Che materia stai cercando?

Anteprima

ESTRATTO DOCUMENTO

From Proteins to Proteomes:

Large Scale Protein

Identification by Two-

Dimensional Electrophoresis

and Amino Acid Analysis

Marc R. Wilkins et al. Nature Biotechnology

14, 61 - 65 (1996)

Publications dealing with proteomics: 31.485

Clinical *

Proteomics

Proteomics * * Clinical Proteomics

(Nephrology)

* Fino a settembre 2010

Base metodologica dell’indagine proteomica

Comparison of techniques

Genomics

Proteomics Adapted from Negm et al. 2002

Why proteomics?

GENES BEHAVIOR

Cover Illustration, The Economist, Dec 13, 2002

Why Proteomics ?

Different urine protein

Different histological

lesions in a glomerulus profiling

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APPLICAZIONE DELLA PROTEOMICA ALLA RICERCA DI

BIOMARCATORI DI MALATTIA: LA PROTEOMICA CLINICA

La speranza Le insidie

Key points:

¾ Appropriate sample collection

¾

Definition of the gold standard

protocol for the chosen biological

samples

¾ Definition of the clinical question

and identification of the patients to

be enrolled

¾ Identification and validation of

the candidate biomarkers

¾ Appropriate sample collection :

BIOBANKING of Biological Samples

DEFINITION

Biobank is ‘a collection of biological material and the associated

data and information stored in an organized system for a

population or a large subset of a population’(Organisation for

Economic Cooperation and Development- OECD)

COLLEZIONE DI MATERIALE BIOLOGICO IN ITALIA

• Presenza di numerose collezioni

“private” curate da singoli

ricercatori il cui uso è limitato al

gruppo di ricerca che ne dispone

• Carenza di personale dedicato

• Carenza di fondi per adeguare gli

impianti alle normative vigenti

(stoccaggio dei campioni in

sicurezza; gestione informatizzata

dei dati)

IMPATTO SOCIALE E CLINICO

LIMITATO

NEW STRATEGIES FOR THE STUDY OF COMPLEX

DISEASES

Most complex diseases are elusive as they do not root in single defects, but are

caused by a large number of small, often additive effects from genetic

predisposition, lifestyle and the environment.

Discovery of adequate biomarkers will depend critically

on the study of large collections of well documented, up-

to-date epidemiological, clinical and biological

information and accompanying material from large

numbers of patients and healthy persons.

LARGE SAMPLE COLLECTIONS ARE NEEDED TO

MAXIMIZE THE “OMICS” POTENTIALITY

GENOME PROTEOME

TRANSCRIPTOME METABONOME v

v

ENVIROMENT DIET

MODALITÀ DI CONSERVAZIONE DEI CAMPIONI

• Secure storage upon the principle of having mirror sites,

with samples being split and banked in separate locations

(freezers or geographic).

• Robust data tracking system (secure data storage and

effective interrogation) with a well-maintained record of

every step in the banking process

• Strictly access-controlled system with automatic data

logging

• Barcoding schemes to allow unique identification of each

sample and aliquot while displaying no personally identifying

information

D.H. Jackson and R.E. Banks, Proteomics Clin. Appl. 2010, 4, 250–270

TIPOLOGIA DEL MATERIALE CONSERVATO

• Cellule

• colture cellulari sia primarie che

derivate

• tessuti adulti e fetali normali e controlli pre-analitici

patologici

• acidi nucleici

• proteine

• liquidi biologici

NECESSITA’ DI STANDARDIZZARE LE VARIABILI PRE-

ANALITICHE

D.H. Jackson and R.E. Banks, Proteomics Clin. Appl. 2010, 4, 250–270

ASPETTI ETICI E GIURIDICI:

Aspetti etici e giuridici

Tutela della riservatezza

La tutela dei diritti della persona e in particolare della riservatezza di

ogni individuo è uno degli aspetti più delicati nella gestione di una

biobanca

Normativa vigente:

Autorizzazione n. 2/2004 del Garante per la protezione dei dati personali

“il trattamento dei dati genetici può

essere effettuato solo da coloro che

svolgono attività sanitarie in senso

stretto o attività di ricerca”

ASPETTI ETICI E GIURIDICI:

Informativa e consenso

L’Informativa deve contenere:

