View
8
Download
0
Category
Preview:
Citation preview
Precision Medicine / Personalized Medicine
Visioner, muligheder, barrierer
Professor, consultant, Research directorIvan Brandslund
14. Marts 2017
2
County Mayor E. Thiedemann, Denmark
”I finally understand the human genome:
To have it for the single person can be compared to having a list of telephone numbers for all the inhabitants on the globe…
…but if you don’t know their names, occupation, address or anything else about them it’s of no use.”
3
Kerneydelser
1. Diagnosticer sygdommen med bedste metoder
2. Behandl patienten efter højeste vidensniveau
3. Giv bedste pleje og trøst
Krav til pkt. 1
1. Sensitivitet
2. Specificitet
3. Positiv prediktiv værdi
4. Negativ prediktiv værdi
4
To use and practice precision medicine a very robust connection is needed between
Genomic sequenceBiomarker
Drug TreatmentPreventive intervention
Disease
Many diseases are unknown in ethiology. Many polymorphisms are unknown in importance. Many drugs fail or need dose adjustment based on mutational differences.
The 3 principles of PM
5
Such considerations have delayed a general acceptance of financing large scale sequencing projects.
A government report instead proposed smaller targeted research projects.
To enable these:
National Danish Bio – and Genebanks are organized for
CancerDiabetesRheumatologyEtc.
6
Precision Medicine is a
Hope DreamVision
for a compass direction of future research
to link
variation in genes, protein functions, metabolites
with
environmental conditions and lifestyles
to
enable individualized prevention and treatment
7
Personalized Medicine links
Precision Medicines 3 principles
with
patients own preferences
from
knowledge on effects and side-effects of treatment / drug in exactly this patient.
8
Precision Medicine and Targeted Treatment in Personalized Medicine
The Right Diagnosis Lab. Tests
The Right Person Genetics / biomarkers
The Right Drug PGx
The Right Time Natural history of disease
The Right Dose PGX and TDM
9
Old examples of PM / PM / TT
Diabetes 1
Diagnostics: Glucose, HbA1c, C-peptide, S-Insulin
Mechanisms known: Low / no Insulin production
Etiology: Unknown, GAD65 pos
Targeted treatment: Insulin
Companion DX: Insulin Ab?
Monitoring: Glucose, HbA1c
10
Old examples of PM / PM / TT
Blood Transfusion
Diagnostics: Low Hb
Genome: Blood Type
Companion DX : Donor blood typing, computer match
Targeted treatment: Transfusion
Effect / drug monitoring: B-Hb
Side-effects: Incompatibility, Type II – III allergic reaction, hemolysis, renal impairment
11
Precision / Personalized Medicine: dream/vision
1. A protein causing a disease is identified in a person (eg. BCR-ABL in CML).
2. The protein and the gene sequence is determined in the disease, CML.
3. A company succeeds in producing a drug, a small chemical molecule, that blocks the protein.
4. Gleevec!
5. Variations in protein amino acid gene sequence is observed to reduce effect of drug.
6. Produce another drug for this patient.
12
CML is Treated with a Tyrosinekinase Inhibitor
BCR-ABL fusionprotein blocked by Gleevec
13
BMS354825 response on BCR-ABL mutant isoforms
Shah et al. SCIENCE (2004) 305 p399
14
PM / PM
Personalized Medicine in its extreme meaning is creation of a tailored drug that targets exactly this – and other patients’ problem, provided the exact same gene/protein-sequence and function is present.
Expensive
The reality is that gene variations in the target protein can partition patients in smaller groups, that differs in their
• risk of getting a disease
• risk of treatment failure
• risk of adverse reactions
• probability for cure or relief of symptoms
Treat those who will benefit. Do not treat those that will not.
