Short- and long-term motor outcome of STN-DBS in Parkinson’s Disease: focus on sex differences

Neurological Sciences - Subthalamic nucleus deep brain stimulation (STN-DBS) is an established treatment for patients with Parkinson’s disease (PD) with motor complications; the contribution...     

Statistical image reconstruction from correlated data with applications to PET

Most statistical reconstruction methods for emission tomography are designed for data modeled as conditionally independent Poisson variates. In reality, due to scanner detectors, electronics and data processing, correlations are introduced into the data resulting in dependent variates. In general, these correlations are ignored because they are difficult to measure and lead to computationally challenging statistical reconstruction algorithms. This work addresses the second concern, seeking to simplify the reconstruction of correlated data and provide a more precise image estimate than the conventional independent methods. In general, correlated variates have a large non-diagonal covariance matrix that is computationally challenging to use as a weighting term in a reconstruction algorithm. This work proposes two methods to simplify the use of a non-diagonal covariance matrix as the weighting term by (a) limiting the number of dimensions in which the correlations are modeled and (b) adopting flexible, yet computationally tractable, models for correlation structure. We apply and test these methods with simple simulated PET data and data processed with the Fourier rebinning algorithm which include the one-dimensional correlations in the axial direction and the two-dimensional correlations in the transaxial directions. The methods are incorporated into a penalized weighted least-squares 2D reconstruction and compared with a conventional maximum a posteriori approach.     

The influence of preoperative teres minor muscle fatty degeneration on active external rotation following reverse total shoulder arthroplasty

BackgroundReverse total shoulder arthroplasty (rTSA) improves shoulder elevation in patients treated for cuff tear arthropathy (CTA) or irreparable massive cuff tears. Patient satisfaction can be limited by reduced active external rotation (AER). Rotator cuff muscles that externally rotate the shoulder are infraspinatus and teres minor (TM).AimThe purpose of this study was to assess the correlation between preoperative TM fatty degeneration and postoperative AER after rTSA performed for CTA or irreparable cuff tears.MethodsConstant scores and active range of motion were consecutively collected for 109 shoulders in 97 patients (mean: 75.73 ± 8.94 years; 31 male, 66 female) over a 10-year period. AER was evaluated with the humerus in adduction (AER1) and in abduction. TM muscle atrophy was scored according to Goutallier's classification, assessed on preoperative computed tomography scans.ResultsMultivariate analysis showed that TM fatty infiltration was a predictor of AER1. AER1 decreased by 4.9 degrees preoperatively and by 6.4 degrees at the final follow-up, for each increment in Goutallier grade (P = .02). Postoperatively, AER evaluated with humerus in abduction improved significantly (P < .001), but did not correlate with TM Goutallier grade. At a mean follow-up of 38 months (range: 24 to 96), mean Constant score improved from 20.5 ± 11.1 to 68.4 ± 14.9 (P < .001), as did shoulder active range of motion in all planes including AER1 (P < .001).ConclusionThis is the first study to quantify the inverse correlation between AER and TM Goutallier grade, both preoperatively and after rTSA; this information guides prognosis for patients with TM degeneration undergoing rTSA. Further studies are necessary to have a better understanding and find reliable solutions.Level of evidenceLevel III; Case Series     

Machine learning model prediction of 6-month functional outcome in elderly patients with intracerebral hemorrhage

Spontaneous intracerebral hemorrhage (ICH) has an increasing incidence and a worse outcome in elderly patients. The ability to predict the functional outcome in these patients can be helpful in supporting treatment decisions and establishing prognostic expectations. We evaluated the performance of a machine learning (ML) model to predict the 6-month functional status in elderly patients with ICH leveraging the predictive value of the clinical characteristics at hospital admission. Data were extracted by a retrospective multicentric database of patients?≥?70 years of age consecutively admitted for the management of spontaneous ICH between January 1, 2014 and December 31, 2019. Relevant demographic, clinical, and radiological variables were selected by a feature selection algorithm (Boruta) and used to build a ML model. Outcome was determined according to the Glasgow Outcome Scale (GOS) at 6 months from ICH: dead (GOS 1), poor outcome (GOS 2–3: vegetative status/severe disability), and good outcome (GOS 4–5: moderate disability/good recovery). Ten features were selected by Boruta with the following relative importance order in the ML model: Glasgow Coma Scale, Charlson Comorbidity Index, ICH score, ICH volume, pupillary status, brainstem location, age, anticoagulant/antiplatelet agents, intraventricular hemorrhage, and cerebellar location. Random forest prediction model, evaluated on the hold-out test set, achieved an AUC of 0.96 (0.94–0.98), 0.89 (0.86–0.93), and 0.93 (0.90–0.95) for dead, poor, and good outcome classes, respectively, demonstrating high discriminative ability. A random forest classifier was successfully trained and internally validated to stratify elderly patients with spontaneous ICH into prognostic subclasses. The predictive value is enhanced by the ability of ML model to identify synergy among variables.

     

Natural course of the vitelliform stage in best vitelliform macular dystrophy: a five-year follow-up study

Graefe's Archive for Clinical and Experimental Ophthalmology - The vitelliform stage is the typical phenotypic manifestation of Best vitelliform macular dystrophy (BVMD). As yet, no study has...     

Feline Morbillivirus Infection in Domestic Cats: What Have We Learned So Far?

Feline morbillivirus (FeMV) was identified for the first time in stray cats in 2012 in Hong Kong and, since its discovery, it was reported in domestic cats worldwide. Although a potential association between FeMV infection and tubulointerstitial nephritis (TIN) has been suggested, this has not been proven, and the subject remains controversial. TIN is the most frequent histopathological finding in the context of feline chronic kidney disease (CKD), which is one of the major clinical pathologies in feline medicine. FeMV research has mainly focused on defining the epidemiology, the role of FeMV in the development of CKD, and its in vitro tropism, but the pathogenicity of FeMV is still not clear, partly due to its distinctive biological characteristics, as well as to a lack of a cell culture system for its rapid isolation. In this review, we summarize the current knowledge of FeMV infection, including genetic diversity of FeMV strains, epidemiology, pathogenicity, and clinicopathological findings observed in naturally infected cats.     

Posterior box isolation as an adjunctive ablation strategy with the second-generation cryoballoon for paroxysmal atrial fibrillation: a comparison with standard cryoballoon pulmonary vein isolation

Journal of Interventional Cardiac Electrophysiology - The purpose of the study was to evaluate the impact of left atrial posterior wall isolation (LAPWI) in addition to pulmonary vein isolation...     

Combined Peridynamics and Discrete Multiphysics to Study the Effects of Air Voids and Freeze-Thaw on the Mechanical Properties of Asphalt

This paper demonstrates the use of peridynamics and discrete multiphysics to assess micro crack formation and propagation in asphalt at low temperatures and under freezing conditions. Three scenarios are investigated: (a) asphalt without air voids under compressive load, (b) asphalt with air voids and (c) voids filled with freezing water. The first two are computed with Peridynamics, the third with peridynamics combined with discrete multiphysics. The results show that the presence of voids changes the way cracks propagate in the material. In asphalt without voids, cracks tend to propagate at the interface between the mastic and the aggregate. In the presence of voids, they ‘jump’ from one void to the closest void. Water expansion is modelled by coupling Peridynamics with repulsive forces in the context of Discrete Multiphysics. Freezing water expands against the voids’ internal surface, building tension in the material. A network of cracks forms in the asphalt, weakening its mechanical properties. The proposed methodology provides a computational tool for generating samples of ‘digital asphalt’ that can be tested to assess the asphalt properties under different operating conditions.