Fixation principles in metaphyseal bone—a patent based review

Osteoporotic changes start in cancellous bone due to the underlying pathophysiology. Consequently, the metaphyses are at a higher risk of "osteoporotic” fracture than the diaphysis. Furthermore, implant purchase to fix these fractures is also affected by the poor bone quality. In general, researchers and developers have worked on three different approaches to address the problem of fractures to osteoporotic bone: adapted anchoring techniques, improved load distribution as well as transfer with angular stable screws, and augmentation techniques using bone substitutes. A patent-based review was performed to evaluate which ideas were utilized to improve fixation in osteoporotic, metaphyseal bone, especially in the proximal femur, and to analyze whether the concept had entered clinical use. Anchoring devices that are either extramedullary or intramedullary have a long clinical history. However, demanding surgical techniques and complications, especially in poor quality bone, are justification that such implants and their corresponding surgical techniques need to be improved upon. Expanding elements have been evaluated in the laboratory. The results are promising and the potential of this approach has yet to be fully exploited in the clinics. Internal fixators with angular stable screws open the door for many new anchorage ideas and have great potential for further optimization of load distribution and transfer. Augmentation techniques may improve anchorage in osteoporotic bone. However, the properties of bone substitute materials will need to be modified and improved upon in order to meet the demanding requirements. If we summarise the development process and the clinical use of implants to date, we have to clearly state that more factors than simply biomechanical advantage will determine the clinical success of a new fixation principle or a new implant. Instead, fracture treatment of patients with osteoporosis really needs an interdisciplinary approach

Stabilisierung sub- und pertrochantärer Femurfrakturen mit dem PFNΑ®

Zusammenfassung: Operationsziel: Primär belastungsstabile Osteosynthese per- und subtrochantärer Femurfrakturen mit intramedullärem Kraftträger, besonders auch in osteoporotischem Knochen. Rasche Wiederherstellung der Anatomie und Funktion des verletzten Beins. Indikationen: Sämtliche per- und subtrochantäre Frakturen der AO-Klassifikation 31-A. Kontraindikationen: Offene Wachstumsfugen und ungeeignete Femuranatomie (pathologische Antekurvation bzw. fehlverheilte Schaftfrakturen). Operationstechnik: Wenn möglich geschlossene, bei Bedarf offene Reposition der Faktur auf dem Extensionstisch. Intramedulläre, unaufgebohrte Nagelung und Frakturfixation durch Einbringen einer Spiralklinge über einen Führungsdraht in das Kopf-Halsfragment. Möglichkeit zur dynamischen oder statischen Verriegelung im Femurschaft. Operative Nachsorge: Rasche Mobilisation ab dem ersten postoperativen Tag mit schmerzadaptierter Vollbelastung. Thromboseprophylaxe für 6Wochen mit Fondaparinux, Rivaroxaban oder niedermolekularem Heparin (NMH), alternativ orale Antikoagulation. Ergebnisse: Im Rahmen einer AO-Multizenterstudie an 11 europäischen Kliniken wurden zwischen April 2004 und Juni 2005 313Patienten (Durchschnittsalter 80,6Jahre, 77% Frauen, 23% Männer) mit 315 instabilen pertrochantären Frakturen mittels PFNΑ® ("proximal femoral nail antirotation") operativ stabilisiert [24]. Bei 82% handelte es sich um 31-A2-Frakturen, bei 18% um 31-A3-Frakturen. Die durchschnittliche Operationszeit betrug 56min für die A2-Frakturen und 66min für die A3-Frakturen. Die durchschnittliche Liegedauer im Akutspital betrug 12Tage. Bei 72% der Patienten konnte ein Repositions- und Stabilisierungsergebnis erreicht werden, welches eine unmittelbare postoperative Vollbelastung erlaubte. Insgesamt wurden 165Komplikationen beobachtet, 117 davon waren nicht auf das Implantat zu beziehen. 46 operationsbedingte Komplikationen führten zu 28 ungeplanten Re-Operationen (u.a. 7Femurschaftfrakturen, 4 azetabuläre Penetrationen). 56% der Patienten konnten über ein ganzes Jahr nachkontrolliert werden. Nach einem Jahr waren 89% der Frakturen konsolidiert. Die höchsten Komplikationsraten wiesen Frakturen der Morphologie 31-A2.3 sowie Patienten älter als 90Jahre auf. Mit dem PFNA® wurde damit eine mit den Resultaten anderer intra- und extramedullärer Implantate vergleichbare Anzahl operationsbedingter Komplikationen (14,6%) beschriebe

Qualitätssicherung interdisziplinärer Polytraumaversorgung: Möglichkeiten und Grenzen retrospektiver Standarderfassung