Serum HER-2 determinations for personalized care of breast cancer patients
Ivan Brandslund,
Professor MD, DMSc
University of Southern Denmark, Department of Clinical Biochemistry,
Vejle Hospital, Denmark
Rome, 2016
16
HER-2
CD340 Neu ErbB-2 HER 2
Cluster of Differentiation
Neuro/glioblastoma
Erythroblastic leukemia viraloncogene
Human Epidermal growth Receptor
chromosome 17
17
HER - family
1978 EGFR = HER 1 by Cohen
1982-84 HER2 by Weinberg at MIT
1984-86 Cloned by Ullrich et al
1986 McAb to HER2 produced
1987 Oncogene amplified in some cancers (Slamon)
1987 HER2 amplification: Shorter survival, aggressive disease
1988 Transfection with the gene cancer in mouse mammary tissue
1989 McAb against HER2 inhibits cell growth
1991 Herceptin produced, tested
1995 Phase 3 trial started
1996 The Dako Herceptest
1998 Herceptin approved: First antibody for targeted therapy in cancer
2003 Herceptest CE approved
2008 Lapatinib synergy with Herceptin
18
The Epidermal Growth Factor Signalling
19
Effect of HER-2 amplification on human breast cancer cells
Human breast cancer cells
Transfect with
HER2 gene
DNA synthesis 50–75%
Cell growth rate 30–50%
Growth in soft agar 225%
Tumourigenicity in nude mice
Metastatic potential 220%in nude mice
Transformedphenotype
HER2–ve
HER2+ve
MCF-7 MCF-7
20
The central dogme
DNA RNA Protein Function
Substrate Product
21
Cancer tissue Autologous
reference tissueNormal tissue
.1
1
10
100
1000
10000H
ER
2 n
g/m
g p
rote
in
(Centa
ur)
22
Indicators of increased HER-2 production
1 = gene copy number
2 = mRNA transcription
3 = cell surface receptor protein expression
4 = release of receptor extracellular domain
Normal Amplification/Overexpression
Cytoplasm
HER2 receptorprotein
Cytoplasmicmembrane
Nucleus
HER2 DNA
HER2mRNA
1
2
3
4
23
Positive or negative HER-2 status
IHC Images courtesy of MJ Kornstein, MD, Medical College of Virginia
Abnormal 2+ Abnormal 3+Normal 0
Normal
Normal 1+
Normal Abnormal low
amplification
Abnormal high
amplification
24
Ross JS, Fletcher JA. Stem Cells 1998; 16: 413–428
Survival of node-negative breast cancer patients related to HER-2 status
1.00
0.75
0.50
0.25
0Cu
mu
lati
ve p
rob
abili
ty
0 24 48 72 96 120 144
Not amplified
Amplified
Amplified: >10 copies/nucleusNot amplified: <3 copies/nucleus
Borderline: excluded
Time to death (months)
Log rank p<0.001
25
0.00
0.25
0.50
0.75
1.00
0 20 40 60 80
Dis
ease f
ree s
urv
ival
Follow-up, months
BTC and HB-EGF in cancer tissue
BTC< 4.1 and HB-EGF<28.5
(11/74)
BTC>=4.1 and HB-EGF>=28.5
(31/76)
0.00
0.25
0.50
0.75
1.00
0 20 40 60 80
Dis
ease f
ree s
urv
ival
Follow-up, months
p=0.0006
Study on value of S-HER2 / HER2 of DNA in breast cancer
• S-HER2 was measured in 862 patients every 3–12 months, HER2 of DNA in selected.
• Tissue HER-2 status was determined by IHC and FISH
• Metastases were diagnosed according to the routine clinical methods using imaging/biopsy
26
27
0
200
400
600
800
1000
1200
1400
Se
rum
HE
R-2
(n
g/m
l)
Withouth relapse/progression With relapse/progression
Maximum serum HER-2 values in 35 patients who were serum HER-2 positive, tissue HER-2 positive
patients without (n=10) or with relapse/progression (n=25). Three tissue positive serum HER-2 values
between 3300 ng/mL and 14,000 ng/mL are not shown (p<0.00003).
28
S-HER2 before metastasis detection in tissue-positive patients using different cutoffs
Cutoff value 15µg/L Cutoff value 15µg/L + a
delta value of > 100 %
increase from individual
baseline after primary
therapy
Cutoff value 32 µg/L
Sensitivity 69 % (53-80 %) 50 % (35-64 %) 47 % (33-62 %)
Specificity 71 % (62-78 %) 96 % (91-98 %) 96 % (91-98 %)
Positive predictive value 47 % (35-59 %) 84 % (65-93 %) 83 % (64-93 %)
Negative predictive
value86 % (77-91 %) 83 % (76-89 %) 83 % (75-88 %)
29
(CI 95 %)
Probabilities for metastatic recurrence in relation to S-HER2 value
S-HER2 values taken at the time of confirmed metastatic recurrence by CT/US/MR in patients with recurrence. For patients without recurrence, the highest S-HER2 for each patient was used.