• le finalità perseguite

• i risultati conseguibili

• il diritto dell’interessato di opporsi al trattamento per motivi legittimi

• la possibilità dell’interessato di limitare l’accesso ai propri dati e ai

campioni

• la disponibilità ad usare questi ultimi per ulteriori scopi

• il periodo di conservazione dei dati e dei campioni biologici

Consenso informato

Autorizzazione alla raccolta e al

trattamento dei dati derivanti dal

materiale depositato Oltre i localismi:

LA POLITICA EUROPEA PER LE INFRASTRUTTURE DI

RICERCA

“…A European Research Infrastructure Consortium (hereinafter

referred to as an ‘ERIC’) involves facilities, resources and related

services that are used by the scientific community to conduct

top-level research in their respective fields and covers major

scientific equipment or sets of instruments” (art. 2)

EUROPEAN STRATEGY FORUM ON RESEARCH

INFRASTRUCTURES – ESFRI

“The ESFRI raodmap aims at building a coordinating,

large scale European research infrastructure of already

existing and de novo collections of biomedically relevant,

quality-assessed samples (with the possibility to link to

related clinical and epidemiological information), to

enhance therapy and prevention of common and rare

diseases, including cancer”

LE ATTUALI INFRASRUTTURE DI RICERCA EUROPEE

“BBMRI is a pan-European and

internationally broadly accessible

research infrastructure that include

samples from patients and healthy

persons, representing different

European populatios (with links to

epidemiological and health care

information), molecular genomic

resources and biocomputational tools

to optimally exploit this resource for

global biomedical research”.

I.N.M.I. "L. Spallanzani"

I.R.C.C.S.

Istituto Superiore di Sanità

(HUB Italiano)

Coordina le attività di 23 biobanche

presenti sul territorio nazionale tra cui:

¾ Definition of the gold standard protocol for the

chosen biological samples:

STANDARDIZATION OF URINE ANALYSIS

Cancer Biomarker Discovery Sample Sources –

Dilution of Markers with Distance from Tumor

Neoplastic Body

Sample Type Cytology

Tissue Fluids

Biomarker Concentration High Medium Low

Seminal Plasma Serum

Biopsy

Sample(s) suitable for Nipple Aspirates Plasma

LCM Fine Needle

proteomic analysis Urine

Aspirates

Biologial samples and Kidney biomarkers

Sample type Biomarkers amount

Biopsy specimens High

intermediate

Urine Low

Serum/Plasma

Urine advantages

Less complex then serum and plasma

Un-invasive recruitment in large

amount Higher presence of kidney derived

molecules

STANDARDIZATION OF URINE ANALYSIS

Fresh sample or frozen one? Time controlled collection or not?

Urine

Storage at RmT: how long can it stay? Between‐subject differences?

Can protease inhibitors efficiently

prevent protein degradation?

CONCLUDING REMARKS

The timing of the collection can affect the proteomic profile

Urine are stable up to 2 hours at RmT after collection

Repeated freeze and thaw cycles do not alter the protein profiles

Large inter‐individual variability

J. Chromatogr. B. Analyt. Technol. Biomed. Life Sci., 856 (2007) 205‐213

Papale M, Di Paolo S, Gesualdo L et Al

¾ Definition of the clinical question and identification of

the patients to be enrolled:

BIOMARKER DISCOVERY STUDIES FOR:

DIAGNOSIS

PROGNOSIS

THERAPY MONITORING

Study Design (

pre - clinical

considerations )

Pre-Analytical Factors Analytical Factors

Pre-Analytical Factors Analytical Factors

• Lab technique.

• Clinical inclusion/exclusion

criteria. • Automation vs. manual

protocols.

• Subject demographics (race, age). • Fractionation protocols.

• Sample size. • Randomization during

• Sample type (EDTA plasma, processing.

Citrate plasma, serum, lysate). • Array binding and washing

• Sample collection Protocol (time protocols.

for clotting, time ‘on the bench’, • Array reading protocols.

transport, aliquoting). • Data pre-processing

• Sample Storage (age, temperature, protocols (Baseline

freeze-thaw cycles). subtraction, noise definition,

normalisation).

• Classification approaches.

Study Design (Clinical aspects)

• Two types of studies:

– Prospective study: enroll patients and examine what

happens to them.

ƒ Case-controlled (clinical trials).

ƒ Cohort – patients self select their group.

ƒ Longitudinal.

