."), ] DEFS = [ ("Overweight / obesity: ", "BMI-for-age z-score >+1 to ≤+2 SD / >+2 SD (WHO 2007 reference)."), ("Anaemia: ", "Hb <11.5 g/dL (5-11 y); <12.0 g/dL (12-14 y; girls ≥15 y); <13.0 g/dL (boys ≥15 y) (WHO)."), ("Inflammation: ", "CRP >5 mg/L; CRP >10 mg/L flagged as possible acute infection."), ("Iron deficiency (ID): ", "ferritin <15 µg/L if CRP ≤5 mg/L; <70 µg/L if CRP >5 mg/L (WHO 2020). Unadjusted ID (ferritin <15 µg/L regardless of CRP) is also reported for comparison with CNNS."), ("Hidden anaemia: ", "ID with Hb at or above the cut-off. Iron deficiency anaemia (IDA): ID with Hb below the cut-off, supported by MCV, MCH and RDW."), ] INCL = ["Age 6-15 years; BMI-for-age z-score >+1 SD.", "Written informed consent from parent/guardian; assent from the child (verbal 7-12 years, written 12-15 years, per ICMR guidelines)."] EXCL = ["Known haemoglobinopathy.", "Iron supplements, haematinics or blood transfusion in the past 3 months.", "Chronic inflammatory or systemic disease, or secondary/syndromic obesity.", "Fever or infection in the past 2 weeks."] M2 = [ ("Data collection: ", "A pre-tested proforma records age, sex, pubertal stage/menarche, diet type, frequency of iron-rich foods, fast food and tea/coffee, deworming in 6 months, physical activity, screen time and socioeconomic status (modified Kuppuswamy scale). One trained observer measures weight, height and waist circumference with calibrated instruments; z-scores are calculated with WHO AnthroPlus."), ("Blood sampling and tests: ", "One venipuncture of 3 mL (1 mL EDTA, 2 mL plain) at 8-10 am after an 8-10 hour overnight fast. Tests: serum ferritin (chemiluminescence immunoassay); quantitative CRP (immunoturbidimetry); CBC (automated 5-part analyser: Hb, MCV, MCH, RDW; Mentzer index, MCV/RBC, flags possible thalassaemia trait)."), ("Justification of panel: ", "Ferritin gives iron stores, CRP makes ferritin interpretable in a population with low-grade inflammation, and the CBC shows the haematological effect. Serum iron and TIBC were omitted: serum iron varies diurnally and falls with inflammation, and the pair costs INR 550 per child, over half the per-child cost. Transferrin saturation is planned for the larger study."), ("Quality control: ", "Accredited laboratory, same analyser throughout, two-level daily internal QC, coded samples processed within 2 hours, serum stored at -20°C if batched, haemolysed samples re-drawn."), ("Confidentiality: ", "Unique numeric codes; no names or hospital numbers in analysis files; password-protected database accessible only to the investigator and guide."), ] STATS = [ "Prevalence of ID (adjusted and unadjusted), hidden anaemia, anaemia and IDA with exact (Clopper-Pearson) 95% CI; median (IQR) for skewed data (ferritin and CRP log-transformed as needed).", "Exact binomial test against 15% (hypothesis); McNemar's test for adjusted versus unadjusted classification.", "Spearman correlation (BMI z-score vs ferritin, CRP, Hb); Mann-Whitney U test (overweight vs obese); Fisher's exact test for proportions.", "No multivariable model, as expected case numbers are too small; age, sex and pubertal stage described. Software: IBM SPSS v26 or later; p<0.05; secondary analyses exploratory.", ] M3 = [ ("Ethical considerations: ", "Institutional Ethics Committee approval before enrolment (ICMR National Ethical Guidelines, 2017), with the protocol, participant information sheet, parental consent form and child assent form in local languages. No intervention; trained staff; voluntary participation; withdrawal without effect on care. Results, including CRP, are shared with families; children with ID, anaemia or CRP >10 mg/L are referred to the treating paediatrician with dietary counselling."), ("Budget: ", "36 children × INR 515 (ferritin 145 + CRP 230 + CBC 140) = INR 18,540, plus INR 1,030 for two repeat draws; total INR 19,570 (ceiling INR 20,000)."), ("Limitations: ", "Single-centre OPD sample, so results apply to hospital attendees; wide confidence intervals; no transferrin saturation or soluble transferrin receptor; thalassaemia trait flagged by indices but not confirmed."), ] IMPL = "This pilot will give the first CRP-adjusted estimate of iron deficiency, including iron deficiency without anaemia, in children with overweight