Supermicrosurgery with perforator-to-perforator anastomoses for lower limb reconstructions - A systematic review and meta-analysis.

BACKGROUND: Supermicrosurgical flaps based on perforator-to-perforator microanastomoses have been described for lower limb reconstruction. This approach offers the benefit of raising short pedicles while sparing axial vessels, which effectively enables complex reconstructive techniques in comorbid patients at high risk of reconstructive failure. The aim of our study is to assess the surgical outcomes of perforator-to-perforator based flaps in comparison to conventional free flaps for reconstructions of the lower limb district, through a systematic review of literature and meta-analysis. METHODS: A search on PubMed, Embase, Cohrane, and Web of Science was performed between March-July 2022. No restrictions were placed on study date. Only English manuscripts were assessed. Reviews, short communications, letters, correspondence were excluded after reviewing their references for potentially relevant studies. A Bayesian approach was used to conduct the meta-analysis comparing flap-related outcomes. RESULTS: From 483 starting citations, 16 manuscripts were included for full-text analysis in the review, and three were included in the meta-analysis. Out of 1556 patients, 1047 received a perforator-to-perforator flap. Complications were reported in 119 flaps (11.4%), which included total flap failure in 71 cases (6.8%), partial flap failure in 47 cases (4.5%). Overall flap complications had a HR of 1.41 (0.94-2.11; 95% C.I.). Supermicrosurgical and conventional microsurgical reconstructions were not associated with statistically significant differences (p = .89). CONCLUSION: Our evidence supports the safety of surgical outcomes, with acceptable flap complication rates. Nevertheless, these findings are limited by poor overall quality which must be addressed and used to encourage higher-level evidence in the field.

Dorso-ulnar reverse flow pedicled osseous flap for reconstruction of the distal phalanx of the thumb: A case report.

Reconstruction of osseous defects of the distal phalanx of the thumb is usually addressed with free bone grafts or free vascularized bone flaps. Some reports demonstrated the possibility to harvest an osteo-cutaneous flap in the dorso-ulnar side of the first metacarpal bone with success. In the same manner, no reports are present in the literature in which bone deficits were reconstructed with this flap elevated as an exclusively osseous flap. We report our successful experience with one case of distal phalanx reconstruction of the thumb by mean of the dorso-ulnar reverse flow pedicled osseous flap. The patient was a 45-year-old woman with symptoms related to a cystic bone tumor that involved the entirety of the distal phalanx of the thumb. Flap dimensions were calculated based on x-ray gap measures, which resulted in need of 1.5 × 0.8 × 0.5 cm flap dimensions. An osseous flap was harvested and transposed from the ulnar side of the first metacarpal bone. K-wire fixation was utilized for bone flap stabilization. No complications occurred and excellent functional result was evaluated at 6 months follow-up. In our opinion, the flap may be considered as an alternative to free bone grafts in situations in which perilesional tissues may jeopardize the process of free graft taking and in cases in which free vascularized bone flaps are not feasible for patient or surgeon decision.

Managing the animation deformity in breast reconstruction transposing the implant to a partial prepectoral pocket: Early experience and preliminary results with a new technique.

BACKGROUND: Two-stage implant-based breast reconstruction remains the most commonly used technique for rebuilding a breast. Subpectoral implant placement minimizes complications, such as capsular contracture, implant visibility, malposition, and extrusion. Nevertheless, it is associated with high animation deformity (AD). Prepectoral reconstruction eliminates AD but is subject to a higher risk of implant extrusion and visibility. In this prospective, single-center study we present a new technique aimed to create a new hybrid pocket in which the upper portion of the implant is placed subcutaneously, whereas its inferior pole is still covered by a pectoralis muscle sling reducing implant lower pole visibility and palpability. MATERIAL AND METHODS: In each case, the prosthesis was removed and a new hybrid pocket was created by splitting the muscle into two portions, separating its cranial part from the overlying subcutaneous tissue and anchoring it back to the chest wall. The caudal muscle sling was left adherent to the subcutaneous tissue of the central part of the breast. Patient outcomes were evaluated with a BREAST-Q questionnaire preoperatively and 1 year after surgery. RESULTS: Forty-eight patients with severe postoperative breast animation were enrolled (8 bilateral and 40 unilateral). No major complications occurred. After a 1-year follow-up, the aesthetic and functional satisfaction rate was high and a good implant coverage was achieved. No residual AD of the breast was observed. CONCLUSIONS: Changing the implant placement from the subpectoral to a partially subcutaneous plane, both severe AD and implant extrusion can be avoided, expanding the indications for safe prosthetic breast reconstruction. LEVEL OF EVIDENCE: Level of evidence IV.