30
31
Summary
Tissue neg. pts.: PPV at 15 ng/ml 42%
PPV at 25 µg/L 100%
Tissue pos. pts.: PPV at 15 ng/ml 71%
PPV at 80 µg/L 100%
Lead-time between S-HER2 increase and the clinical
diagnosis of symptomatic metastasis (at cutoff 15 µg/L)
• 0-38 months
• Median 4 months32
• A prospective follow-up study on Herceptin treatment of BC
• Followed for up to 6 years or until death (2004-2011)
• S-HER2 and S-Trastuzumab was measured at clinically determined intervals of between 3 weeks and 12 months
• Patients were followed routinely for relapse by physical examination. If symptoms of relapse CT/MR/US was performed
33
Dot plot showing delta S-HER2% values in patient events with no progression and with progression, respectively.29 patients (27 patients in the progression group and two patients in the no progression group) with a change > 100% are not depicted in the figure.
34
27 patients
2 patients
S-HER2 ’s ability by increase to predict progression
An increase in S-HER2 of ≥20% was correlated to progression in the disease in 40 out of 44 clinical courses (p < 0.0001).
35
Events with progression Events with no
progression
≥20 % increase in S-HER2 40 4
Not ≥20 % increase in S-HER2 9 32
Sensitivity 82% (68% – 91%)
Specificity 89% (74% – 97%)
PPV 91% (78% – 97%)
NPV 78% (62% – 89%)
(95% CI)
Sensitivity 56% (38% – 72%)
Specificity 98% (89% – 100%)
PPV 95% (76% – 100%)
NPV 75% (63% – 85%)
(95% CI)
A decrease in S-HER2 of ≥20% was correlated to no progression in the disease in 20 out of 21 clinical courses (p < 0.0001).
36
Events with progression Events with no progression
≥20 % decrease in S-HER2 1 20
Not ≥20 % decrease in S-HER2 48 16
S-HER2 ’s ability by decrease to predict response to treatment
37
No. 39
0
100
200
300
400
500
600
700
27-01-09 16-02-09 08-03-09 28-03-09 17-04-09 07-05-09 27-05-09
Date of sample
S-H
ER
2 (
ng
/ml)
0
100
200
300
400
500
600
S-H
erc
ep
tin
(n
g/m
l)
No 39 January 2009 mastitis carcinomatosis, ER and PR negative. Neoadjuvant therapy Herceptin/Taxol 1 serie 05.02.09, because of reduced Muqa continues only Taxol with good effect but operation not possible, continues Taxol. December 2009 progression, treatment changes to Faslodex.
38
No. 67
0
10
20
30
40
50
05-09-
05
24-03-
06
10-10-
06
28-04-
07
14-11-
07
01-06-
08
18-12-
08
06-07-
09
Date of sample
S-H
ER
2 (
ng
/ml)
0
100
200
300
400
500
600
S-H
erc
ep
tin
(n
g/m
l)
No 67
Surgery 25.04.06 after neoadjuvant EC and Iressa (02.02.06 – 06.04.06). 19.05.06 local relapse. Herceptin/Taxotere(24.05.06 – 06.09.06 ). July 2006 complete remission of local relapse. Local radiotherapy. Herceptin monotherapy (27.09.06 – 25.04.07). Currently without relapse.
A typical course for a patient with an initially good response but subsequent lack of response to trastuzumab treatment is that S-HER2 remains at the normal level during the first trastuzumabtreatment period and then increases prior to the second trastuzumab treatment period. S-HER2 decreases markedly and rapidly during the second trastuzumab treatment period.Eventually S-HER2 continues to increase despite therapy and the patients die.