or obesity in Raigad district, and will show how much ignoring inflammation changes that estimate. It will not by itself justify a screening policy, but it will supply prevalence, variance and feasibility data to design an adequately powered ICMR study that adds transferrin saturation or soluble transferrin receptor. The student will gain skills in paediatric anthropometry, interpretation of iron and inflammation biomarkers, ethics documentation and biostatistics." REFS = [ "Kulkarni B, Peter R, Ghosh S, et al. Prevalence of iron deficiency and its sociodemographic patterning in Indian children and adolescents: findings from the Comprehensive National Nutrition Survey 2016-18. J Nutr. 2021. doi:10.1093/jn/nxab145. PMID: 34049401.", "Sethi V, Bassi S, Bahl D, et al. Prevalence of overweight and obesity and associated demographic and health factors in India: findings from the Comprehensive National Nutrition Survey (CNNS). Pediatr Obes. 2024;19(4):e13092. PMID: 38326947.", "Berton PF, Gambero A. Hepcidin and inflammation associated with iron deficiency in childhood obesity - a systematic review. J Pediatr (Rio J). 2024. doi:10.1016/j.jped.2023.06.002. PMID: 37541648.", "Doğan G, Andiran N, Çelik N, Uysal S. Iron parameters, pro-hepcidin and soluble transferrin receptor levels in obese children. Minerva Pediatr. 2020;72(3):219-26. PMID: 28006888.", "Sachdeva M, Malik M, Purohit A, et al. Association of iron deficiency and anemia with obesity among children: a systematic review and meta-analysis. Obes Rev. 2025;26(6):e13892. PMID: 39843408.", "Siyaram D, Bhatia P, Dayal D, Bhalla AK, Marathe R. Hypoferremic state in overweight and obese children. Indian Pediatr. 2018;55(1):72-3. PMID: 29396942.", "Ortiz Pérez M, Vázquez López MA, Ibáñez Alcalde M, et al. Relationship between obesity and iron deficiency in healthy adolescents. Child Obes. 2020;16(6):401-8. PMID: 32877290.", "Muzzio ML, Lozano Chiappe ES, Kabakian L, et al. Effects of pubertal status and inflammation on the use of ferritin to define iron deficiency in children with overweight or obesity. Nutr Metab Insights. 2019. doi:10.1177/1178638819839064. PMID: 31057325.", "Namaste SM, Rohner F, Huang J, et al. Adjusting ferritin concentrations for inflammation: Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia (BRINDA) project. Am J Clin Nutr. 2017;106(Suppl 1):359S-71S. PMID: 28615259.", ] def t(pairs): return sum(wc(a+b) for a,b in pairs) w_title = wc(TITLE) w_intro = t(INTRO) w_obj = wc(OBJ_P[0]) + sum(wc(x) for x in OBJ_S) + wc(HYP[0]) w_meth = t(M1)+t(DEFS)+sum(wc(x) for x in INCL+EXCL+STATS)+t(M2)+t(M3) w_impl = wc(IMPL); w_refs = sum(wc(r) for r in REFS) print("title",w_title,"intro",w_intro,"obj",w_obj,"meth",w_meth,"impl",w_impl,"refs",w_refs) P("STS2026-10322", size=11).runs[0].bold = True H(f"TITLE - Word count: {w_title}") P(TITLE).runs[0].bold = True H(f"INTRODUCTION - Word count: ~{w_intro}") for a,b in INTRO: P(b, a) H(f"OBJECTIVES - Word count: ~{w_obj}") P(OBJ_P[0], OBJ_P[1]); P("", "Secondary objectives:"); B(OBJ_S, "List Number"); P(HYP[0], HYP[1]) H(f"METHODOLOGY - Word count: ~{w_meth}") for a,b in M1: P(b,a) P("", "Operational definitions:"); B(DEFS) P("", "Inclusion criteria:"); B(INCL) P("", "Exclusion criteria:"); B(EXCL) for a,b in M2: P(b,a) P("", "Plan of analysis / statistical tools:"); B(STATS) for a,b in M3: P(b,a) H(f"IMPLICATIONS / STUDY OUTCOME - Word count: ~{w_impl}") P(IMPL) H(f"REFERENCES (Vancouver style, PubMed indexed) - Word count: ~{w_refs}") for i,r in enumerate(REFS,1): P(f"{i}. {r}", size=9.5).paragraph_format.space_after = Pt(2) out="/tmp/workspace/college-grant-proposal/STS_Proposal_Final_Raigad_Iron_Deficiency.docx" doc.save(out); print(out) EOF cd /tmp/workspace/college-grant-proposal && python3 build2.py

https://pubmed.ncbi.nlm.nih.gov/34049401/

https://pubmed.ncbi.nlm.nih.gov/29396942/

PMID: 34049401

cd /tmp/workspace/college-grant-proposal && timeout 60 curl -s "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi?db=pubmed&id=34049401,38326947,37541648,28006888,39843408,29396942,32877290,31057325,28615259&retmode=json" -o es.json; ls -la es.json; head -c 300 es.json