Medial femoral condyle free flap for carpo-metacarpal instability following hamate comminute fracture.

Complete reconstruction of the hamate bone has been reported in the literature mostly following cancer excision or avascular necrosis. For the exiguity of the tissue deficit, bone grafting has usually been used as treatment option for its rapidity and easiness to perform, even if a variable amount of bone resorption may occur. In traumatic cases, microbial contamination may jeopardize the success of a well performed bone graft and vascularised bone grafts may represent a better reconstructive option. Here we describe the first case reported in the literature of a patient underwent complete hamate reconstruction following trauma with an osseous medial femoral condyle free flap as vascularized arthrodesis between the capitate and the 4th MTC base, in order to stabilize the 4th and 5th finger and the ulnar carpo-metacarpal joint.

"DO ONE, GET TWO": dual venous drainage of the radial forearm free flap by a single venous anastomosis.

PURPOSE: The radial forearm free flap (RFFF) remains a workhorse in microsurgical reconstruction. Its failure is primarily due to problems with venous drainage; for this reason, controversy on venous anastomosis patterns still exists. This manuscript describes our experience in using a communicating vein to overcome the complications of venous drainage of the RFFF. METHODS: Following a review of the vascular anatomy of the RFFF, we retrospectively review the use of the communicating vein and report our results, with the aim of overcoming the dichotomy "superficial versus deep venous system" and "single versus double anastomosis" and discussing the evidence of advantages in using a single microanastomosis with a communicating vein. RESULTS: Our retrospective review included a total of 123 patients in which a RFFF was performed to reconstruct intraoral defects, performed with a single venous anastomosis using the communicating vein. Four patients (3.25%) required a return to theatre for revision of the venous anastomosis and one case resulted in flap failure due to arterial insufficiency (0.81%). CONCLUSIONS: Our series highlights the constant presence of the communicating vein, although with variations of origin and course that did not preclude the possibility to correctly perform the anastomosis. Advantages of a single microanastomosis with the communicating vein include ease, speed, reliability and versatility in planning the anastomosis. Based on our results, the use of the communicating vein showed comparable and, in some cases, more favourable results when compared to venous anastomotic complications reported in the literature.

Outcomes of a Mini External Fixator System for the Treatment of Unstable Periphyseal Hand Fractures.

Background: Most unstable hand fractures in children are treated by closed methods. If osteosynthesis is required, Kirschner (K)-wires are commonly used, though they carry a risk of injury to the physis. We have been using a mini external fixator system (MEFS) for the treatment of unstable periphyseal fractures of the hand. The aim of this study is to describe the application and report the outcomes of MEFS for the treatment of periphyseal fractures of the hand. Methods: We retrospectively reviewed all the patients with periphyseal fracture of the hand treated with MEFS from March 2010 to December 2019. Data with regard to age, sex, hand dominance, digit and bone injured, mechanism of injury, medical records and related radiographs were collected. Salter-Harris classification was used to classify epiphyseal fractures and the Al-Qattan classification for categorising neck fractures. Range of motion and residual deformity of the affected fingers were evaluated during follow-up and at 3 months postoperatively. Results: Fourteen periphyseal unstable fractures were treated using closed reduction and MEFS. Only one patient with a fracture of the neck of the proximal phalanx of the little finger required revision surgery. No patient had pin site infection or pin loosening and the device was well tolerated by all patients. All fractures united and all the patients recovered a full range of motion at final follow-up. Conclusions: The MEFS is a reasonable alternative for unstable periphyseal fractures with good outcomes and avoids the risk of iatrogenic physeal injury from K-wire fixation. Level of Evidence: Level IV (Therapeutic).

Insect-Inspired Robots: Bridging Biological and Artificial Systems.

This review article aims to address common research questions in hexapod robotics. How can we build intelligent autonomous hexapod robots that can exploit their biomechanics, morphology, and computational systems, to achieve autonomy, adaptability, and energy efficiency comparable to small living creatures, such as insects? Are insects good models for building such intelligent hexapod robots because they are the only animals with six legs? This review article is divided into three main sections to address these questions, as well as to assist roboticists in identifying relevant and future directions in the field of hexapod robotics over the next decade. After an introduction in section (1), the sections will respectively cover the following three key areas: (2) biomechanics focused on the design of smart legs; (3) locomotion control; and (4) high-level cognition control. These interconnected and interdependent areas are all crucial to improving the level of performance of hexapod robotics in terms of energy efficiency, terrain adaptability, autonomy, and operational range. We will also discuss how the next generation of bioroboticists will be able to transfer knowledge from biology to robotics and vice versa.