– Retrospective study: choose patients from a

databank and examine what happened to them.

ƒ Case-controlled.

ƒ Cross-sectional.

¾ Identification and validation of the candidate

biomarkers:

THE AIMS OF A CLINICAL PROTEOMIC STUDY

Biomarker Discovery and Validation

Work plan

• Discovery phase

– Develop assay conditions.

– Find candidate markers.

– Develop classification rules.

• Validation phase

– Large sample set size.

– Subset of assay conditions.

– Refine classification rules.

• Identification phase

– Identify a candidate biomarker and validate its association

with the condition you are studying by alternative approaches

PROTEOMICA CLINICA:

LA NOSTRA ESPERIENZA

• INDIVIDUAZIONE DI NUOVI BIOMARCATORI DI URINARI

DI CARCINOMA RENALE (RC)

• DIAGNOSI DIFFERENZIALE DELLA NEFROPATIA DIABETICA

• FARMACO-PROTEOMICA DELLA NEFROPATIA DI BERGER (IgAN)

Cross-comparison between tissue and urine

proteomics allowed to identify RCC-specific

urinary biomarkers

Massimo Papale, Margherita Gigante, Clelia Prattichizzo, Maria Teresa

Rocchetti, Grazia Vocino, Michele Battaglia, Loreto Gesualdo and Elena

Ranieri

Renal Cell Carcinoma (RCC)

Most common solid tumor of the kidney that accounts

about 3% of all adult malignancies

Histological Classification (Mainz):

RCC: anatomy and histological characteristics

RCC

Diagnosis Therapy

Complete Blood Count (CBC)

Ultrasound examination

CT with contrast

NMR Radical

nephrectomy

Skeletal scintigraphy

Urography

Arteriography

Urinalysis

Identification of RCC Biomarkers: a current

challenge

AIMS OF THE STUDY

• To identify the best sample for biomarkers

validation

• To validate the specificity of the identified

biomarkers for RCC

Clinical features

HS RCC Non-RCC

N. 21 N.22 N.11

Sex (M/F) 15/6 13/9 8/3

Age 54 63 67

(median)

Grading • 1 ( 9% ) 1 (18%)

• 2 (54%) 2 (54%)

- • 3 (37%) 3 (28%)

TNM - T N0/MX T N0/MX

n n

(100%) (100%)

EXPERIMENTAL DESIGN

SERUM PROFILING

URINE PROFILING CONFIRMATION

RCC (early stage)

RCC (early stage) 15 serum samples

22 pre-operative Vs.

Vs.

urine samples Vs.

PHASE Healthy Subjects

Healthy Subjects 15 serum samples

21 urine samples

DISCOVERY TISSUE IMAGING

TISSUE PROFILING LOCALIZATION

RCC (early stage) RCC (early stage)

10 RCC Vs.

Vs.

Unaffected kidney Unaffected kidney

10 non-RCC PHASE I

SELDI PROFILING ON URINE, TISSUES AND SERA

5000 7500 10000 12500

20 Node 1

Class = DN

15 C08586_0 <= 19.726

Class Cases %

DN 31 43.1

10 Other CKD 41 56.9

W = 72.000

N = 72

5 C08586_0 <= 19.726 C08586_0 > 19.726

0 Terminal Node 2

Node 1 Class = DN

Class = Other CKD C013593_ <= 3.010

Class Cases % Class Cases %

30 DN 0 0.0 DN 31 60.8

Other CKD 21 100.0 Other CKD 20 39.2

W = 21.000 W = 51.000

N = 21 N = 51

20 C013593_ <= 3.010 C013593_ > 3.010

10 Node 3 Terminal

Class = Other CKD Node 4

C03086_1 <= 1.605 Class = DN

Class Cases % Class Cases %

0 DN 1 6.7 DN 30 83.3

Other CKD 14 93.3 Other CKD 6 16.7

W = 15.000 W = 36.000

N = 15 N = 36

10 C03086_1 <= 1.605 C03086_1 > 1.605

Terminal Terminal

Node 2 Node 3

Class = DN Class = Other CKD

5 Class Cases % Class Cases %

DN 1 100.0 DN 0 0.0

Other CKD 0 0.0 Other CKD 14 100.0

W = 1.000 W = 14.000

N = 1 N = 14

0 5000 7500 10000 12500

SAMPLE PREPARATION AND MULTIVARIATE ANALYSIS

DATA ACQUISITION

ANALYSIS ®

(BPS )