cd /tmp/workspace/college-grant-proposal && python3 -c " import json d=json.load(open('es.json'))['result'] for u in d['uids']: r=d[u] ids={i['idtype']:i['value'] for i in r['articleids']} print(u,'|',r['source'],'|',r['pubdate'],'| vol',r['volume'],'| iss',r['issue'],'| pages',r['pages'],'| elocid',r.get('elocationid')) print(' AU:',[a['name'] for a in r['authors']]) print(' TI:',r['title']) print(' pubtype:',r['pubtype']) "

cd /tmp/workspace/college-grant-proposal && python3 - <<'EOF' src = open('build2.py', encoding='utf-8').read() start = src.index("REFS = [") end = src.index("]\n\ndef t(pairs)") + 2 new = '''REFS = [ "Kulkarni B, Peter R, Ghosh S, Pullakhandam R, Thomas T, Reddy GB, et al. Prevalence of iron deficiency and its sociodemographic patterning in Indian children and adolescents: findings from the Comprehensive National Nutrition Survey 2016-18. J Nutr. 2021 Aug;151(8):2422-34. doi:10.1093/jn/nxab145. PMID: 34049401.", "Sethi V, Bassi S, Bahl D, Kumar A, Choedon T, Bhatia N, et al. Prevalence of overweight and obesity and associated demographic and health factors in India: findings from Comprehensive National Nutrition Survey (CNNS). Pediatr Obes. 2024 Apr;19(4):e13092. doi:10.1111/ijpo.13092. PMID: 38326947.", "Berton PF, Gambero A. Hepcidin and inflammation associated with iron deficiency in childhood obesity - a systematic review. J Pediatr (Rio J). 2024 Mar-Apr;100(2):124-31. doi:10.1016/j.jped.2023.06.002. PMID: 37541648.", "Doğan G, Andiran N, Çelik N, Uysal S. Iron parameters, pro-hepcidin and soluble transferrin receptor levels in obese children. Minerva Pediatr. 2020 Jun;72(3):175-81. doi:10.23736/S0026-4946.16.04273-0. PMID: 28006888.", "Sachdeva M, Malik M, Purohit A, Jain L, Kaur K, Pradhan P, et al. Association of iron deficiency and anemia with obesity among children: a systematic review and meta-analysis. Obes Rev. 2025 Jun;26(6):e13892. doi:10.1111/obr.13892. PMID: 39843408.", "Siyaram D, Bhatia P, Dayal D, Bhalla AK, Marathe R. Hypoferremic state in overweight and obese children. Indian Pediatr. 2018 Jan 15;55(1):72-3. PMID: 29396942.", "Ortíz Pérez M, Vázquez López MA, Ibáñez Alcalde M, Galera Martínez R, Martín González M, Lendínez Molinos F, et al. Relationship between obesity and iron deficiency in healthy adolescents. Child Obes. 2020 Sep;16(6):440-7. doi:10.1089/chi.2019.0276. PMID: 32877290.", "Muzzio ML, Lozano Chiappe ES, Kabakian L, Ferraro F, Landó I, Alonso E, et al. Effects of pubertal status and inflammation on the use of ferritin to define iron deficiency in children with overweight or obesity. Nutr Metab Insights. 2019;12:1178638819839064. doi:10.1177/1178638819839064. PMID: 31057325.", "Namaste SM, Rohner F, Huang J, Bhushan NL, Flores-Ayala R, Kupka R, et al. Adjusting ferritin concentrations for inflammation: Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia (BRINDA) project. Am J Clin Nutr. 2017 Jul;106(Suppl 1):359S-71S. doi:10.3945/ajcn.116.141762. PMID: 28615259.", ] ''' open('build2.py','w',encoding='utf-8').write(src[:start]+new+src[end:]) EOF python3 build2.py && python3 -c " from docx import Document d=Document('/tmp/workspace/college-grant-proposal/STS_Proposal_Final_Raigad_Iron_Deficiency.docx') ps=[p.text for p in d.paragraphs] i=[k for k,t in enumerate(ps) if t.startswith('REFERENCES')][0] print('\n'.join(ps[i:])) print('dash' , any(('—' in t or '–' in t) for t in ps)) "

python3 -c " from scipy.stats import binom import math n=35 k=min(k for k in range(n+1) if binom.sf(k-1,n,0.15)<=0.05) print('k',k,'alpha',round(binom.sf(k-1,n,0.15),4)) for p in (0.25,0.30,0.35,0.40): print(p, round(binom.sf(k-1,n,p),3)) for p in (0.2,0.5): print('hw',p, round(1.96*math.sqrt(p*(1-p)/n),4)) print(35*515, 3*515, 35*515+3*515, 35*145,35*230,35*140) print(1.96**2*0.2*0.8/0.15**2, 28/0.8) "; mkdir -p /tmp/workspace/grant-review-deck