39
1
10
100
1000
10000
Group 1
Free of recurrence
Group 2
Recurrence alive
Group 3
Recurrence dead
S-H
ER
2
1
10
100
1000
10000
Group 1
Free of recurrence
Group 2
Recurrence alive
Group 3
Recurrence dead
S-H
ER
2
N=18 N=13 N=17
41
Cancertype Gene/protein Analysis Drug No tests per year
Lungcancer EFGRC-METALK
DNA mutationMutationExpression
Gefitinib, Erlotinib 500
Breastcancer HER-2
HER-2BRCA1BRCA2
IHC, FISH, amplification
S-HER2
Mutation
Trastuzumab, Lapatinib
Trastuzumab, Lapatinib
PARB-inh
400
3.000
50
Colorectal cancer EGFRKRAS
Mutation Cetuximab 150
Ovary cancer HER-2EFGRVEGFVEGGF-R
Mutation div. 400
Prostatecancer BRCA1 og 2 Mutation PARP-inh 100
Melanoma BRAF Mutation Vemura Fenib
Leukemia ABL/BCR Translocation PCR Imatinib 50
Myelomatosis FGFR3 Translocation mutation Div.Daratumumab
100
Molecular test repertoire in cancer at Lillebaelt Hospital, Vejle, Denmark
42
2011
CA125 Ovarycancer 2.000
PSA Prostatecancer 20.000
YKL-40 Lungcancer 1.500
HCG Mola/testescancer 2.000
AFP Liver/testes cancer 2.000
CEA Coloncancer 1.000
DNA-methylation 4.000
Mikro RNA-analyser 4.000
Pattern – profiles/arrays 1.000
GWA 100
M-komponent screen/diagnostics 10.000
S-light chains / myleolomatosis 6.000
Min. Resid disease monitor, specific heavy chain 300
CYP-45 <10
YKL-40 + K-RAS effect of Cetuximab 1.000
Other tests and cancersERBB2, FGFR1, FGFR2, PDGFRA, PDGFRB, ALK, IGF1R, c-KIT, FLT3, RET, JAK2, SRC, Aurora A and B kinases, Polo-like kinases, MTOR, PI3K
43
44
Future of Personalized medicine
45
CPR (civil registration number)
Every person In Denmark has one
and all information is tied to this
number.
Danish health IT-map
CPR (civil registration number)
Every person In Denmark has one
and all information is tied to this
number.
Danish health IT-map
FMK (common prescription system)
Cosmic (regional medical records)
GP-medical records
CPR (civil registration number)
Every person In Denmark has one
and all information is tied to this
number.
Danish health IT-map – Prescription National Database
FMK (common prescription system)
CPR (civil registration number)
Every person In Denmark has one
and all information is tied to this
number.
Danish health IT-map
FMK (common prescription system)
Cosmic (regional medical records)
GP-medical records
BCC (Clinical Chemistry database)
Danish health IT-map
FMK (common prescription system) BCC (Clinical Chemistry database)
CSO/AC(Anticoagulation)
CPR (civil registration number)
Every person In Denmark has one
and all information is tied to this
number.
Sundhed.dk (health.dk)
Nemid (Easy ID)
a common secure logon system for self service
Cosmic (regional medical records)
GP-medical records
Laboratory
Danish health IT-map
FMK (common prescription system)
Cosmic (regional medical records)
GP-medical records
BCC (Clinical Chemistry database)
Sundhed.dk (health.dk)
CPR (civil registration number)
Every person In Denmark has one
and all information is tied to this
number.
Laboratory
Reimbursement
by the stok
Pharmacy
Drug
Danish health IT-map
FMK (common prescription system) BCC (Clinical Chemistry database)
CSO/AC(Anticoagulation)
PGx-
hospitalCosmic (regional medical records)
GP-medical records
CPR (civil registration number)
Every person In Denmark has one
and all information is tied to this
number.
CPR (civil registration number)
Every person In Denmark has one
and all information is tied to this
number.
Sundhed.dk (health.dk)
Nemid (Easy ID)
a common secure logon system for self service
Flow of data till generation of dosage letter
Presentation of patients Patient record
Dosage prescriptionLetter to patient
E-mail correspondence with the patient via the CSO-system
Letter to the patient
Login - Patient Self Testing
58
The history about the Tempus 600 system
Background
Conclusion from engineer students
• Reduction of delivery times (from blood test to answer)
• Optimizing of workflow in the blood test part
- Focus on the values (VA) and the non-value activities.
Venepuncture
VA 3,48 Min.
Non VA 39,06 Min.
Total 42,54 Min.
Transport 16 %
Wait for other tests 74 %
Wait /search (patients) 2 %
Value 8 %
59
Dispatch
60
GLP Conveyer belt
2016 Vejle Hospital
First inn / First out robot
(FIFO)
VS 2016
Morning round, time of sampling and reporting 2012 versus 2015
61
07:45
08:00
08:15
08:30
08:45
09:00
09:15
09:30
09:45
10:00
10:15
10:30
10:45
26 28 30 32 34 36 38 40 42 44 46 48 50
Tim
e
Week no.
2015
2015
2012
2012
62
2012 2015 Gain
Time of sampling mean2 SD min.
8:1218
8:026
1012
Received in Lab min.2 SD min.
