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ALAM MEDICAL

 

 

Diagnóstico y Evaluación Arterial: Velocidad de Onda de pulso (VOP) y Presión Central

 

 

Complior Analyse

 

 

 

Velocidad de Onda de Pulso

 

 

 

Presión Central

 

 

 

Protocolos de Medición

 

 

Informaciones Básicas

 

 

 

Conceptos  Básicos de VOP:  Velocidade de Onda de Pulso

 

 

 

Conceptos Básicos de
Presión Central

 

 

 

Protocolo de Medición con Complior Analyse

 

 

 

Validación do Complior Analyse

 

 

 

Algunas Publicaciones

 

 

 

Folletos

 

     
 

COMPUMEDICS

 
 

Equipos de Diagnóstico en las Áreas de Sueño, la Neurología y la Cardiología

 
  Somnea  
  Somt?/a>  
  Somt?PSG  
  E-Series EEG / PSG  
  SynAmps2 Neuvo  
  Siesta  
  Safiro  
  Grael  
  Xegis EMG / PE / IOM  
     
 

NEUROSCAN

 
 

Softwares, Amplificadores
y Accesorios para la Investigación en Neurociencia

 
  Maglink  
  Scan NuAmps Express  
  SynAmps RT  
  Stim2  
  Curry 6  
  Source 2  
  Source 5  
  Scan 4.5  
  Access SDK  
  Quik-Caps  
  QuikCell  
       
 

MEDOC

 
 

Sistemas de Neuro Diagnóstico para Pesquisa
da Dor Cônica e Aguda

 
  AlgoMed  
  Covas  
  Pathway  
  Pathway Modelo ATS  
  Pathway Modelo ATS
Accessories
 
  Pathway Modelo Cheps  
  Pathway Modelo Cheps Acessories  
  TSA II Neuro Sensory
Analyzer
 
  TSA II
Accessories
 
  VSA-3000 - Vibratory
Sensory Analyzer
 
       
 

ALPHA OMEGA

 
 

Equipos para investigación neurofisiológica en animales

 
 

Registro y Estimulación  
   

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AlphaLab SnR  
   

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TeleSpike  
   

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TeleSpike Mini  
 

Micromanipuladores  
   

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EPS  
   

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FlexMT  
   

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MultiDrive  
 

In Vitro MEA  
   

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Array de
Microelectrodo In Vitro
 
 

Accesorios Neurociencia  
   

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Electrodos  
   

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MEA - Array de Microelectrodo  
   

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FMA - Array de Microelectrodo Flotante  
   

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LMA - Array de
Microelectrodo Linear
 
   

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WMA - Microwire Array  
   

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Electrodo de Inyección  
       
 

UGO BASILE

 
 

Instrumentos para
Animales de Laboratorio

 
  Dolor e Inflamación  
  Coordinación Motora, Actividad y Fuerza de Agarre  
  Ventilación y Gases Anestésicos  
  Condicionamiento y Recompensa  
  Laberintos y Monitoreo  
 

Baño de Órganos, Grabación y Estimuladores  
  ECT, Productor de Lesiones, Diversos  
  Presión Sanguínea y Funciones Vitales  
  Metabolismo y Comportamiento Alimentario  
  Fijación por Irradiación
de Microondas
 
       
     XLTEK  
 

Equipos de
neurodiagnóstico:
EMG, PE y IOM

 
 

NeuroMax EMG / PE  
 

Protektor IOM  
 

XCalibur EMG / PE  
       
     CAREFUSION  
 

Equipos de
neurodiagnóstico:
EEG, MG, PE y IOM

 
 

Endeavor CR IOM  
 

Synergy N-EP - EMG / PE  
 

Synergy N2 - EMG  
 

Synergy T2 - EMG  
 

Synergy T-EP - EMG / PE  
 

NicoletOne  
 

-
-
-
-
-
-

Ambulatory EEG
cEEG
nEEG
LTM
Sleep
vEEG
 
 

VikingQuest IOM  
 

VikingQuest EMG  
 

VikingSelect  
 

VikingSelect IOM  
       
 

BIOLOG

 
 

Sistema de Identificación Microbiana

 
 

GEN III
Identificación
Microbiana
 
 

OmniLog
Incubación de Placas
 
 

EcoPlate
Análisis de Comunidades Microbianas
 
 

FF MicroPlate
Identificación de Hongos Filamentosos y Levaduras
 
 

Rainbow Agar
Detección de

E. coli O157
 
 

Rainbow Agar
Detección de
 H2S Salmonella
 
 

Rainbow Agar
Detección de

Shingella / Aeromonas
 
       
 

AEROCRINE - BIOSCAN

 
 

Medidores Portátil de
Óxido Nítrico (FENO)

 
 

Niox Mino  
       
 

SLEEP SUPPORT

 
 