cat > /tmp/workspace/grant-review-deck/build_deck.py <<'EOF' from pptx import Presentation from pptx.util import Inches, Pt from pptx.dml.color import RGBColor from pptx.enum.text import PP_ALIGN, MSO_ANCHOR from pptx.enum.shapes import MSO_SHAPE from pptx.chart.data import CategoryChartData from pptx.enum.chart import XL_CHART_TYPE, XL_LABEL_POSITION NAVY=RGBColor(0x1F,0x3A,0x6B); TEAL=RGBColor(0x2A,0x9D,0x8F); CORAL=RGBColor(0xE7,0x6F,0x51) INK=RGBColor(0x2B,0x2B,0x2B); GREY=RGBColor(0x6B,0x72,0x80); LIGHT=RGBColor(0xEE,0xF3,0xF9); WHITE=RGBColor(255,255,255) FONT="Calibri" prs=Presentation(); prs.slide_width=Inches(13.333); prs.slide_height=Inches(7.5) BL=prs.slide_layouts[6] def box(sl,x,y,w,h,fill=None,line=None,shape=MSO_SHAPE.RECTANGLE): s=sl.shapes.add_shape(shape,Inches(x),Inches(y),Inches(w),Inches(h)) if fill is None: s.fill.background() else: s.fill.solid(); s.fill.fore_color.rgb=fill if line is None: s.line.fill.background() else: s.line.color.rgb=line; s.line.width=Pt(1.25) s.shadow.inherit=False return s def text(sl,x,y,w,h,paras,size=16,color=INK,bold=False,align=PP_ALIGN.LEFT,anchor=MSO_ANCHOR.TOP,space=6): """paras: list of str or (str, dict) ; dict keys size,color,bold""" tb=sl.shapes.add_textbox(Inches(x),Inches(y),Inches(w),Inches(h)); tf=tb.text_frame tf.word_wrap=True; tf.vertical_anchor=anchor tf.margin_left=tf.margin_right=Inches(0.05); tf.margin_top=tf.margin_bottom=Inches(0.03) for i,p in enumerate(paras): o={} if isinstance(p,tuple): p,o=p para=tf.paragraphs[0] if i==0 else tf.add_paragraph() para.alignment=o.get("align",align); para.space_after=Pt(o.get("space",space)) r=para.add_run(); r.text=p; f=r.font; f.name=FONT f.size=Pt(o.get("size",size)); f.bold=o.get("bold",bold); f.color.rgb=o.get("color",color) return tb def bullets(sl,x,y,w,h,items,size=16,color=INK,space=8): tb=sl.shapes.add_textbox(Inches(x),Inches(y),Inches(w),Inches(h)); tf=tb.text_frame; tf.word_wrap=True tf.margin_left=tf.margin_right=Inches(0.05) for i,it in enumerate(items): para=tf.paragraphs[0] if i==0 else tf.add_paragraph(); para.space_after=Pt(space) lead=None if isinstance(it,tuple): lead,it=it r0=para.add_run(); r0.text="\u2022 "; r0.font.size=Pt(size); r0.font.color.rgb=TEAL; r0.font.bold=True; r0.font.name=FONT if lead: r=para.add_run(); r.text=lead+" "; r.font.bold=True; r.font.size=Pt(size); r.font.color.rgb=NAVY; r.font.name=FONT r=para.add_run(); r.text=it; r.font.size=Pt(size); r.font.color.rgb=color; r.font.name=FONT def base(title,n,notes,src=None): sl=prs.slides.add_slide(BL) box(sl,0,0,13.333,7.5,fill=WHITE) box(sl,0,0,0.18,7.5,fill=NAVY) text(sl,0.6,0.35,12.1,1.0,[title],size=28,color=NAVY,bold=True,anchor=MSO_ANCHOR.MIDDLE) box(sl,0.65,1.38,1.2,0.06,fill=TEAL) text(sl,0.6,7.0,6,0.3,["STS2026-10322"],size=11,color=GREY) if src: text(sl,3.2,7.0,8.6,0.3,[src],size=11,color=GREY,align=PP_ALIGN.RIGHT) text(sl,12.3,7.0,0.7,0.3,[str(n)],size=11,color=GREY,align=PP_ALIGN.RIGHT) sl.notes_slide.notes_text_frame.text=notes return sl def card(sl,x,y,w,h,big,label,fill=LIGHT,bigcolor=NAVY,bigsize=40,lsize=14): box(sl,x,y,w,h,fill=fill) box(sl,x,y,0.08,h,fill=TEAL) text(sl,x+0.25,y+0.1,w-0.4,h*0.5,[big],size=bigsize,color=bigcolor,bold=True,anchor=MSO_ANCHOR.MIDDLE) text(sl,x+0.25,y+h*0.52,w-0.4,h*0.45,[label],size=lsize,color=INK) # ---------- 1 Title sl=prs.slides.add_slide(BL) box(sl,0,0,13.333,7.5,fill=NAVY) box(sl,0.8,3.55,1.6,0.07,fill=TEAL) text(sl,0.8,0.5,6,0.4,["STS2026-10322"],size=14,color=RGBColor(0xBF,0xD0,0xE8)) text(sl,0.8,1.3,11.6,2.2,["Iron deficiency without anaemia in overweight and obese children"],size=40,color=WHITE,bold=True,anchor=MSO_ANCHOR.BOTTOM) text(sl,0.8,3.8,11.6,1.0,["A CRP-adjusted serum ferritin pilot study from Raigad district"],size=24,color=RGBColor(0xDD,0xE8,0xF5)) text(sl,0.8,5.4,11.6,0.8,["Grant review committee | Cross-sectional pilot | n = 35 | Budget INR 19,570"],size=16,color=RGBColor(0xBF,0xD0,0xE8)) sl.notes_slide.notes_text_frame.text=("Opening: one sentence. We want to find children who are iron deficient but not yet anaemic, " "and we want to measure them correctly by pairing ferritin with CRP. Total ask is under INR 20,000.") # ---------- 2 Why it matters sl=base("Excess weight and iron deficiency now coexist in Indian children",2, "CNNS 2016-18 is the national benchmark. Inflammation-adjusted iron deficiency by ferritin <15 ug/L was 11-15% in 5-9 year olds and adolescent boys and 30.4% in adolescent girls, higher in urban and richer households. " "In the same survey, overweight and obesity in 5-9 year olds was linked to micronutrient deficiency (OR 1.72). Point to make: these children are assumed to be well nourished, so nobody looks.", "Sources: CNNS analyses [1,2]") card(sl,0.65,1.8,3.9,2.0,"11-15%","iron deficiency (ferritin <15 \u00b5g/L, inflammation-adjusted) in children 5-9 y and adolescent