Echo State Networks for Estimating Exteroceptive Conditions From Proprioceptive States in Quadruped Robots.

We propose a methodology based on reservoir computing for mapping local proprioceptive information acquired at the level of the leg joints of a simulated quadruped robot into exteroceptive and global information, including both the ground reaction forces at the level of the different legs and information about the type of terrain traversed by the robot. Both dynamic estimation and terrain classification can be achieved concurrently with the same reservoir computing structure, which serves as a soft sensor device. Simulation results are presented together with preliminary experiments on a real quadruped robot. They demonstrate the suitability of the proposed approach for various terrains and sensory system fault conditions. The strategy, which belongs to the class of data-driven models, is independent of the robotic mechanical design and can easily be generalized to different robotic structures.

A new model of Hopfield network with fractional-order neurons for parameter estimation.

In this work, we study an application of fractional-order Hopfield neural networks for optimization problem solving. The proposed network was simulated using a semi-analytical method based on Adomian decomposition,, and it was applied to the on-line estimation of time-varying parameters of nonlinear dynamical systems. Through simulations, it was demonstrated how fractional-order neurons influence the convergence of the Hopfield network, improving the performance of the parameter identification process if compared with integer-order implementations. Two different approaches for computing fractional derivatives were considered and compared as a function of the fractional-order of the derivatives: the Caputo and the Caputo-Fabrizio definitions. Simulation results related to different benchmarks commonly adopted in the literature are reported to demonstrate the suitability of the proposed architecture in the field of on-line parameter estimation.

RNN- and LSTM-Based Soft Sensors Transferability for an Industrial Process.

The design and application of Soft Sensors (SSs) in the process industry is a growing research field, which needs to mediate problems of model accuracy with data availability and computational complexity. Black-box machine learning (ML) methods are often used as an efficient tool to implement SSs. Many efforts are, however, required to properly select input variables, model class, model order and the needed hyperparameters. The aim of this work was to investigate the possibility to transfer the knowledge acquired in the design of a SS for a given process to a similar one. This has been approached as a transfer learning problem from a source to a target domain. The implementation of a transfer learning procedure allows to considerably reduce the computational time dedicated to the SS design procedure, leaving out many of the required phases. Two transfer learning methods have been proposed, evaluating their suitability to design SSs based on nonlinear dynamical models. Recurrent neural structures have been used to implement the SSs. In detail, recurrent neural networks and long short-term memory architectures have been compared in regard to their transferability. An industrial case of study has been considered, to evaluate the performance of the proposed procedures and the best compromise between SS performance and computational effort in transferring the model. The problem of labeled data scarcity in the target domain has been also discussed. The obtained results demonstrate the suitability of the proposed transfer learning methods in the design of nonlinear dynamical models for industrial systems.

Stacked autoencoders as new models for an accurate Alzheimer's disease classification support using resting-state EEG and MRI measurements.

OBJECTIVE: This retrospective and exploratory study tested the accuracy of artificial neural networks (ANNs) at detecting Alzheimer's disease patients with dementia (ADD) based on input variables extracted from resting-state electroencephalogram (rsEEG), structural magnetic resonance imaging (sMRI) or both. METHODS: For the classification exercise, the ANNs had two architectures that included stacked (autoencoding) hidden layers recreating input data in the output. The classification was based on LORETA source estimates from rsEEG activity recorded with 10-20 montage system (19 electrodes) and standard sMRI variables in 89 ADD and 45 healthy control participants taken from a national database. RESULTS: The ANN with stacked autoencoders and a deep leaning model representing both ADD and control participants showed classification accuracies in discriminating them of 80%, 85%, and 89% using rsEEG, sMRI, and rsEEG + sMRI features, respectively. The two ANNs with stacked autoencoders and a deep leaning model specialized for either ADD or control participants showed classification accuracies of 77%, 83%, and 86% using the same input features. CONCLUSIONS: The two architectures of ANNs using stacked (autoencoding) hidden layers consistently reached moderate to high accuracy in the discrimination between ADD and healthy control participants as a function of the rsEEG and sMRI features employed. SIGNIFICANCE: The present results encourage future multi-centric, prospective and longitudinal cross-validation studies using high resolution EEG techniques and harmonized clinical procedures towards clinical applications of the present ANNs.

Lyapunov approach to synchronization of chaotic systems with vanishing nonlinear perturbations: From static to dynamic couplings.