Sensores e electrodos para EEG, PSG, PE, EMG y ECG

 
 

Cinturones de Esfuerzo Respiratorio, Torácicos
y Abdominales

 
 

Sensores de Flujo Respiratorio Buconasal  
 

Transductor de Presión Flujo Respiratorio y Ronquido por Cánula  
 

Sensores de Ronquido Micrófono  
 

Electrodo de Superficie de Copa Orificada Reutilizable
Sueño - EEG - Mapeo

 
       

       

 

 

 

 

EcoPlate?- Biolog

 

 

Microbial Community Analysis

 

 

 

 

 

 

 

 

Introduction

 

 

Microbial communities provide useful data for studying both applied and Basic environmental events. Microorganisms are present in virtually all environments and are typically the first organisms to react to chemical and physical changes in the environment. Because they are at the bottom of the food chain, changes in microbial communities are often a precursor to changes in the health and viability of the environment as a whole.

 

 

The Biolog EcoPlate?(Figure 1) was created specifically for community analysis and microbial ecological studies. It was originally designed at the request of a group of microbiol ecologists that wanted more replicates than the Biolog GN MicroPlate?provided.

 

 

Community analysis using Biolog MicroPlates was originally described in 1991 by J. Garland and A. Mills.1 Researchers found that by inoculating Biolog GN MicroPlates with a mixed culture of microorganisms and measuring the community fingerprint over time, they could ascertain characteristics about that community of microbes. This approach called community–level physiological profiling has been demonstrated to be effective at distinguishing spatial and temporal changes in microbial communities. In applied ecological research, the MicroPlates are used as both an assay of the stability of a normal population and to detect and assess changes based upon the variable introduced.

 

 

Studies have been done in all areas of environmental science and have demonstrated the fundamental utility of Biolog MicroPlates for this application. Studies demonstrating the utility of Biolog MicroPlates in detecting population change have been done in soil, water, wastewater, activated sludge, compost, and industrial waste. The utility of the information has been documented in over 500 publications using Biolog technology to analyze microbial communities. A bibliography of publications is posted on the Biolog website (www.biolog.com).

 

 

 

A1

A2

A3

A4

A1

A2

A3

A4

A1

A2

A3

A4

Water

β-Methyl-D
Glucoside

D-
Galactonic
Acid

γ
-Lactone

L-Arginine

Water

β-Methyl-D
Glucoside

D-Galactonic
Acid

γ
-Lactone

L-Arginine

Water

β-Methyl-D
Glucoside

D-Galactonic
Acid

γ
-Lactone

L-Arginine

B1

B2

B3

B4

B1

B2

B3

B4

B1

B2

B3

B4

Pyruvic Acid
Methyl Ester

D-Xylose

D-Galacturonic
Acid

L-
Asparagine

Pyruvic Acid
Methyl Ester

D-Xylose

D-Galacturonic
Acid

L-Asparagine

Pyruvic Acid
Methyl Ester

D-Xylose

D-Galacturonic
Acid

L-Asparagine

C1

C2

C3

C4

C1

C2

C3

C4

C1

C2

C3

C4

Tween 40

i-
Erythritol

2-
Hydroxy
Benzoic Acid

L-Phenylalanine

Tween 40

i-
Erythritol

2-
Hydroxy
Benzoic Acid

L-Phenylalanine

Tween 40

i-
Erythritol

2-
Hydroxy
Benzoic Acid

L-Phenylalanine

D1

D2

D3

D4

D1

D2

D3

D4

D1

D2

D3

D4

Tween 80

D-Mannitol

4-
Hydroxy
Benzoic Acid

L-Serine

Tween 80

D-Mannitol

4-
Hydroxy
Benzoic Acid

L-Serine

Tween 80

D-Mannitol

4-
Hydroxy
Benzoic Acid

L-Serine

F1

F2

F3

F4

F1

F2

F3

F4

F1

F2

F3

F4

Glycogen

D-
Glucosaminic
Acid

Itaconic Acid

Glycyl-L- Glutamic
Acid

Glycogen

D-
Glucosaminic
Acid

Itaconic Acid

Glycyl-L- Glutamic
Acid

Glycogen

D-
Glucosaminic
Acid

Itaconic Acid

Glycyl-L- Glutamic
Acid

G1

G2

G3

G4

G1

G2

G3

G4

G1

G2

G3

G4

D-Cellobiose

Glucose-1-
Phosphate

α-Ketobutyric
Acid

Phenylethylamine

D-Cellobiose

Glucose-1-
Phosphate

α-Ketobutyric
Acid

Phenylethylamine

D-Cellobiose

Glucose-1-
Phosphate

α-Ketobutyric
Acid

Phenylethylamine

H1

H2

H3

H4

H1

H2

H3

H4

H1

H2

H3

H4

α-D-Lactose

D,L-α-Glycerol
Phosphate

D-Malic Acid

Putrescine

α-D-Lactose

D,L-α-Glycerol
Phosphate

D-Malic Acid

Putrescine

α-D-Lactose

D,L-α-Glycerol
Phosphate

D-Malic Acid

Putrescine

Figure 1. Carbon Sources in EcoPlate

 

