No abstract

Chronic lymphocytic leukemia (CLL) clones are characterized by loss of a critical region in 13q14.3, (del(13)(q14)) involving the microRNA (miRNA) cluster miR-15a and miR-16-1. We have investigated the effects of replacement of miR-15a and miR-16-1. CLL cells transfected with these miRNA mimics exhibited a decrease in cell viability in vitro and impaired capacity for engraftment and growth in NOD/Shi-scid,γcnull (NSG) mice. No synergistic effects were observed when the two miRNA mimics were combined. The phenomena were not restricted to CLL with the del(13)(q14) lesion. Similar effects induced by miRNA mimics were seen in cells with additional chromosomal abnormalities with the exception of certain CLL clones harboring TP53 alterations. Administration of miRNA mimics to NSG mice previously engrafted with CLL clones resulted in substantial tumor regression. CLL cell transfection with miR-15a and miR-16-1-specific inhibitors resulted in increased cell viability in vitro and in an enhanced capacity of the engrafted cells to grow in NSG mice generating larger splenic nodules. These data demonstrate that the strong control by miR-15a and miR-16-1 on CLL clonal expansion is exerted also at the level of full-blown leukemia and provide indications for a miRNA-based therapeutic strategy.

BackgroundClinical markers of cognitive decline in Parkinson's disease (PD) encompass several mental non-motor symptoms such as hallucinations, apathy, anxiety, and depression. Furthermore, freezing of gait (FOG) and specific gait alterations have been associated with cognitive dysfunction in PD. Finally, although low cerebrospinal fluid levels of amyloid-β42 have been found to predict cognitive decline in PD, hitherto PET imaging of amyloid-β (Aβ) failed to consistently demonstrate the association between Aβ plaques deposition and mild cognitive impairment in PD (PD-MCI).AimFinding significant features associated with PD-MCI through a machine learning approach.Patients and methodsPatients were assessed with an extensive clinical and neuropsychological examination. Clinical evaluation included the assessment of mental non-motor symptoms and FOG using the specific items of the MDS-UPDRS I and II. Based on the neuropsychological examination, patients were classified as subjects without and with MCI (noPD-MCI, PD-MCI). All patients were evaluated using a motion analysis system. A subgroup of PD patients also underwent amyloid PET imaging. PD-MCI and noPD-MCI subjects were compared with a univariate statistical analysis on demographic data, clinical features, gait analysis variables, and amyloid PET data. Then, machine learning analysis was performed two times: Model 1 was implemented with age, clinical variables (hallucinations/psychosis, depression, anxiety, apathy, sleep problems, FOG), and gait features, while Model 2, including only the subgroup performing PET, was implemented with PET variables combined with the top five features of the former model.ResultsSeventy-five PD patients were enrolled (33 PD-MCI and 42 noPD-MCI). PD-MCI vs. noPD-MCI resulted in older and showed worse gait patterns, mainly characterized by increased dynamic instability and reduced step length; when comparing amyloid PET data, the two groups did not differ. Regarding the machine learning analyses, evaluation metrics were satisfactory for Model 1 overcoming 80% for accuracy and specificity, whereas they were disappointing for Model 2.ConclusionsThis study demonstrates that machine learning implemented with specific clinical features and gait variables exhibits high accuracy in predicting PD-MCI, whereas amyloid PET imaging is not able to increase prediction. Additionally, our results prompt that a data mining approach on certain gait parameters might represent a reliable surrogate biomarker of PD-MCI.

The presence or absence of choline peak evaluated by (1)H-MRS, well correlated with the expression of CaSR in patients with breast lesions (p < 0.01), supports the hypothesis that CaSR may play an important role in the production of choline in breast cancer.