boys") card(sl,4.7,1.8,3.9,2.0,"30.4%","iron deficiency in adolescent girls; higher in urban and wealthier households") card(sl,8.75,1.8,3.9,2.0,"OR 1.72","overweight or obesity and micronutrient deficiency in children aged 5-9 y",bigcolor=CORAL) box(sl,0.65,4.2,12.0,2.2,fill=LIGHT) text(sl,0.9,4.3,11.5,2.0,[ ("The blind spot",{"bold":True,"color":NAVY,"size":20}), ("Children with excess weight are assumed to be well nourished, so they are rarely screened.",{"size":18}), ("Iron deficiency can exist with a normal haemoglobin ('hidden anaemia'). Haemoglobin-only screening misses it, and it can impair attention and learning.",{"size":18}), ],space=8) # ---------- 3 Evidence & controversy sl=base("The evidence is mixed, and ferritin alone can mislead",3, "Left: the 2025 meta-analysis of 42 studies shows more iron deficiency in children with obesity, but no significant increase in iron deficiency anaemia. The Indian figure of 62% comes from 71 children and does not look ferritin-defined (mean ferritin 41.9). " "Right: ferritin is an acute-phase protein. Obesity is a low-grade inflammatory state, so ferritin and CRP are higher and a normal ferritin may hide empty stores. Muzzio found low ferritin in 2.6% but low transferrin saturation in 23.8% of 152 children. WHO 2020 recommends reading ferritin with an inflammation marker.", "Sources: [3-9]") box(sl,0.65,1.8,5.9,4.9,fill=LIGHT) text(sl,0.85,1.9,5.5,0.5,["What the studies show"],size=20,color=NAVY,bold=True) bullets(sl,0.85,2.5,5.5,4.2,[ ("Meta-analysis, 42 studies:","iron deficiency OR 1.64 (95% CI 1.22-2.21); prevalence 20.1% vs 16.1%."), ("Anaemia:","iron deficiency anaemia OR 0.78 (0.43-1.43), not significant."), ("Indian study, n = 71:","62% 'hypoferraemic', but mean ferritin was 41.9 \u00b5g/L, so the definition is unclear."), ],size=16) box(sl,6.8,1.8,5.9,4.9,fill=LIGHT) text(sl,7.0,1.9,5.5,0.5,["Why ferritin alone misleads"],size=20,color=CORAL,bold=True) bullets(sl,7.0,2.5,5.5,4.2,[ "Ferritin is an acute-phase protein that rises with inflammation.", "Children with obesity have higher CRP and ferritin than normal-weight peers.", ("In 152 children with overweight or obesity:","low ferritin 2.6% vs low transferrin saturation 23.8%."), ("WHO 2020:","read ferritin together with CRP."), ],size=16) # ---------- 4 Gap and objectives sl=base("The gap: no CRP-adjusted iron data for Raigad district",4, "State the research question first. Primary objective is prevalence of iron deficiency using CRP-adjusted ferritin, and the share who are not anaemic. Four secondary objectives, each answered from the same blood draw. " "Hypothesis uses the CNNS 15% benchmark. Be honest: it is a pilot, so power is limited unless true prevalence is about 30% or higher.") box(sl,0.65,1.75,12.0,1.2,fill=NAVY) text(sl,0.9,1.8,11.5,1.1,["Among children aged 6-15 years with overweight or obesity in Raigad district, how many are iron deficient once ferritin is read against CRP, and how many of them are not anaemic?"],size=19,color=WHITE,bold=True,anchor=MSO_ANCHOR.MIDDLE) text(sl,0.65,3.15,6.3,0.4,["Objectives"],size=20,color=NAVY,bold=True) bullets(sl,0.65,3.6,6.3,3.4,[ ("Primary:","prevalence of iron deficiency by CRP-adjusted ferritin (WHO 2020), and the proportion without anaemia."), ("2:","prevalence of inflammation; adjusted vs unadjusted classification."), ("3:","prevalence of anaemia and iron deficiency anaemia."), ("4:","correlation of BMI z-score with ferritin, CRP, Hb."), ("5:","overweight vs obese comparison."), ],size=15,space=6) box(sl,7.2,3.15,5.45,3.6,fill=LIGHT) text(sl,7.4,3.25,5.1,0.4,["Hypothesis"],size=20,color=CORAL,bold=True) text(sl,7.4,3.8,5.1,2.9,[ ("H0: iron deficiency prevalence \u2264 15%",{"bold":True,"color":NAVY}), ("H1: prevalence > 15%",{"bold":True,"color":NAVY}), "15% is the CNNS adjusted estimate for 5-9 year olds and boys. Tested by an exact binomial test; results are also reported as estimates with 95% CI." ],size=16,space=8) # ---------- 5 Design sl=base("Design: consecutive OPD children, one blood draw",5, "Cross-sectional is the right design for prevalence at one time point. Consecutive sampling limits selection bias. Anthropometry by one observer. Site is written as district only, per STS rules. " "Exclusions remove known causes of confounding: haemoglobinopathy, recent