Metabolic Bone Disease in preterm newborn: an update on nutritional issues

Osteopenia, a condition characterised by a reduction in bone mineral content, is a common disease of preterm babies between the tenth and sixteenth week of life. Prematurely born infants are deprived of the intrauterine supply of minerals affecting bone mineralization

Autonomous tissue retraction with a biomechanically informed logic based framework

Autonomy in robot-assisted surgery is essential to reduce surgeons’ cognitive load and eventually improve the overall surgical outcome. A key requirement for autonomy in a safety-critical scenario as surgery lies in the generation of interpretable plans that rely on expert knowledge. Moreover, the Autonomous Robotic Surgical System (ARSS) must be able to reason on the dynamic and unpredictable anatomical environment, and quickly adapt the surgical plan in case of unexpected situations. In this paper, we present a modular Framework for Robot-Assisted Surgery (FRAS) in deformable anatomical environments. Our framework integrates a logic module for task-level interpretable reasoning, a biomechanical simulation that complements data from real sensors, and a situation awareness module for context interpretation. The framework performance is evaluated on simulated soft tissue retraction, a common surgical task to remove the tissue hiding a region of interest. Results show that the framework has the adaptability required to successfully accomplish the task, handling dynamic environmental conditions and possible failures, while guaranteeing the computational efficiency required in a real surgical scenario. The framework is made publicly available

Autonomous tissue retraction with a biomechanically informed logic based framework

Autonomy in robot-assisted surgery is essential to reduce surgeons\u2019 cognitive load and eventually improve the overall surgical outcome. A key requirement for autonomy in a safety-critical scenario as surgery lies in the generation of interpretable plans that rely on expert knowledge. Moreover, the Autonomous Robotic Surgical System (ARSS) must be able to reason on the dynamic and unpredictable anatomical environment, and quickly adapt the surgical plan in case of unexpected situations. In this paper, we present a modular Framework for Robot-Assisted Surgery (FRAS) in deformable anatomical environments. Our framework integrates a logic module for task-level interpretable reasoning, a biomechanical simulation that complements data from real sensors, and a situation awareness module for context interpretation. The framework performance is evaluated on simulated soft tissue retraction, a common surgical task to remove the tissue hiding a region of interest. Results show that the framework has the adaptability required to successfully accomplish the task, handling dynamic environmental conditions and possible failures, while guaranteeing the computational efficiency required in a real surgical scenario. The framework is made publicly available

Deliberation in autonomous robotic surgery: a framework for handling anatomical uncertainty

Autonomous robotic surgery requires deliberation, i.e. the ability to plan and execute a task adapting to uncertain and dynamic environments. Uncertainty in the surgical domain is mainly related to the partial pre-operative knowledge about patient-specific anatomical properties. In this paper, we introduce a logic-based framework for surgical tasks with deliberative functions of monitoring and learning. The DEliberative Framework for Robot-Assisted Surgery (DEFRAS) estimates a pre-operative patient-specific plan, and executes it while continuously measuring the applied force obtained from a biomechanical pre-operative model. Monitoring module compares this model with the actual situation reconstructed from sensors. In case of significant mismatch, the learning module is invoked to update the model, thus improving the estimate of the exerted force. DEFRAS is validated both in simulated and real environment with da Vinci Research Kit executing soft tissue retraction. Compared with state-of-the-art related works, the success rate of the task is improved while minimizing the interaction with the tissue to prevent unintentional damage

Autonomous Robotic System for Breast Biopsy With Deformation Compensation

Image-guided biopsy is the most common technique for breast cancer diagnosis. Although magnetic resonance imaging (MRI) has the highest sensitivity in breast lesion detection, ultrasound (US) biopsy guidance is generally preferred due to its non-invasiveness and real-time image feedback during the insertion. In this work, we propose an autonomous robotic system for US-guided biopsy of breast lesions identified on pre-operative MRI. After initial MRI to breast registration, the US probe attached to the robotic manipulator compresses the breast tissues until a pre-determined force level is reached. This technique, known as preloading, will allow to minimize lesion displacement during the needle insertion. Our workflow integrates a deformation compensation strategy based on patient-specific biomechanical model to update the US probe orientation keeping the target lesion on the US image plane during compression. By relying on a deformation model, the proposed system does not require lesion visibility on US. Experimental evaluation is performed to assess the performance of the system on a realistic breast phantom with 15 internal lesions, considering different preloading forces. The deformation compensation strategy allows to improve localization accuracy, and as a consequence final probe positioning, for all considered lesions. Median lesion localization error is 3.3 mm, which is lower than the median lesion radius, when applying a preloading of 2 N, which guarantees both minimal needle insertion error and tissue stress

Mucosal delivery of anti-inflammatory IL-1Ra by sporulating recombinant bacteria

BACKGROUND: Mucosal delivery of therapeutic protein drugs or vaccines is actively investigated, in order to improve bioavailability and avoid side effects associated with systemic administration. Orally administered bacteria, engineered to produce anti-inflammatory cytokines (IL-10, IL-1Ra), have shown localised ameliorating effects in inflammatory gastro-intestinal conditions. However, the possible systemic effects of mucosally delivered recombinant bacteria have not been investigated. RESULTS: B. subtilis was engineered to produce the mature human IL-1 receptor antagonist (IL-1Ra). When recombinant B. subtilis was instilled in the distal colon of rats or rabbits, human IL-1Ra was found both in the intestinal lavage and in the serum of treated animals. The IL-1Ra protein in serum was intact and biologically active. IL-1-induced fever, neutrophilia, hypoglycemia and hypoferremia were inhibited in a dose-dependent fashion by intra-colon administration of IL-1Ra-producing B. subtilis. In the mouse, intra-peritoneal treatment with recombinant B. subtilis could inhibit endotoxin-induced shock and death. Instillation in the rabbit colon of another recombinant B. subtilis strain, which releases bioactive human recombinant IL-1β upon autolysis, could induce fever and eventually death, similarly to parenteral administration of high doses of IL-1β. CONCLUSIONS: A novel system of controlled release of pharmacologically active proteins is described, which exploits bacterial autolysis in a non-permissive environment. Mucosal administration of recombinant B. subtilis causes the release of cytoplasmic recombinant proteins, which can then be found in serum and exert their biological activity in vivo systemically