Zusammenfassung: Hintergrund: Inwieweit kann die Auswertung standardmäßig erhobener Patienten- und Krankenhausdaten einen Behandlungsvergleich mit anderen Erhebungen gestatten? Material und Methoden: Es wurde eine retrospektive Analyse epidemiologischer und klinisch-technischer Parameter aller Mehrfachverletzten [Injury Severity Score (ISS)>15] einer Zentrumsklinik (n=172; Zeitraum: 01.01.1997-31.12.1999) bezüglich der Ablauforganisation und des Outcome (p74Jahre, Hypotension, initial verminderte Hämoglobin- und Quick-Werte, verminderte Glasgow Coma Scale (GCS) sowie Anzahl erhaltener Blutkonzentrate. Eine Gegenüberstellung der erhobenen Daten mit der zeitgleichen prospektiven Multizenterstudie der Deutschen Gesellschaft für Unfallchirurgie (DGU) bestätigte die Ergebnisse bezüglich des Ablaufs und des Outcome. Schlussfolgerung: Die interdisziplinäre retrospektive Datenauswertung ist unter Fokussierung auf prognoserelevante und routinemäßig erhobene Parameter eine praktikable sowie aussagefähige Alternative zu prospektiven Erfassungen und ermöglicht eine erste qualitative Standortbestimmun

Interdisziplinäres Schockraum-Management unfallchirurgischer Patienten aus der Sicht der Mitarbeitenden

Zusammenfassung: Einleitung: Wir untersuchten, ob die Mitarbeiterbefragung in der Qualitätskontrolle des Schockraum-Managements von Nutzen sein kann. Methode: Konsekutive anonyme schriftliche Befragung (15Fragen, Likert-Skala 1-5) der klinisch Mitarbeitenden aller Schockraumeinsätze mit Verdacht auf Mehrfachverletzung von Juli 2002 bis Dezember 2003 (Anova; p<0,05). Ergebnisse: Bei 171 unfallchirurgischen Einsätzen retournierten 884Beteiligte den Antwortbogen. Die Beobachtungen der Mitarbeitenden hingen signifikant von der jeweiligen Schockraumsituation ab. Am meisten kritisiert wurden das Zeitmanagement und die eigene Ausbildung (Likert-Skala <4). Leitende- und Oberärzte bewerteten ihren Ausbildungsstand besser als Assistenzärzte und hatten häufiger einen ATLS®-Kurs absolviert (p<0,001). Es fanden sich signifikante systematische Unterschiede in den Beurteilungen, z.B. je nach Fachdisziplin der Antwortenden. Schlussfolgerung: Unser Fragebogen erwies sich als gut diskriminierendes Instrument und kann somit die Erfassung klinischer Parameter im Qualitätsmanagement der Schockraumphase sinnvoll ergänzen. Vor einer breiteren Anwendung werden allerdings zusätzliche Validierungs- und Korrelationsuntersuchungen benötig

Combining physics-based and data-driven models: advancing the frontiers of research with scientific machine learning

Scientific Machine Learning (SciML) is a recently emerged research field which combines physics-based and data-driven models for the numerical approximation of differential problems. Physics-based models rely on the physical understanding of the problem at hand, subsequent mathematical formulation, and numerical approximation. Data-driven models instead aim to extract relations between input and output data without arguing any causality principle underlining the available data distribution. In recent years, data-driven models have been rapidly developed and popularised. Such a diffusion has been triggered by a huge availability of data (the so-called big data), an increasingly cheap computing power, and the development of powerful Machine Learning (ML) algorithms. SciML leverages the physical awareness of physics-based models and, at the same time, the efficiency of data-driven algorithms. With SciML, we can inject physics and mathematical knowledge into ML algorithms. Yet, we can rely on data-driven algorithms' capability to discover complex and nonlinear patterns from data and improve the descriptive capacity of physics-based models. After recalling the mathematical foundations of digital modelling and ML algorithms, and presenting the most popular ML architectures, we discuss the great potential of a broad variety of SciML strategies in solving complex problems governed by Partial Differential Equations (PDEs). Finally, we illustrate the successful application of SciML to the simulation of the human cardiac function, a field of significant socio-economic importance that poses numerous challenges on both the mathematical and computational fronts. The corresponding mathematical model is a complex system of nonlinear ordinary and PDEs describing the electromechanics, valve dynamics, blood circulation, perfusion in the coronary tree, and torso potential. Despite the robustness and accuracy of physics-based models, certain aspects, such as unveiling constitutive laws for cardiac cells and myocardial material properties, as well as devising efficient reduced-order models to dominate the extraordinary computational complexity, have been successfully tackled by leveraging data-driven models

Influence of combined impact and cyclic loading on the overall fatigue life of forged steel, EA4T

The performance of forged steel, EA4T, used in rail industry, under simulated in service conditions, i.e. combined impact - cyclic loading, was investigated through a comprehensive experimental programme. The standard Paris-Erdogan fatigue design curve parameters, m and C, were calibrated to account for the effect of the impact component of loading. A minimum threshold for impact load component, identified in the experiments, was also incorporated in the proposed empirical model. Comparison with experimental findings indicated that this “modified” Fatigue design curve could predict the fatigue life of pre impact loaded specimens with sufficient accuracy. It was therefore suggested that the modified model may be used as a novel design tool for predicting the overall fatigue life of components made of this material under the specified combined impact and fatigue loading conditions.Publisher Statement: The final publication is available at Springer via http://dx.doi.org/10.1007/s12206-016-0923-

Taking a hard line with biotemplating: cobalt-doped magnetite magnetic nanoparticle arrays.