iron or transfusion, chronic or secondary causes, and acute infection.") box(sl,0.65,1.75,5.9,5.0,fill=LIGHT) text(sl,0.85,1.85,5.5,0.4,["Study frame"],size=20,color=NAVY,bold=True) bullets(sl,0.85,2.4,5.5,4.3,[ ("Type:","cross-sectional, observational pilot."), ("Setting:","paediatric OPD, tertiary care centre, Raigad district."), ("Duration:","3 months; recruitment about 8 weeks."), ("Sampling:","consecutive eligible children."), ("Measures:","weight, height, waist; BMI z-score (WHO 2007); pubertal stage; diet; activity."), ],size=16) box(sl,6.8,1.75,5.85,2.35,fill=LIGHT) text(sl,7.0,1.85,5.5,0.4,["Include"],size=20,color=TEAL,bold=True) bullets(sl,7.0,2.35,5.5,1.8,["Age 6-15 years","BMI z-score > +1 SD (overweight or obese)","Parental consent; child assent per ICMR"],size=15,space=4) box(sl,6.8,4.3,5.85,2.45,fill=LIGHT) text(sl,7.0,4.4,5.5,0.4,["Exclude"],size=20,color=CORAL,bold=True) bullets(sl,7.0,4.9,5.5,1.9,["Known haemoglobinopathy","Iron, haematinics or transfusion in past 3 months","Chronic or systemic disease; secondary or syndromic obesity","Fever or infection in past 2 weeks"],size=15,space=4) # ---------- 6 Sample size sl=base("Sample size: 35 children, from a calculation that fits the budget",6, "n = Z squared times p(1-p) over d squared. With p 0.20 from the 2025 meta-analysis, Z 1.96 and d 0.15, we need 28. Add 20% for haemolysed or incomplete samples: 28 divided by 0.8 equals exactly 35. " "We did not use 62% because that study's hypoferraemic state does not look ferritin-defined. Precision: half-width about 13% at 20% prevalence and at most about 17% in the worst case. " "Power of the binomial test against 15%: about 63% if true prevalence is 30%, and 84% if 35%. Say plainly that this is a pilot.", "Sources: pooled prevalence from [5]; Siyaram [6]") card(sl,0.65,1.8,3.55,1.8,"p = 0.20","expected prevalence: pooled ID in children with obesity (20.1%)",bigsize=32) card(sl,4.4,1.8,3.55,1.8,"n = 28","Z = 1.96, d = 0.15: n = Z\u00b2p(1-p)/d\u00b2",bigsize=32) card(sl,8.15,1.8,4.5,1.8,"35","28 \u00f7 0.8: allows 20% for haemolysed or incomplete samples",bigcolor=CORAL,bigsize=44) box(sl,0.65,3.95,12.0,2.8,fill=LIGHT) text(sl,0.9,4.05,5.6,0.4,["What 35 children can tell us"],size=20,color=NAVY,bold=True) bullets(sl,0.9,4.55,5.6,2.2,[ "95% CI half-width: about 13% at 20% prevalence; at most about 17%.", "Power against 15%: 63% if true prevalence is 30%; 84% if 35%.", ],size=15) text(sl,6.9,4.05,5.6,0.4,["Why not 62%?"],size=20,color=CORAL,bold=True) text(sl,6.9,4.55,5.6,2.1,["The 62% came from 71 children and was a 'hypoferraemic state', not a ferritin-defined deficiency (mean ferritin 41.9 \u00b5g/L). The pooled meta-analysis figure is the safer planning value."],size=15) # ---------- 7 Algorithm sl=base("CRP decides which ferritin cut-off applies",7, "This is the core idea. One draw gives ferritin, CRP and CBC. If CRP is 5 mg/L or less, deficiency is ferritin below 15. If CRP is above 5, ferritin is read against 70 per WHO 2020. We also report unadjusted ferritin below 15 for comparison with CNNS. " "Then Hb separates hidden anaemia from iron deficiency anaemia. CRP above 10 is flagged as possible acute infection and referred. Mentzer index flags possible thalassaemia trait.", "WHO 2020 ferritin guideline; WHO Hb cut-offs; [9]") box(sl,0.65,1.8,3.0,1.1,fill=NAVY) text(sl,0.65,1.8,3.0,1.1,["One blood draw (3 mL)",("Ferritin + CRP + CBC",{"size":14,"bold":False})],size=17,color=WHITE,bold=True,align=PP_ALIGN.CENTER,anchor=MSO_ANCHOR.MIDDLE,space=2) box(sl,4.4,1.5,3.9,1.0,fill=LIGHT,line=TEAL) text(sl,4.4,1.5,3.9,1.0,["CRP \u2264 5 mg/L",("ID if ferritin < 15 \u00b5g/L",{"size":14,"bold":False})],size=17,color=NAVY,bold=True,align=PP_ALIGN.CENTER,anchor=MSO_ANCHOR.MIDDLE,space=2) box(sl,4.4,2.75,3.9,1.0,fill=LIGHT,line=CORAL) text(sl,4.4,2.75,3.9,1.0,["CRP > 5 mg/L",("ID if ferritin < 70 \u00b5g/L",{"size":14,"bold":False})],size=17,color=CORAL,bold=True,align=PP_ALIGN.CENTER,anchor=MSO_ANCHOR.MIDDLE,space=2) box(sl,9.1,1.8,3.55,1.1,fill=NAVY) text(sl,9.1,1.8,3.55,1.1,["Iron deficient?",("then check haemoglobin",{"size":14,"bold":False})],size=17,color=WHITE,bold=True,align=PP_ALIGN.CENTER,anchor=MSO_ANCHOR.MIDDLE,space=2) for (x1,y1,x2,y2) in [(3.65,2.35,4.4,2.0),(3.65,2.35,4.4,3.25),(8.3,2.0,9.1,2.3),(8.3,3.25,9.1,2.5)]: