Parainfluenza 3 (Strain III V2932)

Name: Parainfluenza 3 (Strain III V2932)
SKU: DAGA-511
Rating: 5 (1 reviews)

Parainfluenza 3 strain iii, native protein

COA

SDS

Datasheet

Summary

Nature

Native

Tag/Conjugate

Unconjugated

Alternative Names

Group V ((-)ssRNA); Mononegavirales; Paramyxoviridae; Human parainfluenza viruses; HPIV3; Parainfluenza 3 virus; PIV3; Parainfluenza Type 2 and 6

Format

Liquid

Concentration

Batch dependent - please inquire should you have specific requirements

Preservative

None

Storage

Store at <-72°C. Avoid multiple freeze/thaw cycles.

Antigen Description

Parainfluenza viruses belong to the genus Paramyxovirus of the family Paramyxoviridae. They are enveloped viruses with a single-strand RNA genome of negative polarity and range in diameter from 150 to 202 nm. Four types of parainfluenza viruses have beendtified. Types 1 and 2 are major causes of laryngotracheo-bronchitis (croup), with greatest severity in children ages 2 to 4 years. Type 3 infection can also lead to croup but, most notably, is a major cause of infant bronchiolitis, pneumonia, and hospitalization. Infection from type 3 virus is most severe in infants less than 1 year old. Parainfluenza type 4 has been associated only with mild upper respiratory illness in adults and children.

Keywords

Group V ((-)ssRNA);Mononegavirales;Paramyxoviridae;Human parainfluenza viruses;HPIV3;Parainfluenza 3 virus;PIV3;Parainfluenza Type 2 and 6

Citations

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Documents

pdf COA

pdf SDS

pdf Datasheet

QA & Reviews

Customer Reviews

References

Dynamic Characteristics and Wall Effects of Bubble Bursting in Gas-Liquid-Solid Three-Phase Particle Flow

PROCESSES

Authors: Lu, Jianfei; Wang, Tong; Li, Lin; Yin, Zichao; Wang, Ronghui; Fan, Xinghua; Tan, Dapeng

Abstract

The bubble bursting process existing in the particle flow is a complex gas-liquid-solid three-phase coupling dynamic problem. The bubble bursting mechanism, including dynamic characteristics and wall effects, is not clear. To address the above matters, we present a modeling method for the piecewise linear interface calculation-volume of fluid (PLIC-VOF) based bubble burst. The bubble bursting process near or on the wall is analyzed to reveal the dynamic characteristics of bubble bursting and obtain the effect of a bubble bursting on the surrounding flow field. Then a particle image velocimetry (PIV) based self-developed experimental observation platform is established, and the effectiveness of the proposed method is verified. Research results indicate that, in the high-speed turbulent environment, a large pressure difference existed in the bubble tail, which induces the bubble burst to occur; the distance between the wall and the bubble decreases; the higher the flow velocity is, the less time is acquired for bubble bursting, but when the flow velocity exceeds the critical velocity 50 m/s, more time is needed; the coalescence-burst process of double bubbles increases the bubble bursting time, which causes the acceleration of particle motion to reduce.

The Pan-Immune-Inflammation Value is a new prognostic biomarker in metastatic colorectal cancer: results from a pooled-analysis of the Valentino and TRIBE first-line trials

BRITISH JOURNAL OF CANCER

Authors: Fuca, Giovanni; Guarini, Vincenzo; Antoniotti, Carlotta; Morano, Federica; Moretto, Roberto; Corallo, Salvatore; Marmorino, Federica; Lonardi, Sara; Rimassa, Lorenza; Sartore-Bianchi, Andrea; Borelli, Beatrice; Tampellini, Marco; Bustreo, Sara; Claravezza, Matteo; Boccaccino, Alessandra; Murialdo, Roberto; Zaniboni, Alberto; Tomasello, Gianluca; Loupakis, Fotios; Adamo, Vincenzo; Tonini, Giuseppe; Cortesi, Enrico; de Braucl, Filippo; Cremolini, Chiara; Pietrantonio, Filippo

Abstract

Background Immune-inflammatory biomarkers (IIBs) showed a prognostic relevance in patients with metastatic CRC (mCRC). We aimed at evaluating the prognostic power of a new comprehensive biomarker, the Pan-Immune-Inflammation Value (PIV), in patients with mCRC receiving first-line therapy. Methods In the present pooled-analysis, we included patients enrolled in the Valentino and TRIBE trials. PIV was calculated as: (neutrophil count x platelet count x monocyte count)/lymphocyte count. A cut-off was determined using the maximally selected rank statistics method. Generalised boosted regression (GBR), the Kaplan-Meier method and Cox hazards regression models were used for survival analyses. Results A total of 438 patients were included. Overall, 208 patients (47%) had a low-baseline PIV and 230 (53%) had a high-baseline PIV. Patients with high PIV experienced a worse PFS (HR, 1.66; 95% CI, 1.36-2.03, P < 0.001) and worse OS (HR, 2.01; 95% CI, 1.57-2.57; P < 0.001) compared to patients with low PIV. PIV outperformed the other IIBs in the GBR model and in the multivariable models. Conclusion PIV is a strong predictor of survival outcomes with better performance than other well-known IIBs in patients with mCRC treated with first-line therapy. PIV should be prospectively validated to better stratify mCRC patients undergoing first-line therapy.