 

 

 

 

 

 

EcoPlate?- Biolog

 

 

The Biolog EcoPlate contains 31 of the most useful carbon sources for soil community analysis. These 31 carbon sources are repeated 3 times to give the scientist more replicates of the data. Communities of organisms will give a characteristic reaction pattern called a metabolic fingerprint. These fingerprint reaction patterns rapidly and easily provide a vast amount of information from a single Biolog MicroPlate.

 

 

The community reaction patterns are typically analyzed at defined time intervals over 2 to 5 days. The changes in the pattern are compared and analyzed using statistical analysis software. The most popular method of analysis of the data is via Principle Components Analysis (PCA) of average well color development (AWCD) data, but alternative methods may also offer advantages2−7. The changes observed in the fingerprint pattern provide key data about the microbial population changes over time.

 

 

Biolog MicroPlates have been compared to other methods, such as phospholipid fatty-acid analysis, for monitoring community and ecological changes. The MicroPlates were found to be more sensitive to changes in the environment8. Biolog MicroPlates were also indicated as more sensitive to changes in major determinants such as temperature and water. Similar analyses have been performed using the Biolog GN and GN2 MicroPlates. For some applications these MicroPlates may be preferable to the EcoPlate. The individual application will dictate which MicroPlate is best suited for the community or ecological analysis.

 

 

 

 

 

Typical Procedure 2

 

 

Step 1: Environmental samples are inoculated directly into Biolog MicroPlates either as aqueous samples or after suspension (soil, sludge, sediment, etc?.

 

 

Step 2: The Biolog MicroPlates are incubated and analyzed at defined time intervals.

 

 

Step 3: The community-level physiological profile is assessed for key characteristics:

 

 

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Pattern development (similarity)

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Rate of color change in each well

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Richness of well response (diversity)

 

 

Formation of purple color occurs when the microbes can utilize the carbon source and begin to respire. The respiration of the cells in the community reduces a tetrazolium dye that is included with the carbon source.

 

 

The reaction patterns are most effectively analyzed with a microplate reader, using the Biolog MicroLog?E or MicroStation?Systems. However, any good microplate reader can be used to provide optical density (OD590) values.

 

 

Statistical analysis of the data is typically performed using standard software packages. Some researchers have found that PCA provides greater resolution than other methods of statistical analysis9.

 

 

 

 

 

References

 

 

[1]

 

Classification and characterization of heterotrophic microbial communities on the basis of patterns of community level sole-carbon-source utilization. J.L. Garland, A.L. Mills, Applied and Environmental Microbiology, 1991, v.57, p. 2351-2359.

[2]

Analysis and interpretation of community-level physiological profiles in microbial ecology. J.L. Garland, Federation of European Microbiological Societies, Microbiology Ecology, 1997, v. 24, p289- 300.

[3]

Community analysis by Biolog: curve integration for statistical analysis of activated sludge microbial habitats, J.B. Guckert, G.J. Carr, T.D. Johnson, B.G. Hamm, D.H. Davidson, Y. Kumagai, Journal of Microbiological Methods, 1996, v. 27:2-3, p. 183- 187.

[4]

Statistical analysis of the time-course of Biolog substrate utilization. C.A. Hackett, B.S. Griffiths, Journal of Microbiological Methods, 1997, v. 30, p. 63-69.

[5]

Statistical comparisons of community catabolic profiles. E. Glimm, H. Heuer, B. Engelen, K. Smalla, H. Backhaus, Journal of Microbiological Methods, 1997, v. 30, p. 71-80.

[6]

Application of multivariate analysis of variance and related techniques in soil studies with substrate utilization tests, W. Hitzl, M. Henrich, M. Kessel, and H. Insam, Journal of Microbiological Methods, 1997, v. 30, p. 81-89.

[7]

Using the Gini coefficient with BIOLOG substrate utilization data to provide an alternative quantitative measure  for comparing bacterial soil communities, B.D. Harch, R.L. Correll, W. Meech, C.A. Kirkby, and C.E. Pankhurst, Journal of Microbiological Methods, 1997, v. 30, p. 91-101. 

[8]

Defining soil quality in terms of microbial community structure. M. Firestone, T. Balser, D. Herman, Annual Reports of Research Projects, UC Berkeley, 1998.

[9]

Defining soil quality in terms of microbial community structure. M. Firestone, T. Balser, D. Herman, Annual Reports of Research Projects, UC Berkeley, 1997.

 

 

 

 

         
 

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