c=sl.shapes.add_connector(1,Inches(x1),Inches(y1),Inches(x2),Inches(y2)); c.line.color.rgb=GREY; c.line.width=Pt(1.75) card(sl,0.65,4.2,5.9,1.6,"Hidden anaemia","iron deficient with Hb at or above the WHO cut-off for age and sex",bigsize=26,bigcolor=TEAL) card(sl,6.75,4.2,5.9,1.6,"Iron deficiency anaemia","iron deficient with Hb below the cut-off, supported by MCV, MCH and RDW",bigsize=26,bigcolor=CORAL) text(sl,0.65,6.0,12.0,0.9,["Also reported: unadjusted ferritin < 15 \u00b5g/L for CNNS comparison. CRP > 10 mg/L is flagged as possible acute infection. Mentzer index (MCV/RBC) flags possible thalassaemia trait."],size=14,color=GREY) # ---------- 8 Analysis and ethics sl=base("Analysis is simple and honest for a pilot; ethics is in place",8, "No multivariable model because case numbers are too small. Exact Clopper-Pearson intervals. McNemar compares adjusted vs unadjusted classification, which is the direct test of what CRP adds. " "Ethics: IEC approval before enrolment under ICMR 2017 guidelines, with protocol, information sheet, consent and assent forms in local languages. Everyone is told their results; children with deficiency, anaemia or CRP above 10 are referred.") box(sl,0.65,1.75,5.9,5.0,fill=LIGHT) text(sl,0.85,1.85,5.5,0.4,["Plan of analysis"],size=20,color=NAVY,bold=True) bullets(sl,0.85,2.4,5.5,4.3,[ "Prevalence with exact (Clopper-Pearson) 95% CI; median (IQR) for skewed data.", "Exact binomial test against 15%.", "McNemar's test: adjusted vs unadjusted ID.", "Spearman: BMI z-score vs ferritin, CRP, Hb.", "Mann-Whitney U: overweight vs obese; Fisher's exact for proportions.", "No multivariable model (too few cases); SPSS v26 or later.", ],size=15,space=6) box(sl,6.8,1.75,5.85,5.0,fill=LIGHT) text(sl,7.0,1.85,5.5,0.4,["Ethics and safety"],size=20,color=NAVY,bold=True) bullets(sl,7.0,2.4,5.5,4.3,[ "IEC approval before enrolment (ICMR 2017).", "Information sheet, parental consent and child assent forms in local languages.", "Single 3 mL venipuncture; voluntary; withdrawal without effect on care.", "Coded data, password-protected.", "Results shared; referral for deficiency, anaemia or CRP > 10 mg/L.", ],size=15,space=6) # ---------- 9 Budget sl=base("Budget: INR 19,570 against a ceiling of INR 20,000",9, "The original three-test panel at 50 children would cost INR 41,750. All four tests at 50 children would cost 53,250. Our design costs INR 515 per child: ferritin 145, CRP 230, CBC 140. 35 children = 18,025. " "Three repeat draws for haemolysed or clotted samples = 1,545. Total 19,570. Iron and TIBC (INR 550) were deliberately deferred: serum iron varies through the day and falls with inflammation, and the pair is over half of the per-child cost. Transferrin saturation goes into the larger study.") cd=CategoryChartData(); cd.categories=["Original panel, n = 50","All 4 tests, n = 50","This proposal, n = 35","Grant ceiling"] cd.add_series("INR",(41750,53250,19570,20000)) gf=sl.shapes.add_chart(XL_CHART_TYPE.COLUMN_CLUSTERED,Inches(0.65),Inches(1.7),Inches(6.6),Inches(5.0),cd); ch=gf.chart ch.has_legend=False; ch.has_title=False pl=ch.plots[0]; pl.gap_width=60; pl.has_data_labels=True dl=pl.data_labels; dl.number_format='#,##0'; dl.number_format_is_linked=False; dl.position=XL_LABEL_POSITION.OUTSIDE_END dl.font.size=Pt(13); dl.font.bold=True; dl.font.name=FONT cols=[GREY,GREY,TEAL,CORAL] for i,c in enumerate(cols): pt=pl.series[0].points[i]; pt.format.fill.solid(); pt.format.fill.fore_color.rgb=c ch.category_axis.tick_labels.font.size=Pt(12); ch.category_axis.tick_labels.font.name=FONT ch.value_axis.visible=False; ch.value_axis.has_major_gridlines=False; ch.value_axis.maximum_scale=62000 rows=[("Item","Cost (INR)"),("Ferritin: 35 \u00d7 145","5,075"),("CRP: 35 \u00d7 230","8,050"),("CBC: 35 \u00d7 140","4,900"),("Subtotal (515 per child)","18,025"),("3 repeat draws (3 \u00d7 515)","1,545"),("Total","19,570")] tbl=sl.shapes.add_table(len(rows),2,Inches(7.6),Inches(1.8),Inches(5.05),Inches(3.4)).table tbl.columns[0].width=Inches(3.45); tbl.columns[1].width=Inches(1.6) for i,(a,b) in enumerate(rows): for j,v in enumerate((a,b)): c=tbl.cell(i,j); c.text=v; p=c.text_frame.paragraphs[0]; r=p.runs[0]; r.font.size=Pt(14); r.font.name=FONT p.alignment=PP_ALIGN.RIGHT if j==1 else PP_ALIGN.LEFT