Background: Laboratory evidence of cholesterol-induced production of amyloid ␤ as a putative neurotoxin precipitating Alzheimer disease, along with epidemiological evidence, suggests that cholesterol-lowering statin drugs may favorably influence the progression of the disorder.Objective: To determine if treatment with atorvastatin calcium affects the cognitive and/or behavioral decline in patients with mild to moderate Alzheimer disease.Design: Pilot intention-to-treat, proof-of-concept, doubleblind, placebo-controlled, randomized (1:1) trial with a 1-year exposure to once-daily atorvastatin calcium (80 mg; two 40-mg tablets) or placebo using last observation carried forward analysis of covariance as the primary method of statistical assessment.Participants: Individuals with mild to moderate Alzheimer disease (Mini-Mental State Examination score of 12-28) were recruited. Of the 98 participants providing informed consent, 71 were eligible for randomization, 67 were randomized, and 63 subjects completed the 3-month visit and were considered evaluable. Main Outcome Measures:The primary outcome measures were change in Alzheimer's Disease Assessment Scale-cognitive subscale and the Clinical Global Impres-

Among Parkinson’s disease (PD) symptoms, freezing of gait (FoG) is one of the most debilitating. To assess FoG, current clinical practice mostly employs repeated evaluations over weeks and months based on questionnaires, which may not accurately map the severity of this symptom. The use of a non-invasive system to monitor the activities of daily living (ADL) and the PD symptoms experienced by patients throughout the day could provide a more accurate and objective evaluation of FoG in order to better understand the evolution of the disease and allow for a more informed decision-making process in making adjustments to the patient’s treatment plan. This paper presents a new algorithm to detect FoG with a machine learning approach based on Support Vector Machines (SVM) and a single tri-axial accelerometer worn at the waist. The method is evaluated through the acceleration signals in an outpatient setting gathered from 21 PD patients at their home and evaluated under two different conditions: first, a generic model is tested by using a leave-one-out approach and, second, a personalised model that also uses part of the dataset from each patient. Results show a significant improvement in the accuracy of the personalised model compared to the generic model, showing enhancement in the specificity and sensitivity geometric mean (GM) of 7.2%. Furthermore, the SVM approach adopted has been compared to the most comprehensive FoG detection method currently in use (referred to as MBFA in this paper). Results of our novel generic method provide an enhancement of 11.2% in the GM compared to the MBFA generic model and, in the case of the personalised model, a 10% of improvement with respect to the MBFA personalised model. Thus, our results show that a machine learning approach can be used to monitor FoG during the daily life of PD patients and, furthermore, personalised models for FoG detection can be used to improve monitoring accuracy.

Among Parkinson’s disease (PD) motor symptoms, freezing of gait (FOG) may\ud be the most incapacitating. FOG episodes may result in falls and reduce patients’\ud quality of life. Accurate assessment of FOG would provide objective information\ud to neurologists about the patient’s condition and the symptom’s characteristics,\ud while it could enable non-pharmacologic support based on rhythmic\ud cues.\ud This paper is, to the best of our knowledge, the first study to propose a\ud deep learning method for detecting FOG episodes in PD patients. This model\ud is trained using a novel spectral data representation strategy which considers information from both the previous and current signal windows. Our approach\ud was evaluated using data collected by a waist-placed inertial measurement unit\ud from 21 PD patients who manifested FOG episodes. These data were also employed\ud to reproduce the state-of-the-art methodologies, which served to perform\ud a comparative study to our FOG monitoring system.\ud The results of this study demonstrate that our approach successfully outperforms\ud the state-of-the-art methods for automatic FOG detection. Precisely, the\ud deep learning model achieved 90% for the geometric mean between sensitivity\ud and specificity, whereas the state-of-the-art methods were unable to surpass the\ud 83% for the same metric.Peer ReviewedPostprint (published version

A novel biosurfactant detection assay was developed for the observation of surfactants on agar plates. By using an airbrush to apply a fine mist of oil droplets, surfactants can be observed instantaneously as halos around biosurfactant-producing colonies. This atomized oil assay can detect a wide range of different synthetic and bacterially produced surfactants. This method could detect much lower concentrations of many surfactants than a commonly used water drop collapse method. It is semiquantitative and therefore has broad applicability for uses such as high-throughput mutagenesis screens of biosurfactant-producing bacterial strains. The atomized oil assay was used to screen for mutants of the plant pathogen Pseudomonas syringae pv. syringae B728a that were altered in the production of biosurfactants. Transposon mutants displaying significantly altered surfactant halos were identified and further analyzed. All mutants identified displayed altered swarming motility, as would be expected of surfactant mutants. Additionally, measurements of the transcription of the syringafactin biosynthetic cluster in the mutants, the principal biosurfactant known to be produced by B728a, revealed novel regulators of this pathway.

We have compared the clinical and histological features of 149 complete moles with 146 triploid partial moles and 107 diploid non-molar hydropic abortions initially registered as moles for human chorionic gonadotrophin (hCG) follow-up. Forty-one patients with complete moles, five with partial moles and one with hydropic abortion received chemotherapy for hCG elevations interpreted as persistent trophoblastic disease. Complete moles were aborted or were evacuated significantly earlier than partial moles (means of 12.1 and 15.4 weeks; P < 0.001) and hydropic abortions significantly earlier than complete moles (mean 10.7 weeks; P < 0.005). The means of the highest recorded hCG were higher in complete moles (184,056 i.v.) than in partial moles (66,259 i.v.) and hydropic abortion (7942 i.v.). When hCG became normal without chemotherapy, this occurred earlier in patients with hydropic abortion than in those with partial moles (means of 46.7 days and 62.8 days; P < 0.001) and earlier in partial moles than in complete moles (mean 78.3 days; P < 0.005). The incidence of partial moles was comparable throughout fertile years but rose to 1.9 times the average after 40 years. Complete moles were commoner between 14 and 25 years and after 35 years, reaching 4.8 times the average after 40 years. Hydropic abortions were rare before 25 years and increased with age to 12 times the average after 40 years. Stromal karyorrhexis and shape of villi, before they become hydropic, discriminate well between complete and partial mole. Hydrops increased and vascularity decreased with molar age and the presence of non-hydropic villi or vessels did not discriminate between partial mole and the younger complete moles evacuated nowadays.