Clinical and radiological outcomes using the LIMA SMR stemless implant—A 2-year follow-up in 49 patients

BackgroundSince the introduction of stemless anatomic total shoulder arthroplasty (TSA), many studies have been published on this specific group of TSA implants. The following study aimed to evaluate clinical and radiological short- to mid-term outcomes of the LIMA SMR stemless anatomical implant.MethodsWe prospectively evaluated the outcome of 53 TSAs in 52 patients, with a mean age of 58.45 years (range 47-78 years) at the time of implantation with a minimum follow-up of 2 years (range 24-47 months). All patients were physically and radiologically examined; the results were documented by the Constant-Murley Score and the Simple Shoulder Value.ResultsSignificant improvements from preoperative to latest follow-up were documented in Constant-Murley Score (29.0-75.84 pts; P < .001), active range of motion (abduction 84.7°-133.2°, flexion 95.3°-146.4°, and external rotation 12.7°-32.4°). The mean Simple Shoulder Value was 82.15% at the last follow-up. There was no complete loosening of the humeral component, but a lowering of bone mineral density (radiolucencies, RLL) was observed in anteroposterior or axially views—radiographs at the humeral component in 10.2% of the cases, most of them on the anteroposterior view at the calcar region. There was no statistical difference in the outcome of the patients with RLL compared with the ones without radiolucencies. Major complications or revisions did not occur.ConclusionThis study provides comparable improvement in functional, radiographic, and subjective mid-term results with other stemless anatomic implants.     

125 Bände Unfallchirurgie

Die Unfallchirurgie -     

Im Wandel der Zeit – Der Unfallchirurg 1985–2021

Die Unfallchirurgie -     

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.

     

Expression of decitabine-targeted oncogenes in meningiomas in vivo

Neurosurgical Review - Treatment of meningiomas refractory to surgery and irradiation is challenging and effective chemotherapies are still lacking. Recently, in vitro analyses revealed decitabine...     

First detection of Theileria parva in cattle from Cameroon in the absence of the main tick vector Rhipicephalus appendiculatus

A major risk factor for the spread of livestock diseases and their vectors is the uncontrolled transboundary movement of live animals for trade and grazing. Such movements constrain effective control of tick‐transmitted pathogens, including Theileria parva. Only limited studies have been undertaken to identify ticks and tick‐borne diseases (TTBDs) affecting cattle in central African countries, including Cameroon. We hereby report the collection of baseline data on the prevalence of T. parva in Cameroon through a countrywide cross‐sectional survey, conducted in 2016, involving collection of blood samples from cattle from 63 sites across the five agro‐ecological zones (AEZs) of the country. ELISA‐based surveillance of infected cattle was performed on 479 randomly selected samples and revealed specific antibodies to T. parva in 22.7% and T. mutans in 41.1% of cattle. Screening of 1,340 representative DNA samples for the presence of T. parva identified 25 (1.86%) positives using a p104 antigen gene‐based nested PCR assay. The positives were distributed across agro‐ecological zones I, II, III and V. None of the p104 positive cattle exhibited clinical symptoms of East Coast fever (ECF). Using reverse line blot (RLB), 58 (4.3%) and 1,139 (85%) of the samples reacted with the T. parva and T. mutans oligonucleotide probes, respectively. This represents the first report of T. parva from Cameroon. Surprisingly, no Rhipicephalus appendiculatus ticks, the main vector of T. parva, were identified in a parallel study involving comprehensive morphological and molecular survey of tick species present in the country. Only two of the 25 p104 positive cattle were PCR‐positive for the CD8+ T‐cell target schizont‐expressed antigen gene Tp1. Cloning and sequencing of Tp1 amplicons revealed sequence identity with the reference T. parva Muguga. This new finding raises serious concerns of a potential spread of ECF into the central African region.     

The Influence of Edge Energy on Step Flow Instability for Crystals with Bravais Versus Non-Bravais Lattice Structures

We examine the meandering instability for prismatic lattices formed from triangles, squares and hexagons using a nearest neighbor kinetic Monte Carlo model. In the first two cases, which are Bravais lattices, we find that facets with the orientation favored in the equilibrium shape of isolated islands are most prone to this instability, while the analogous facet for the hexagonal lattice is the least unstable. We argue that this is due to a significant difference in the reconstructed/equilibrium versus the non-reconstructed edge energy for non-Bravais crystals. Surface/edge energy is typically modeled as a single-valued function of orientation. We put forward a simple geometric argument that suggests this picture is inadequate for crystals with a non-Bravais lattice structure. In the case of a hexagonally structured lattice, our arguments indicate that the non-reconstructed edge energy can be viewed as both discontinuous and multi-valued for a subset of orientations that are commensurate with the crystal structure. We support these conclusions with density functional theory calculations that also reveal multivalued surface energies for the set of singular orientations.