Prediction of post-stroke cognitive impairment by Montreal Cognitive Assessment (MoCA) performances in acute stroke: comparison of three normative datasets

Background Cognitive assessment in acute stroke is relevant for identifying patients at risk of persistent post-stroke cognitive impairment (PSCI). Despite preliminary evidence on MoCA accuracy, there is no consensus on its optimal score in the acute stroke setting to predict PSCI. Aims (1) To explore whether the application of different normative datasets to MoCA scores obtained in the acute stroke setting results in variable frequency of patients defined as cognitively impaired; (2) to assess whether the normality cut-offs provided by three normative datasets predict PSCI at 6-9 months; (3) to calculate alternative MoCA cut-offs able to predict PSCI. Methods Consecutive stroke patients were reassessed at 6-9 months with extensive neuropsychological and functional batteries for PSCI determination. Results Out of 207 enrolled patients, 118 (57%) were followed-up (mean 7.4 +/- 1.7 months), and 77 of them (65%) received a PSCI diagnosis. The application of the normality thresholds provided by the 3 normative datasets yielded to variable (from 28.5% to 41%) rates of patients having an impaired MoCA performance, and to an inadequate accuracy in predicting PSCI, maximizing specificity instead of sensitivity. In ROC analyses, a MoCA score of 22.82, adjusted according to the most recent normative dataset, achieved a good diagnostic accuracy in predicting PSCI. Conclusions The classification of acute stroke patients as normal/impaired based on MoCA thresholds proposed by general population normative datasets underestimated patients at risk of persistent PSCI. We calculated a new adjusted MoCA score predictive of PSCI in acute stroke patients to be further tested in larger studies

An RFID-Based Tracing and Tracking System for the Fresh Vegetables Supply Chain

The paper presents an innovative gapless traceability system able to improve the main business processes of the fresh vegetables supply chain. The performed analysis highlighted some critical aspects in the management of the whole supply chain, from the land to the table of the end consumer, and allowed us to reengineer the most important processes. In particular, the first steps of the supply chain, which include cultivation in greenhouses and manufacturing of packaged vegetables, were analyzed. The re-engineered model was designed by exploiting the potentialities derived from the combined use of innovative Radio Frequency technologies, such as RFID and NFC, and important international standards, such as EPCglobal. The proposed tracing and tracking system allows the end consumer to know the complete history of the purchased product. Furthermore, in order to evaluate the potential benefits of the reengineered processes in a real supply chain, a pilot project was implemented in an Italian food company, which produces ready-to-eat vegetables, known asIV gammaproducts. Finally, some important metrics have been chosen to carry out the analysis of the potential benefits derived from the use of the re-engineered model

Bioinformatics-Driven Multi-Factorial Insight into α-Galactosidase Mutations

Fabry disease is a rare genetic disorder caused by deficient activity of the lysosomal enzyme alpha-galactosidase A (AGAL), resulting in the accumulation of globotriaosylceramides (Gb3) in tissues and organs. This buildup leads to progressive, multi-systemic complications that severely impact quality of life and can be life-threatening. Interpreting the functional consequences of missense variants in the GLA gene remains a significant challenge, especially in rare diseases where experimental evidence is scarce. In this study, we present an integrative computational framework that combines structural, interaction, pathogenicity, and stability data from both in silico tools and experimental sources, enriched through expert curation and structural analysis. Given the clinical relevance of pharmacological chaperones in Fabry disease, we focus in particular on the structural characteristics of variants classified as “amenable” to such treatments. Our multidimensional analysis—using tools such as AlphaMissense, EVE, FoldX, and ChimeraX—identifies key molecular features that distinguish amenable from non-amenable variants. We find that amenable mutations tend to preserve protein stability, while non-amenable ones are associated with structural destabilisation. By comparing AlphaMissense with alternative predictors rooted in evolutionary (EVE) and thermodynamic (FoldX) models, we explore the relative contribution of different biological paradigms to variant classification. Additionally, the investigation of outlier variants—where AlphaMissense predictions diverge from clinical annotations—highlights candidates for further experimental validation. These findings demonstrate how combining structural bioinformatics with machine learning–based predictions can improve missense variant interpretation and support precision medicine in rare genetic disorders