Rapid advancements made in technology, and the drive towards miniaturisation, means that we require reliable, sustainable and cost effective methods of manufacturing a wide range of nanomaterials. In this bioinspired study, we take advantage of millions of years of evolution, and adapt a biomineralisation protein for surface patterning of biotemplated magnetic nanoparticles (MNPs). We employ soft-lithographic micro-contact printing to pattern a recombinant version of the biomineralisation protein Mms6 (derived from the magnetotactic bacterium Magnetospirillum magneticum AMB-1). The Mms6 attaches to gold surfaces via a cysteine residue introduced into the N-terminal region. The surface bound protein biotemplates highly uniform MNPs of magnetite onto patterned surfaces during an aqueous mineralisation reaction (with a mean diameter of 90 ± 15 nm). The simple addition of 6% cobalt to the mineralisation reaction maintains the uniformity in grain size (with a mean diameter of 84 ± 14 nm), and results in the production of MNPs with a much higher coercivity (increased from ≈156 Oe to ≈377 Oe). Biotemplating magnetic nanoparticles on patterned surfaces could form a novel, environmentally friendly route for the production of bit-patterned media, potentially the next generation of ultra-high density magnetic data storage devices. This is a simple method to fine-tune the magnetic hardness of the surface biotemplated MNPs, and could easily be adapted to biotemplate a wide range of different nanomaterials on surfaces to create a range of biologically templated devices

In Vitro Aging of Human Skin Fibroblasts: Age-Dependent Changes in 4-Hydroxynonenal Metabolism

Evidence suggests that the increased production of free radicals and reactive oxygen species lead to cellular aging. One of the consequences is lipid peroxidation generating reactive aldehydic products, such as 4-hydroxynonenal (HNE) that modify proteins and form adducts with DNA bases. To prevent damage by HNE, it is metabolized. The primary metabolic products are the glutathione conjugate (GSH-HNE), the corresponding 4-hydroxynonenoic acid (HNA), and the alcohol 1,4-dihydroxynonene (DHN). Since HNE metabolism can potentially change during in vitro aging, cell cultures of primary human dermal fibroblasts from several donors were cultured until senescence. After different time points up to 30 min of incubation with 5 \ub5M HNE, the extracellular medium was analyzed for metabolites via liquid chromatography coupled with electrospray ionization mass spectrometry (LC/ESI-MS). The metabolites appeared in the extracellular medium 5 min after incubation followed by a time-dependent increase. But, the formation of GSH-HNL and GSH-DHN decreased with increasing in vitro age. As a consequence, the HNE levels in the cells increase and there is more protein modification observed. Furthermore, after 3 h of incubation with 5 \ub5M HNE, younger cells showed less proliferative capacity, while in older cells slight increase in the mitotic index was noticed

Collective Awareness for Abnormality Detection in Connected Autonomous Vehicles

The advancements in connected and autonomous vehicles in these times demand the availability of tools providing the agents with the capability to be aware and predict their own states and context dynamics. This article presents a novel approach to develop an initial level of collective awareness (CA) in a network of intelligent agents. A specific collective self-awareness functionality is considered, namely, agent-centered detection of abnormal situations present in the environment around any agent in the network. Moreover, the agent should be capable of analyzing how such abnormalities can influence the future actions of each agent . Data-driven dynamic Bayesian network (DBN) models learned from time series of sensory data recorded during the realization of tasks (agent network experiences) are here used for abnormality detection and prediction. A set of DBNs, each related to an agent , is used to allow the agents in the network to reach synchronously aware possible abnormalities occurring when available models are used on a new instance of the task for which DBNs have been learned. A growing neural gas (GNG) algorithm is used to learn the node variables and conditional probabilities linking nodes in the DBN models; a Markov jump particle filter (MJPF) is employed for state estimation and abnormality detection in each agent using learned DBNs as filter parameters. Performance metrics are discussed to asses the algorithm’s reliability and accuracy. The impact is also evaluated by the communication channel used by the network to share the data sensed in a distributed way by each agent of the network. The IEEE 802.11p protocol standard has been considered for communication among agents. Performances of the DBN-based abnormality detection models under different channel and source conditions are discussed. The effects of distances among agents and of the delays and packet losses are analyzed in different scenario categories (urban, suburban, and rural). Real data se..