c.fill.solid() if i==0: c.fill.fore_color.rgb=NAVY; r.font.color.rgb=WHITE; r.font.bold=True elif i==len(rows)-1: c.fill.fore_color.rgb=RGBColor(0xDD,0xF1,0xEE); r.font.bold=True; r.font.color.rgb=INK else: c.fill.fore_color.rgb=LIGHT if i%2 else WHITE; r.font.color.rgb=INK text(sl,7.6,5.4,5.05,1.4,[("Deferred to the larger study: serum iron and TIBC (INR 550 per child), which varies by time of day and falls with inflammation.",{"size":14,"color":GREY})]) # ---------- 10 Outcomes and limits sl=base("Expected outcome: pilot data to power the next ICMR study",10, "Close by being modest. We will not claim a screening policy from 35 children. We will deliver the first CRP-adjusted estimate for the district, the size of the effect of ignoring inflammation, and the variance and feasibility data for a powered multicentre study with transferrin saturation or sTfR. " "Limitations are real and stated: single-centre OPD sample, wide intervals, no TSAT, thalassaemia trait flagged but not confirmed. Ask the committee for the INR 19,570.", "Key sources: Kulkarni 2021; Sachdeva 2025; Muzzio 2019; Namaste 2017") box(sl,0.65,1.75,5.9,4.3,fill=LIGHT) text(sl,0.85,1.85,5.5,0.4,["What we will deliver"],size=20,color=TEAL,bold=True) bullets(sl,0.85,2.4,5.5,3.6,[ "First CRP-adjusted iron deficiency estimate for this population in Raigad district.", "How much ignoring inflammation changes the estimate.", "Whether haemoglobin-only screening misses affected children.", "Variance and feasibility data for a powered ICMR study.", "Student skills: anthropometry, biomarkers, ethics, biostatistics.", ],size=15,space=6) box(sl,6.8,1.75,5.85,4.3,fill=LIGHT) text(sl,7.0,1.85,5.5,0.4,["Limitations we state up front"],size=20,color=CORAL,bold=True) bullets(sl,7.0,2.4,5.5,3.6,[ "Single-centre OPD sample: applies to hospital attendees.", "Pilot size: wide confidence intervals.", "No transferrin saturation or sTfR in this phase.", "Thalassaemia trait flagged by indices, not confirmed.", ],size=15,space=6) box(sl,0.65,6.2,12.0,0.65,fill=NAVY) text(sl,0.65,6.2,12.0,0.65,["Request: INR 19,570 to generate the first CRP-adjusted iron data for children with excess weight in Raigad district."],size=17,color=WHITE,bold=True,align=PP_ALIGN.CENTER,anchor=MSO_ANCHOR.MIDDLE) out="/tmp/workspace/grant-review-deck/Grant_Review_Committee_Deck.pptx" prs.save(out); print(out) EOF cd /tmp/workspace/grant-review-deck && python3 build_deck.py

cd /tmp/workspace/grant-review-deck && python3 - <<'EOF' import math from pptx import Presentation from pptx.util import Emu prs=Presentation('Grant_Review_Committee_Deck.pptx') for i,sl in enumerate(prs.slides,1): for sh in sl.shapes: if not sh.has_text_frame or not sh.text_frame.text.strip(): continue w=sh.width/914400-0.15; h=sh.height/914400 tot=0 for p in sh.text_frame.paragraphs: txt=''.join(r.text for r in p.runs) if not p.runs: continue sz=p.runs[-1].font.size.pt if p.runs[-1].font.size else 16 cpl=max(1,w*72/(sz*0.5)) lines=max(1,math.ceil(len(txt)/cpl)) sa=(p.space_after.pt if p.space_after else 0) tot+=lines*sz*1.2/72+sa/72 if tot>h*1.0+0.05: print('slide',i,'OVER est',round(tot,2),'> box',round(h,2),'|',sh.text_frame.text[:50].replace('\n',' / ')) print('checked') EOF cd /tmp/workspace/college-grant-proposal && python3 - <<'EOF' s=open('build2.py',encoding='utf-8').read() reps=[ ("With n = 36, an exact binomial test has about 67% power if true prevalence is 30% and 86% if 35%;","With n = 35, an exact binomial test has about 63% power if true prevalence is 30% and 84% if 35%;"), ("the target is 36. The 62% Indian","the target is 35. The 62% Indian"), ("With n = 36 the 95% CI half-width is about 13% at p = 0.20 and at most 16%.","With n = 35 the 95% CI half-width is about 13% at p = 0.20 and at most 17%."), ("36 children × INR 515 (ferritin 145 + CRP 230 + CBC 140) = INR 18,540, plus INR 1,030 for two repeat draws; total INR 19,570 (ceiling INR 20,000).","35 children × INR 515 (ferritin 145 + CRP 230 + CBC 140) = INR 18,025, plus INR 1,545 for three repeat draws; total INR 19,570 (ceiling INR 20,000)."), ] for a,b in reps: assert a in s, a[:40] s=s.replace(a,b) open('build2.py','w',encoding='utf-8').write(s) EOF python3 build2.py; grep -c "36" build2.py

"ferritin"[MeSH Terms] AND "iron deficiency" AND "C-reactive protein"