Exploring ligand interactions with human phosphomannomutases using recombinant bacterial thermal shift assay and biochemical validation

PMM2-CDG, a disease caused by mutations in phosphomannomutase-2, is the most common congenital disorder of glycosylation. Yet, it still lacks a cure. Targeting phosphomannomutase-2 with pharmacological chaperones or inhibiting the phosphatase activity of phosphomannomutase-1 to enhance intracellular glucose-1,6-bisphosphate have been proposed as therapeutical approaches. We used Recombinant Bacterial Thermal Shift Assay to assess the binding of a substrate analog to phosphomannomutase-2 and the specific binding to phosphomannomutase-1 of an FDA-approved drug - clodronate. We also deepened the clodronate binding by enzyme activity assays and in silico docking. Our results confirmed the selective binding of clodronate to phosphomannomutase-1 and shed light on such binding

Early predictors of dysphagia in ischaemic stroke patients

Background and purpose: Post-stroke dysphagia affects outcome. In acute stroke patients, the aim was to evaluate clinical, cognitive and neuroimaging features associated with dysphagia and develop a predictive score for dysphagia.Methods: Ischaemic stroke patients underwent clinical, cognitive and pre-morbid function evaluations. Dysphagia was retrospectively scored on admission and discharge with the Functional Oral Intake Scale.Results: In all, 228 patients (mean age 75.8 years; 52% males) were included. On admission, 126 (55%) were dysphagic (Functional Oral Intake Scale <= 6). Age (odds ratio [OR] 1.03, 95% confidence interval [CI] 1.00-1.05), pre-event modified Rankin scale (mRS) score (OR 1.41, 95% CI 1.09-1.84), National Institutes of Health Stroke Scale (NIHSS) score (OR 1.79, 95% CI 1.49-2.14), frontal operculum lesion (OR 8.53, 95% CI 3.82-19.06) and Oxfordshire total anterior circulation infarct (TACI) (OR 1.47, 95% CI 1.05-2.04) were independently associated with dysphagia at admission. Education (OR 0.91, 95% CI 0.85-0.98) had a protective role. At discharge, 82 patients (36%) were dysphagic. Pre-event mRS (OR 1.28, 95% CI 1.04-1.56), admission NIHSS (OR 1.88, 95% CI 1.56-2.26), frontal operculum involvement (OR 15.53, 95% CI 7.44-32.43) and Oxfordshire classification TACI (OR 3.82, 95% CI 1.95-7.50) were independently associated with dysphagia at discharge. Education (OR 0.89, 95% CI 0.83-0.96) and thrombolysis (OR 0.77, 95% CI 0.23-0.95) had a protective role. The 6-point "NOTTEM" (NIHSS, opercular lesion, TACI, thrombolysis, education, mRS) score predicted dysphagia at discharge with good accuracy. Cognitive scores had no role in dysphagia risk.Conclusions: Dysphagia predictors were defined and a score was developed to evaluate dysphagia risk during stroke unit stay. In this setting, cognitive impairment is not a predictor of dysphagia. Early dysphagia assessment may help in planning future rehabilitative and nutrition strategies

In Vitro Modeling of Tumor-Immune System Interaction.

Immunotherapy has emerged during the past two decades as an innovative and successful form of cancer treatment. However, frequently, mechanisms of actions are still unclear, predictive markers are insufficiently characterized, and preclinical assays for innovative treatments are poorly reliable. In this context, the analysis of tumor/immune system interaction plays key roles, but may be unreliably mirrored by in vivo experimental models and standard bidimensional culture systems. Tridimensional cultures of tumor cells have been developed to bridge the gap between in vitro and in vivo systems. Interestingly, defined aspects of the interaction of cells from adaptive and innate immune systems and tumor cells may also be mirrored by 3D cultures. Here we review in vitro models of cancer/immune cell interaction and we propose that updated technologies might help develop innovative treatments, identify biologicals of potential clinical relevance, and select patients eligible for immunotherapy treatments