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Background: Despite the increased use of mechanical thrombectomy (MT) in recent years, there remains a lack of research on in-hospital mortality rates following the procedure, the primary factors influencing these rates, and the potential for predicting them. This study aimed to utilize interpretable machine learning (ML) to help clarify these uncertainties. Methods: This retrospective study involved patients with anterior circulation large vessel occlusion (LVO)-related ischemic stroke who underwent MT. The patient division was made into two groups: (I) the in-hospital death group, referred to as miserable outcome, and (II) the in-hospital survival group, or favorable outcome. Python 3.10.9 was utilized to develop the machine learning models, which consisted of two types based on input features: (I) the Pre-MT model, incorporating baseline features, and (II) the Post-MT model, which included both baseline and MT-related features. After a feature selection process, the models were trained, internally evaluated, and tested, after which interpretation frameworks were employed to clarify the decision-making processes. Results: This study included 602 patients with a median age of 76 years (interquartile range (IQR) 65–83), out of which 54% (n = 328) were female, and 22% (n = 133) had miserable outcomes. Selected baseline features were age, baseline National Institutes of Health Stroke Scale (NIHSS) value, neutrophil-to-lymphocyte ratio (NLR), international normalized ratio (INR), the type of the affected vessel ('Vessel type'), peripheral arterial disease (PAD), baseline glycemia, and premorbid modified Rankin scale (pre-mRS). The highest odds ratio of 4.504 was observed with the presence of peripheral arterial disease (95% confidence interval (CI), 2.120–9.569). The Pre-MT model achieved an area under the curve (AUC) value of around 79% utilizing these features, and the interpretable framework discovered the baseline NIHSS value as the most influential factor. In the second data set, selected features were the same, excluding pre-mRS and including puncture-to-procedure-end time (PET) and onset-to-puncture time (OPT). The AUC value of the Post-MT model was around 84% with age being the highest-ranked feature. Conclusions: This study demonstrates the moderate to strong effectiveness of interpretable machine learning models in predicting in-hospital mortality following mechanical thrombectomy for ischemic stroke, with AUCs of 0.792 for the Pre-MT model and 0.837 for the Post-MT model. Key predictors included patient age, baseline NIHSS, NLR, INR, occluded vessel type, PAD, baseline glycemia, pre-mRS, PET, and OPT. These findings provide valuable insights into risk factors and could improve post-procedural patient management. [ABSTRACT FROM AUTHOR]

Doxorubicin is the most effective single agent in the treatment of non‐Hodgkin's lymphoma (NHL). Its use is limited because of the cardiac toxicity primarily in elderly patients (pts) and in pts with history of cardiac disease. Liposomal doxorubicin has been proven to reduce cardiotoxicity. The aim of this retrospective study was the use of nonpeghylated liposomal doxorubicin (NPLD) in term of efficacy, response rate and incidence of cardiac events. We retrospectively collected the experience of 33 Hematological Italian Centers in using NPLD. Nine hundred and forty‐six consecutive pts treated with R‐COMP (doxorubicin was substituted with NPLD, Myocet) were collected. Median age was 74 years, the reasons for use of NPLD were: age (466 pts), cardiac disease (298 pts), uncontrolled hypertension (126 pts), other reasons (56 pts). According to clinicians' evaluation, 49.9% of pts would not have used standard doxorubicin for different situations (age, cardiomyopathy, previous use of doxorubicin, and uncontrolled hypertension). Overall 687 pts (72.6%) obtained a complete remission (CR). About 5% (n = 51) of subjects developed major cardiotoxic events including heart failure (N = 31), ischemic heart disease (N = 16), acute heart attack (N = 3), and acute pulmonary oedema (N = 1). After a median follow‐up of 32 months, 651 pts were alive and the overall survival (OS) was 72%. After a median observation period of 23 months disease free survival (DFS) was 58%. Either in univariate or in multivariate analysis OS and DFS were not significantly affected by age or cardiac disease. Our findings strongly support that including R‐COMP is effective and safe when the population is at high risk of cardiac events and negatively selected. Moreover, the use of this NPLD permitted that about half of our population had the opportunity to receive the best available treatment. [ABSTRACT FROM AUTHOR]

In Saccharomyces cerevisiae the second messenger cyclic adenosine monophosphate (cAMP) and protein kinase A (PKA) play a central role in metabolism regulation, stress resistance and cell cycle progression. To monitor cAMP levels and PKA activity in vivo in single S. cerevisiae cells, we expressed an Epac-based FRET probe and a FRET-based A-kinase activity reporter, which were proven to be useful live-cell biosensors for cAMP levels and PKA activity in mammalian cells. Regarding detection of cAMP in single yeast cells, we show that in wild type strains the CFP/YFP fluorescence ratio increased immediately after glucose addition to derepressed cells, while no changes were observed when glucose was added to a strain that is not able to produce cAMP. In addition, we had evidence for damped oscillations in cAMP levels at least in SP1 strain. Regarding detection of PKA activity, we show that in wild type strains the FRET increased after glucose addition to derepressed cells, while no changes were observed when glucose was added to either a strain that is not able to produce cAMP or to a strain with absent PKA activity. Taken together these probes are useful to follow activation of the cAMP/PKA pathway in single yeast cells and for long times (up to one hour). [ABSTRACT FROM AUTHOR]