Synchronization of chaotic dynamics can be pursued by means of different coupling strategies. Definitely, master-slave coupling represents one of the most adopted solutions, even if it presents some limitations due to the coupling term's selection strategy. In this paper, we investigate the role of different structures of coupling terms on the synchronization properties of master-slave chaotic system configurations. Here, Lyapunov theory for linear systems with nonlinear vanishing perturbations is exploited. The obtained results allow to determine the capability of a static, dynamic, or mixed coupling connection in stabilizing the synchronization manifold, using linear techniques based on the root locus. This knowledge allows to design the coupling structure considering also the synchronization error transient features, which are, here, shown to improve in the presence of higher-order dynamic couplings. A number of cases of study, involving classical chaotic nonlinear systems, show the efficacy and simplicity of the application of the strategy proposed.

Integrative Biomimetics of Autonomous Hexapedal Locomotion.

Despite substantial advances in many different fields of neurorobotics in general, and biomimetic robots in particular, a key challenge is the integration of concepts: to collate and combine research on disparate and conceptually disjunct research areas in the neurosciences and engineering sciences. We claim that the development of suitable robotic integration platforms is of particular relevance to make such integration of concepts work in practice. Here, we provide an example for a hexapod robotic integration platform for autonomous locomotion. In a sequence of six focus sections dealing with aspects of intelligent, embodied motor control in insects and multipedal robots-ranging from compliant actuation, distributed proprioception and control of multiple legs, the formation of internal representations to the use of an internal body model-we introduce the walking robot HECTOR as a research platform for integrative biomimetics of hexapedal locomotion. Owing to its 18 highly sensorized, compliant actuators, light-weight exoskeleton, distributed and expandable hardware architecture, and an appropriate dynamic simulation framework, HECTOR offers many opportunities to integrate research effort across biomimetics research on actuation, sensory-motor feedback, inter-leg coordination, and cognitive abilities such as motion planning and learning of its own body size.

Master-slave synchronization of hyperchaotic systems through a linear dynamic coupling.

The development of synchronization strategies for dynamical systems is an important research activity that can be applied in several different fields from locomotion control of multilimbed structures to secure communication. In the presence of chaotic systems, synchronization is more difficult to accomplish and there are different techniques that can be adopted. In this paper we considered a master-slave topology where the coupling mechanism is realized through a second-order linear dynamical system. This control scheme, recently applied to chaotic systems, is here analyzed in the presence of hyperchaotic dynamics that represent a more challenging scenario. The possibility to reach a complete synchronization and the range of allowable coupling strength is investigated comparing the effects of the dynamical coupling with a standard configuration characterized by a static gain. This methodology is also applied to weighted networks to reach synchronization regimes otherwise not obtainable with a static coupling.

Motor-Skill Learning in an Insect Inspired Neuro-Computational Control System.

In nature, insects show impressive adaptation and learning capabilities. The proposed computational model takes inspiration from specific structures of the insect brain: after proposing key hypotheses on the direct involvement of the mushroom bodies (MBs) and on their neural organization, we developed a new architecture for motor learning to be applied in insect-like walking robots. The proposed model is a nonlinear control system based on spiking neurons. MBs are modeled as a nonlinear recurrent spiking neural network (SNN) with novel characteristics, able to memorize time evolutions of key parameters of the neural motor controller, so that existing motor primitives can be improved. The adopted control scheme enables the structure to efficiently cope with goal-oriented behavioral motor tasks. Here, a six-legged structure, showing a steady-state exponentially stable locomotion pattern, is exposed to the need of learning new motor skills: moving through the environment, the structure is able to modulate motor commands and implements an obstacle climbing procedure. Experimental results on a simulated hexapod robot are reported; they are obtained in a dynamic simulation environment and the robot mimicks the structures of Drosophila melanogaster.

A Fly-Inspired Mushroom Bodies Model for Sensory-Motor Control Through Sequence and Subsequence Learning.

Classification and sequence learning are relevant capabilities used by living beings to extract complex information from the environment for behavioral control. The insect world is full of examples where the presentation time of specific stimuli shapes the behavioral response. On the basis of previously developed neural models, inspired by Drosophila melanogaster, a new architecture for classification and sequence learning is here presented under the perspective of the Neural Reuse theory. Classification of relevant input stimuli is performed through resonant neurons, activated by the complex dynamics generated in a lattice of recurrent spiking neurons modeling the insect Mushroom Bodies neuropile. The network devoted to context formation is able to reconstruct the learned sequence and also to trace the subsequences present in the provided input. A sensitivity analysis to parameter variation and noise is reported. Experiments on a roving robot are reported to show the capabilities of the architecture used as a neural controller.