Biomechanical modelling of probe to tissue interaction during ultrasound scanning

Purpose: Biomechanical simulation of anatomical deformations caused by ultrasound probe pressure is of outstanding importance for several applications, from the testing of robotic acquisition systems to multi-modal image fusion and development of ultrasound training platforms. Different approaches can be exploited for modelling the probe-tissue interaction, each achieving different trade-offs among accuracy, computation time and stability. Methods: We assess the performances of different strategies based on the finite element method for modelling the interaction between the rigid probe and soft tissues. Probe\u2013tissue contact is modelled using (i) penalty forces, (ii) constraint forces, and (iii) by prescribing the displacement of the mesh surface nodes. These methods are tested in the challenging context of ultrasound scanning of the breast, an organ undergoing large nonlinear deformations during the procedure. Results: The obtained results are evaluated against those of a non-physically based method. While all methods achieve similar accuracy, performance in terms of stability and speed shows high variability, especially for those methods modelling the contacts explicitly. Overall, prescribing surface displacements is the approach with best performances, but it requires prior knowledge of the contact area and probe trajectory. Conclusions: In this work, we present different strategies for modelling probe\u2013tissue interaction, each able to achieve different compromises among accuracy, speed and stability. The choice of the preferred approach highly depends on the requirements of the specific clinical application. Since the presented methodologies can be applied to describe general tool\u2013tissue interactions, this work can be seen as a reference for researchers seeking the most appropriate strategy to model anatomical deformation induced by the interaction with medical tools

Različit, ali isti: uz pomoć DNA identifikacije otkrivena iznenađujuća obojenost primjeraka Sredozemnog morskog puža stražnjoškržnjaka (Mollusca: Nudibranchia)

A peculiar eolid nudibranch showing an unknown chromatic array was found in a rocky bottom of Santa Maria al Bagno, in the Salento peninsula, Ionian Sea (Central Mediterranean Sea). This specimen, initially identified as Piseinotecus sp., was observed in situ and photographed while feeding and laying eggs close to individuals belonging to the Mediterranean Piseinotecus soussi. To assess the identity of this unexpected Piseinotecus ‘white morph’, a DNA identification approach was carried out using mitochondrial cytochrome c oxidase subunit I (COI), as it is the molecular marker mostly used to distinguish nudibranchs species. The molecular analysis unambiguously identified this specimen as Piseinotecus soussi and helped to shed lights on the striking intraspecific colour variability characterizing this rare species.Neobični primjerak eolidnog morskog puža stražnjoškržnjaka s dosad nezabilježenim obojenjem pronađen je na stjenovitom dnu lokaliteta Santa Maria al Bagno na poluotoku Salentu u Jonskom moru (središnje Sredozemno more). Ovaj primjerak, prvobitno identificiran kao Piseinotecus sp., promatran je i fotografiran dok se hranio i polagao jaja u blizini jedinki koje pripadaju sredozemnoj vrsti Piseinotecus soussi. Kako bi se otkrio identitet ovog neobičnog „bijelog oblika“ Piseinotecus sp. primjerka, provedena je identifikacija DNA pomoću mitohondrijske podjedinice citokrom c oksidaze I (COI), budući da je to molekularni marker koji se uglavnom koristi za razlikovanje vrsta stražnjoškržnjaka. Molekularna analiza nedvojbeno je identificirala ovaj primjerak kao Piseinotecus soussi i pomogla da se rasvijetli upečatljiva intraspecifična varijabilnost obojenja koja karakterizira ovu rijetku vrstu

Enteral feeding of intrauterine growth restriction preterm infants: theoretical risks and practical implications

Intrauterine growth restriction (IUGR) infants are thought to have impaired gut function after birth secondary to intrauterine redistribution of the blood flow, due to placental insufficiency, with a consequent reduction of gut perfusion. For this reason, infants complicated by IUGR have been considered at higher risk of feeding intolerance. Postnatal evaluation of splanchnic perfusion, through Doppler of the superior mesenteric artery, and of splanchnic oxygenation, through near infrared spectroscopy measurements, may be useful in evaluating the persistence (or not) of the redistribution of blood flow occurred in utero

UnityFlexML: Training Reinforcement Learning Agents in a Simulated Surgical Environment

Sim-to-real Deep Reinforcement Learning (DRL) has shown promising in subtasks automation for surgical robotic systems, since it allows to safely perform all the trial and error attempts needed to learn the optimal control policy. However, a realistic simulation environment is essential to guarantee direct transfer of the learnt policy from the simulated to the real system. In this work, we introduce UnityFlexML, an open-source framework providing support for soft bodies simulation and state-of-the-art DRL methods. We demonstrate that a DRL agent can be successfully trained within UnityFlexML to manipulate deformable fat tissues for tumor exposure during a nephrectomy procedure. Furthermore, we show that the learned policy can be directly deployed on the da Vinci Research Kit, which is able to execute the trajectories generated by the DRL agent. The proposed framework represents an essential component for the development of autonomous robotic systems, where the interaction with the deformable anatomical environment is involved