RCC Vs. Healthy Subjects (URINE)

SELDI MASS SPECTRA

5000 7500 10000 12500

20

uA 15 RCC

10

INTENSITY 5

0

30

20 HS

10

0 MASS m/z (Da)

Fifty-seven mass peaks resulted differentially

excreted (p-value < 0.05) between RCC and healthy

subjects ( mass range: 3000 e 30000 Da )

Classification and Regression Tree (CART)

Analysis

Node 1

Class = RCC M0_Mx

C04128_0 <= 14.796

Class Cases %

METHOD

CTRL 12 46.2

RCC M0_Mx 14 53.8

W = 26.000

N = 26

SELDI dataset analysis by multivariate classification software may provide rapid,

simplified pattern analysis for discovery of multiple biomarkers

C04128_0 <= 14.796 C04128_0 > 14.796

Terminal Node 2

Node 1 Class = RCC M0_Mx

Class = CTRL C03736_9 <= 4.991

Class Cases % Class Cases %

CTRL 11 91.7 CTRL 1 7.1

Basic principle of CART (Classification and Regression Trees) analysis

RCC M0_Mx 1 8.3 RCC M0_Mx 13 92.9

W = 12.000 W = 14.000

N = 12 N = 14

peak intensity values are used to define a single splitting rule that best

•SELDI C03736_9 <= 4.991 C03736_9 > 4.991

segregates the training set by phenotype. Terminal Terminal

Node 2 Node 3

operation is repeated on each peak to produce a decision tree describing the best

•The Class = CTRL Class = RCC M0_Mx

Class Cases % Class Cases %

set of rules for organizing the samples according to phenotype.

CTRL 1 100.0 CTRL 0 0.0

RCC M0_Mx 0 0.0 RCC M0_Mx 13 100.0

W = 1.000 W = 13.000

N = 1 N = 13

ARE THERE COMPARABLE REDUCED OR

INCREASED RCC MARKERS IN KIDNEY AND URINE?

RCC Vs “normal” tissue RCC Vs HS urine

Differentially

Differentially expressed mass peaks

expressed mass peaks

Common markers

(confident RCC biomarkers )

Differently expressed urine biomarkers in

tissue profiling

3600 3700 3800 3900 8500 8750 9000 9250 8500 8750 9000 9250

3600 3700 3800 3900

500 200

400 150

300

uA uA 100

200 50

100 0

0

500 200

400 150

300

uA uA 100

200 50

100 HS

0 0

500 200

400

300 150

uA uA

tissue 200 100

100 50

0 0

500

400 200

300 150

uA uA

200 100

100 50

0 0

500

400 200

300 150

uA uA

200 100

100 50

0 0

500

400 200

300 150

uA uA

200 100

100 RCC

50

0 0

500

400 200

300 150

uA uA

200 100

100 50

0 0

500

400 200

300 150

uA uA

200 100

100 50

0 0

3600 3700 3800 3900 8500 8750 9000 9250 8500 8750 9000 9250

3600 3700 3800 3900

3600 3800 8500 9000 8500 9000

3600 3800 125

80 100

60 uA 75

uA 40 50

25

20 0

0

80 125

100

60 uA 75

uA 40 50 HS

20 25

Urine 0

0

80 125

100

60 uA 75

uA 40 50

20 25

0

0

80 125

100

60 uA 75

uA 40 50

20 25

0

0

80 125

60 100

uA 75

uA 40 50

20 25

0 0

80 125

60 100

uA

uA 75

40 50

20 RCC

25

0 0

80 125

60 100

uA

uA 75

40 50

20 25

0 0

80 125

60 100

uA uA 75

40 50

20 25

0 0

3600 3800

3600 3800 8500 9000 8500 9000

3736 m/z 8755 m/z

ANDAMENTO DEL PICCO DI 3736 m/z nelle urine e nei

tessuti RCC

URINE TESSUTO

CTRL RCC Tex CTRL Tex RCC

CART analysis

Classification Tree for RCC and HS (URINE)