3126
1614
1512
Time to report mean2 SD min.
9640
5614
4026
Effect of Tempus System on ToTAT
Morning round
63
64
From patient to waste container- automatized transport- and analyze production platform for (blood)samples –
From patient to waste container- automatized transport- and analyze production platform for (blood)samples –
Effect of Reduced TAT of 1 hour
• 1000 patients per day: 1000 hours less waiting
120 Parking lots
120 m2 waiting rooms
120 chairs
1000 hours less idle time for patients, doctors, nurses
Value? > 100.000 DKKr per day
> 25 Mill DKKr per year
66
GLP/Sysmex preanalytical centrifugation delay 2015
General Coagulation
Centrifugation time, min 10 14
Average time for passage, min.24 30
Minimum, min.Maximum, min. 2 SD, min.
146018
196018
67
3 centrifuges batch–production.
Morning 8-12 h, 300-600 samples per h.
68
CACS
HC Smede, Børkop
2015
Patent
69
Projekt DESERT
AIM Safer, faster and cheaper care of acute admission patients by Hospitals of Region of Southern Denmark
70
Success criteria
Shorter hospital stay
Reduced mortality
Reduces readmission number
Reduces critical outcomes – sepsis
Reduced use of intensive care units
71
Necessities
Necessary laboratory service
Short turnaround time: < 60 min
Large repertoire: 60-70 components
Necessary computer facilities
Transfer of lab. Data for BCC to algorithm analysis system.
72
User presentation
1. Comment on critical lab results
2. Risk assessment of patient
3. Top 3 possible diagnosis and probability
Costs in Region Southern Denmark
50 analyses ekstra per patient:
150 Kr. x 150.000 pts. 23 M.kr./year
SAS platform and program: 5 mill. Kr. 10 years + 1,5 mill. Kr./year 2 M.kr./year
25 M. Kr./year
5 h shorter hosp. stay: 1000 kr. x 150.000 pts. 150 M.kr./year
Cost reduction 125 M.kr./year
73
74
• Risk assessment
• Screen for disease
• Diagnose disease
• Predict effect of treatment
• Select best treatment according to serum analyzes: Theragnostics
• Monitor effect of surgery
• Detect effect of treatment
• Detect side effects of drug treatment
• Adjust treatment dose according to individual test results
• Discontinue treatment / change treatment in case of resistance to drug
75
Economy
Patients sampled per year Costs per year
v. 50 kr./pt.
Vejle Hospital 300.000 2 Million Euro
Vejle County 800.000 5 Million Euro
Denmark 12 Million 70 Million Euro
Europe 650 Million 4 Billion Euro
4 AU G U S T 2 0 1 6 | VO L 5 3 6 | N AT U R E | 4 1
Diabetes articles 1997-2016
Nature Genetics 46: 357-361, 2014
I omfattende genome-wide-associationsstudier Identificeres mutationer i et gen, som danner en zinktransportør i de insulin-producerende betaceller i pancreas
Mutationerne beskytter mod udvikling af type 2 diabetes og zinktransportøren undersøges nu af flere farma-virksomheder, som et muligt mål for ny udvikling af diabetes medicin
Vejle Diabetes Biobank
Nature Genetics 46:295-299, 2014
Igen et eksempel på hvorledes internationale genome-wide-
associationsstudier er I stand til at kortlægge nye områder i
det humane genom, som enten øger eller mindsker risiko for
type 2 diabetes
Vejle Diabetes Biobank
Nature Genetics 48: 1151-1161, 2016
Kortlægning af 30 nye områder i det humane
genom som bidrager til udvikling af
hypertension
Vejle Diabetes Biobank
Diabetes articles 1997-2016
18 SEPTEMBER 2015 • VOL 349 ISSUE 6254
J Med Genet. 2016 Sep;53(9):616-23. doi: 10.1136/jmedgenet-2015-103728. Epub 2016 Apr 11.
