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#IL6

Cytology and Geneticscytgen
2024-12-26

Effect of the Secretome of Mesenchymal Placenta Stem Cells on the Functional Properties of Lewis Lung Carcinoma Cells In Vitro - - link.springer.com/article/10.3

Giuseppe MichieliGMIK69@mstdn.science
2023-09-01

''Interpretation: In this first prospective #UK study, probable CAPA was associated with #corticosteroid use, receipt of #IL6 inhibitors and pre-existing #COPD. CAPA did not impact #mortality following adjustment for prognostic variables.''

Arjan Boltjestinyspheresof
2023-07-21

@gpollara @JExpMed

The work on the DC - quite descriptive - is published. Find it here: mastodon.social/@tinyspheresof

The T cell work has not been published. I think I have a mostly complete manuscript that I could put on bioRXv. I'll discuss it with my former PI.
The short version: and were very abundant, and were in the way of Treg-Teff interaction, not reacting to suppression anymore. Possibly partly explaining why drugs against these work in .

Arjan Boltjestinyspheresof
2023-07-21

@gpollara @JExpMed Intriguing. In an earlier postdoc position I had a look at , and especially the were abundant, but seemed to be just sitting in the inflammatory environment, doing little. One of the very abundant cytokines:

We also looked at the effect of that and other , but mostly on , among others due to the low abundance of cDC1 in HC blood to be able to do many in vitro experiments.

AMPEL-CDSSAMPEL@med-mastodon.com
2023-02-13

#ML outperforms classic scores. One of these is the end-stage liver disease prediction model for 90-day mortality (#MELD)

- We derived an ML model AMELD which outperforms MELD, MELD-Na, MELD 3.0, and MELD-Plus7

- How? AMELD extends the classic MELD predictors INR, bilirubin, and cystatin C / creatinine to include total protein, cholinesterase and #IL6

doi.org/10.1515/labmed-2022-01

Using more accurate AMELD prediction may improve #CDS for performing liver transplants

#AMPEL #CDSS #LTx #Liver

Front page of a paper with the title "A new machine-learning based prediction of survival in patients with end-stage liver disease" Abstract: This study aims to improve the model of end-stage liver disease (MELD) score for 90-day mortality prediction with the help of different ML algorithms. Methods: We retrospectively analyzed the clinical and laboratory data of 654 patients who were recruited during the evaluation process for liver transplantation at University Hospital Leipzig. After comparing 13 different machinelearning algorithms in a nested cross-validation setting and selecting the best performing one, we built a new model to predict 90-day mortality in patients with end-stage liver disease. Results: Penalized regression algorithms yielded the highest prediction performance in our machine-learning algorithm benchmark. In favor of a simpler model, we chose the least absolute shrinkage and selection operator (lasso) regression. Beside the classical MELD international normalized ratio (INR) and bilirubin, the lasso regression selected cystatin C over creatinine, aswell as IL-6, total protein, and cholinesterase. The new model offers improved discrimination and calibration over MELD and MELD with sodium (MELD-Na), MELD 3.0, or the MELD-Plus7 risk score. Conclusions: We provide a new machine-learning-based model of end-stage liver disease that incorporates synthesis and inflammatory markers and may improve the classical MELD score for 90-day survival prediction.

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