Node 1

Class = RCC M0_Mx

C08755_0 <= 9.139

Class Cases %

CTRL 12 46.2

RCC M0_Mx 14 53.8

W = 26.000

N = 26 1.0

C08755_0 <= 9.139 C08755_0 > 9.139 0.8

Node 2 Node 3

Class = CTRL Class = RCC M0_Mx

C03736_9 <= 25.320 C03736_9 <= 7.575 Sensitivity

Class Cases % Class Cases % 0.6

CTRL 10 76.9 CTRL 2 15.4

RCC M0_Mx 3 23.1 RCC M0_Mx 11 84.6

W = 13.000 W = 13.000 0.4

N = 13 N = 13 ROC Integral

0.764

C03736_9 <= 25.320 C03736_9 > 25.320 C03736_9 <= 7.575 C03736_9 > 7.575 0.2

Terminal Terminal Terminal Terminal

Node 1 Node 2 Node 3 Node 4 0.0

Class = CTRL Class = RCC M0_Mx Class = CTRL Class = RCC M0_Mx

Class Cases % Class Cases % Class Cases % Class Cases % 0.0 0.2 0.4 0.6 0.8 1.0

CTRL 10 83.3 CTRL 0 0.0 CTRL 1 50.0 CTRL 1 9.1

RCC M0_Mx 2 16.7 RCC M0_Mx 1 100.0 RCC M0_Mx 1 50.0 RCC M0_Mx 10 90.9 1 - Specif icity

W = 12.000 W = 1.000 W = 2.000 W = 11.000

N = 12 N = 1 N = 2 N = 11

Prediction Success for CTRL (testing set)

Actual Total Percent CTRL RCC

Class Cases Correct N=8 M0_Mx

N=9

CTRL 9 77.778 7 2

RCC 8 87.500 1 7

M0_Mx

TISSUE LOCALIZATION BY MALDI IMAGING

CTRL 1 CTRL 2 CTRL 1 CTRL 2

m/z m/z

3736 8755

RCC 1 RCC 2

RCC 2 RCC 1

MALDI/TOF/MS/MS

Autofllex III Smartbeam

These RCC-derived urine biomarkers were further used

to classify tissues…….

Adapted from Schwamborn K, et al. Nat Rev Cancer, 10 (9): 640

CLASSIFICATION TREE FOR RCC

(TISSUE)

Node 1

Class = Tex RCC ROC for Class Tex RCC

C03736_9 <= 143.699

Class Cases %

Tex CTRL 10 47.6

Tex RCC 11 52.4

W = 21.000

N = 21 1.0

C03736_9 <= 143.699 C03736_9 > 143.699

Node 2 Terminal

Class = Tex CTRL Node 4

C03736_9 <= 96.502 Class = Tex RCC 0.8

Class Cases % Class Cases %

Tex CTRL 10 58.8 Tex CTRL 0 0.0

Tex RCC 7 41.2 Tex RCC 4 100.0

W = 17.000 W = 4.000 Sensitivity

N = 17 N = 4 0.6

C03736_9 <= 96.502 C03736_9 > 96.502

Node 3 Terminal

Class = Tex RCC Node 3 0.4

C08755_0 <= 33.855 Class = Tex CTRL

Class Cases % Class Cases % ROC Integral

Tex CTRL 7 50.0 Tex CTRL 3 100.0

Tex RCC 7 50.0 Tex RCC 0 0.0

W = 14.000 W = 3.000 0.905

N = 14 N = 3 0.2

C08755_0 <= 33.855 C08755_0 > 33.855

Terminal Terminal 0.0

Node 1 Node 2

Class = Tex CTRL Class = Tex RCC

Class Cases % Class Cases % 0.0 0.2 0.4 0.6 0.8 1.0

Tex CTRL 6 66.7 Tex CTRL 1 20.0

Tex RCC 3 33.3 Tex RCC 4 80.0

W = 9.000 W = 5.000 1 - Specif icity

N = 9 N = 5 Prediction Success

Actual Total Percent Tex CTRL Tex RCC

Class Cases Correct N=9 N=11

Tex CTRL 10 80.000 8 2

Tex RCC 10 90.000 1 9

VALIDATION OF RCC BIOMARKERS (SERUM)