Diabetes articles 1997-2016
BMJ Open Diabetes Research and Care 2015;3:e000095. doi:10.1136/bmjdrc-2015-000095
Diabetes articles 1997-2016
PLOS ONE | DOI:10.1371/journal.pone.0120890 March 23, 2015
Diabetes articles 1997-2016
J Clin Endocrinol Metab, April 2015, 100(4):E664–E671
Diabetes articles 1997-2016
October 2014 | Volume 9 | Issue 10 | e109646
Diabetes articles 1997-2016
BMC Endocr Disord. 2014 Aug 28;14:74
Diabetes articles 1997-2016
Diabetes articles 1997-2016
Accepted 27 May 2015
Diabetes articles 1997-2016
Diabetes articles 1997-2016
Rates of Infection in Type 2 Diabetes • CID 2016:63 (15 August) 2016
Diabetes articles 1997-2016
Diabetes articles 1997-2016
Diabetes articles 1997-2016
Diabetes articles 1997-2016
Diabetic Retinopathy
• Most frequent diabetic complication
• Risk increases with the duration of diabetes
• Hypertension, poor glycemic control and elevated serum
lipids are risk factors
Results
P=0.48 P=0.50 P=0.81
”Whole Genome” sekventering Targeteret sekventering
Maturity-Onset Diabetes of the Young (MODY)
X X X X X
MODY1 MODY2
• Mutation in one of 13 know genes.
• Type-2 diabetes diagnosis wrong.
• 5-20 % in patients MODY are found.
Case
• Male 62 years
• BMI: 23
• Type-2 Diabetes diagnosis as 30-years
• PDX1 mutation
99
3 New Steno Diabetes Centers in Denmark
• 1 Billion Euro funding from Novo Nordisk Foundation
• To increase quality of clinical treatment
• Do research in Type 1 and 2 Diabetes
• Introduce personalized medicine in the treatment of the single patient
100
101
Danish Ministry of Health
Mapping of international activities concerning
Personalized Medicine
June, 2016
102
Personalized medicine – improving outcomes
103
The precision-medicine ecosystem
104
Kilde:
https://www.genomicsengland.co.uk/taking-
part/genomic-medicine-centres/
Oversigt over placeringen af de 13 Genomic Medicin Centres
105
Overordnet it-arkitektur for Genomics England og The 100,000 Genomes Project
106
Eksempel på online-kursus ved Genomics Education Programme
107
Forståelsesramme for kortlægning af internationale erfaringer med personlig medicin
108
Fordeling af udvalgte lande
109
Report on Personalized Medicine
October 2016
Suggestions for projects, organization and management
110
Skitse til overordnet arkitektur for genomdata
111
Eksempel på sammenhængen mellem forskning og klinisk praksis ved personlig medicin
112
Udgifter til personlig medicin og stor skala-sekventering –Bemærk illustration af estimater
113
Grove overslag for økonomiske hovedelementer for etablering og drift af national satsning på personlig medicin – Bemærk mulig skalering
114
Opdeling af afledte økonomiske effekter af personlig medicin på kort sigt (fem år)
115
Recommendation in Denmark
1. National strategy for direction and priorities.
2. Justicial/law-based activities. Patient rights, safety, security
3. Education and information to Citizens: Insight and transparency
4. Effects in society: outcomes and economic gain? Health care efficiency? Investments and growth in biotech commercial area?
5. Formal networks and coordination of cooperation.
6. PM and genome data needs more people with many and more competences and educational level
7. Technological and IT infra structure IT standards, registers, databases, architecture and compatibility.
116
Best links:
https://www.nih.gov/precision-medicine-initiative-cohort-program
http://precisionmedicine.ucsf.edu/elements-precision-medicine
https://ghr.nlm.nih.gov/primer/precisionmedicine
Trombose / Trombofili
APTT
Protein C-resistens
F5- Gen Leiden
Protrombin – Gen/DNA
Cøliaki
Autoantistoffer Transglutaminase
Gliadin
+ vævstype
HLA-DQ8 + HLA-DR2
Immunologi? Genetik?
We don’t care
just an analytical test
STAT !
Urin – Flow Cytometry
Metode: Detekterer og kvantitererLeucocytter, Blod, CastsBakterier(samt U-stix analyser)
Outcome: Nedbringer antal dyrkninger i MikrobiologiGør U-stix obsolete
U-dyrk (MADS-flow) U-stix (Biokemi)
Tempus Tempus
Flow (på fælles GLP conveyer belt)
Negative svares i BCC < 10 minPositive dyrkes
Svares i MADS / ROS
120
121
122
123
Dobbeltfunktion / konfliktområder
PatologiMikroskopisk diagnostik på væv diagnostik af cancer
Biokemi analyser på vævMol.biol. analyser på væv
Mol.biol. analyser på blod/plasma
Mol.biol. Og Biokemiske analyser på væv, celler, blod, plasma, urin, fæces m.m.
Biokemi
124
Vejle Hospital
Recommended