Bilateral Stress Fractures of Amputated Tibial Stumps in the Setting of Chronic Compartment Syndrome.

Introduction: Limb amputation and subsequent prosthetics lead to significant disturbances in bone residual limb remodeling.

Aim: To familiarize specialists with the possibility of simultaneous bilateral stress fractures of amputation residual limbs resulting from intensive loads and poor-quality prosthetics causing chronic compartment syndrome. A case of bilateral stress fractures of the tibia in a 28-year-old male military serviceman with paired transtibial amputation is presented.

Bone Bridge Transtibial Amputation by an Innovative Technique.

We present a case report of synostosis after transtibial amputation because of distraction regenerate formation after decortication of the lateral surfaces of the tibia and fibula, sequential compression, and distraction using the Ilizarov apparatus. Its advantage is that there is no need to shorten bone. The establishment of distal tibia-fibula synostosis (Ertl) in patients with transtibial amputation has been advocated to improve function and prosthetic wear.

Remodeling of the Fibula Stump After Transtibial Amputation.

Aim: To study the peculiarities of peroneal stump remodelling after transtibial amputation in the process of prosthesis usage.

Material And Methods: A histological study of the ends of the stumps of the fibula in 68 patients was performed. Terms after amputation: 2-8 years.

Vascularization of a bone stump.

Aim To study the character of blood circulation in the bone stump at tight and loose closure of the medullary cavity. Methods Two series of experiments on 39 rabbits with mid-third femoral amputation and muscular plasty were carried out. In the 1st (experimental) series, the bone scapula was closed by thin cortical autograft taken from the epimetaphyseal area, and then the muscles were sutured, and in the 2nd - the scapula was closed by myoplasty only.

Quantum Autoencoders to Denoise Quantum Data.

Entangled states are an important resource for quantum computation, communication, metrology, and the simulation of many-body systems. However, noise limits the experimental preparation of such states. Classical data can be efficiently denoised by autoencoders-neural networks trained in unsupervised manner.

Training deep quantum neural networks.

Neural networks enjoy widespread success in both research and industry and, with the advent of quantum technology, it is a crucial challenge to design quantum neural networks for fully quantum learning tasks. Here we propose a truly quantum analogue of classical neurons, which form quantum feedforward neural networks capable of universal quantum computation. We describe the efficient training of these networks using the fidelity as a cost function, providing both classical and efficient quantum implementations.