Primary Cementless Bipolar Long Stem Hemiarthroplasty for Unstable Osteoporotic Intertrochanteric Fracture in the Elderly Patients

BACKGROUND: Intertrochanteric fracture (ITF) is a major part of fracture in femoral head fracture. 95% of ITF are found in elderly patients. Osteosynthese is the preferred method of choice. However, elderly patients had osteoporotic, combined with many of chronic disease conditions that increase the rate of osteosynthese failure. Hemiarthroplasty bipolar long-stem is a surgical method that helps patients relieve pain, facilitate early rehabilitation, limit long-term complications, and improve quality of life for patients. AIM: The aim of our study is to evaluate the clinical of result of primary cementless bipolar long stem hemiarthroplasty in treatment for unstable ITF in the elderly patients who have severe osteoporosis. METHODS: Between 01/2016 and 12/2017, 35 patients with ITF type A2.2 and A2.3 (AO) were included in our prospective study. These patients were over 70 years old and treatment by hemiarthroplasty cementless long stem at E hospital and Saint Paul hospital by one group surgeons. RESULTS: Mean age of studied subjects was 84.29 ± 6.17, the lowest was 71, the highest was 96; ratio male/female was 1/4. Follow-up of 35 patients for at least 6 months showed 88.6% caused by a low-energy injury; Average rehabilitation time was 4.63 ± 1.7 days. The average Harris point at the end was 90.4 ± 4.72. CONCLUSION: Primary cementless bipolar long stem hemiarthroplasty is one of good choices in treatment unstable ITF in elderly patients with severe osteoporosis helped patients improve the quality of life

Creating Fatigue Curve for Steel Machine Elements Using Fatigue Test Method with Gradually Increasing Stress Amplitude

In order to create a fatigue curve, the traditional fatigue test method is applied to specimens using a cyclic stress with constant amplitude. However, this method has disadvantages such as the experimental results could not be used because of specimens broken before reaching the expected stress amplitude, or the tests may be stopped before specimen broken because of limitation of time. To overcome this hurdle of the traditional method, a new experimental method using cyclic stress with gradually increasing amplitude was proposed to build the fatigue curve for steel machine elements

Preliminary Result of Arthroscopic Anterior Cruciate Ligament Reconstruction Using Anterior Half of Peroneus Longus Tendon Autograft

BACKGROUND: Anthroscopic anterior cruciate ligament reconstruction is one of the most successful operations in sports medicine. At present, ligament autografts have been the best method due to good histocompatibility, rapid healing, no cross-contamination, and low cost of treatment. However, autografts do not have infinite amount and are also not always feasible. Anterior half of peroneus longus tenden autograft is likely to become a source of autograft with many advantages. This study aims to evaluate the clinical outcomes of anthroscopic anterior cruciate ligament reconstruction using anterior half of peroneus longus tendon autograft (AHPLT). AIM: To evaluate the initial outcome of ACL reconstruction arthroscopy by anterior half of peroneus longus tendon. METHODS: This is a prospective non-controlled case series. RESULTS: A prospective study on 30 patients (from 9 / 2016 to 01 / 2019) had both ACL and MCL injury who had operated ACL reconstruction using anterior half of peroneus longus tendon autograft (AHPLT) at Department of General Orthopaedic and Trauma, Viet Duc hospital. Our outcome: the year average 35.4 ys, the rate of ACL rupture combined with meniscus injury was 40%. The average diameter AHPLT autograft is 7.0 mm. The function Lysholm scores improved from 59 to 94.27 postoperative 6 months. No difference beetwen the AOFAS scale of preoperative and postoperative. CONCLUSION: Peroneus longus tendon is recommended to be a safe and practical autograft resource for anthroscopic anterior cruciate ligament reconstruction

Achieving Better Regret against Strategic Adversaries

We study online learning problems in which the learner has extra knowledge about the adversary's behaviour, i.e., in game-theoretic settings where opponents typically follow some no-external regret learning algorithms. Under this assumption, we propose two new online learning algorithms, Accurate Follow the Regularized Leader (AFTRL) and Prod-Best Response (Prod-BR), that intensively exploit this extra knowledge while maintaining the no-regret property in the worst-case scenario of having inaccurate extra information. Specifically, AFTRL achieves $O(1)$ external regret or $O(1)$ \emph{forward regret} against no-external regret adversary in comparison with $O(\sqrt{T})$ \emph{dynamic regret} of Prod-BR. To the best of our knowledge, our algorithm is the first to consider forward regret that achieves $O(1)$ regret against strategic adversaries. When playing zero-sum games with Accurate Multiplicative Weights Update (AMWU), a special case of AFTRL, we achieve \emph{last round convergence} to the Nash Equilibrium. We also provide numerical experiments to further support our theoretical results. In particular, we demonstrate that our methods achieve significantly better regret bounds and rate of last round convergence, compared to the state of the art (e.g., Multiplicative Weights Update (MWU) and its optimistic counterpart, OMWU)

Last Round Convergence and No-Instant Regret in Repeated Games with Asymmetric Information

This paper considers repeated games in which one player has more information about the game than the other players. In particular, we investigate repeated two-player zero-sum games where only the column player knows the payoff matrix A of the game. Suppose that while repeatedly playing this game, the row player chooses her strategy at each round by using a no-regret algorithm to minimize her (pseudo) regret. We develop a no-instant-regret algorithm for the column player to exhibit last round convergence to a minimax equilibrium. We show that our algorithm is efficient against a large set of popular no-regret algorithms of the row player, including the multiplicative weight update algorithm, the online mirror descent method/follow-the-regularized-leader, the linear multiplicative weight update algorithm, and the optimistic multiplicative weight update