Node 1

Class = RCC Mo_Mx 1.0

C03736_9 <= 2.137

Class Cases %

CTRL 15 50.0

RCC Mo_Mx 15 50.0 0.8

W = 30.000

N = 30 ity

C03736_9 <= 2.137 C03736_9 > 2.137 0.6

iv

Node 2 Terminal it

Class = CTRL Node 3 Sens

C08755_0 <= 75.276 Class = RCC Mo_Mx Prediction Success

Class Cases % Class Cases % 0.4

CTRL 14 93.3 CTRL 1 6.7 ROC Integral

RCC Mo_Mx 1 6.7 RCC Mo_Mx 14 93.3 Actual Total Percent CTRL RCC

W = 15.000 W = 15.000

N = 15 N = 15 0.969

0.2 Class Cases Correct N=14 Mo_Mx

C08755_0 <= 75.276 C08755_0 > 75.276 N=16

Terminal Terminal 0.0

Node 1 Node 2 CTRL 15 93.333 14 1

Class = CTRL Class = RCC Mo_Mx 0.0 0.2 0.4 0.6 0.8 1.0

Class Cases % Class Cases % RCC 15 100.000 0 15

CTRL 14 100.0 CTRL 0 0.0

RCC Mo_Mx 0 0.0 RCC Mo_Mx 1 100.0 1 - Specif icity

W = 14.000 W = 1.000 Mo_Mx

N = 14 N = 1

P <0.01

(μA)

Intensity P <0.05 N.S.

P <0.001 P <0.001

P <0.001

3736 m/z 8755 m/z

SPECIFICITY OF RCC URINE PROFILE

7250 7500 7750 8000 7250 7500 7750 8000

Node 1

Class = non-RCC 60

C07660_2 <= 5.449 40

uA

Class Cases % 20

RCC M0_Mx 22 66.7 7651.09

0

non-RCC 11 33.3 60

W = 33.000 40

uA

N = 33 20 7653.81

0

60

C07660_2 <= 5.449 C07660_2 > 5.449 Non-RCC

40

uA

Node 2 Terminal 20

Class = non-RCC Node 3 7651.53

0

C05370_9 <= 6.955 Class = RCC M0_Mx 60

Class Cases % Class Cases % 40

uA

RCC M0_Mx 5 33.3 RCC M0_Mx 17 94.4 20

non-RCC 10 66.7 non-RCC 1 5.6 7652.36

W = 15.000 W = 18.000 0

60

N = 15 N = 18 7656.84

40

uA 20 7656.84

C05370_9 <= 6.955 C05370_9 > 6.955 0

60

Terminal Terminal 7654.61

40

uA

Node 1 Node 2 20

Class = RCC M0_Mx Class = non-RCC 7654.61 RCC

Class Cases % Class Cases % 0 7656.95

60

RCC M0_Mx 4 100.0 RCC M0_Mx 1 9.1

non-RCC 0 0.0 non-RCC 10 90.9 40

uA

W = 4.000 W = 11.000 20 7656.95

N = 4 N = 11 0

60

40

uA 7659.20

20 7659.20

0 7250 7500 7750 8000 7250 7500 7750 8000

Concluding remarks

• analysis of urine, sera and tissues allowed to

Cross-comparative

identify RCC-derived biomarkers in both serum and urine of the affected

patients

• putative biomarkers showed increased levels and statistical

The

significance in urine then in serum, thus urine would represent the most

appropriate biological fluid for for future validation studies

• Although these markers were not specific for RCC, urine proteome of

RCC patients showed a cluster of mass peaks (7.000-8.000 m/z range)

able to efficiently distinguish RCC form non-RCC tumors

• going MS identification of these candidate biomarkers and their

On

further validation in larger cohort of RCC patients could contribute to

set-up new diagnostic kit for early diagnosis of Clear Cell Carcinoma

DIAGNOSI DIFFERENZIALE

DELLA NEFROPATIA

DIABETICA


PAGINE

132

PESO

11.97 MB

AUTORE

kalamaj

PUBBLICATO

+1 anno fa


DETTAGLI
Corso di laurea: Corso di laurea magistrale in medicina e chirurgia (a ciclo unico - 6 anni)
SSD:
Università: Foggia - Unifg
A.A.: 2012-2013

I contenuti di questa pagina costituiscono rielaborazioni personali del Publisher kalamaj di informazioni apprese con la frequenza delle lezioni di Medicina di Laboratorio - Patologia Clinica e studio autonomo di eventuali libri di riferimento in preparazione dell'esame finale o della tesi. Non devono intendersi come materiale ufficiale dell'università Foggia - Unifg o del prof Ranieri Elena.

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