Artificial intelligence (AI) is significantly transforming the specialty of allergy by offering novel tools for diagnosis, risk prediction, disease monitoring, and personalized care. This narrative review comprehensively explores key advances in the application of AI techniques-expert systems, machine learning, deep learning, and natural language processing-across various allergic diseases such as asthma, anaphylaxis, atopic dermatitis, food allergy, allergic rhinitis, eosinophilic esophagitis, and drug hypersensitivity reactions. Despite these advancements, several challenges persist, including model interpretability, external validation, and ethical and regulatory barriers. Nonetheless, AI holds strong potential to enhance diagnostic efficiency, therapeutic precision, and patient outcomes. This review presents an updated perspective on the state of the art of AI in allergy, highlighting both its achievements and ongoing challenges.

BackgroundUltrasound is very important in medicine and teaching, but there are not many formal training programs. We also do not know much about what students think. To be good at using ultrasound, one needs to learn technical, thinking, and seeing skills. This is especially true in regional anesthesia (RA), where mistakes in reading images can cause problems. Training with simulations is a safe and good way to learn these skills. Some models are helpful for teaching how to perform procedures using ultrasound. ObjectiveThis study aimed to evaluate the effectiveness, localization time, and success rate of traditional teaching versus a new simulation-based teaching method for RA designed by the investigators among undergraduate medical students. MethodsA prospective, randomized controlled trial was conducted at the University of Salamanca from April 2022 to January 2023. A total of 34 medical students in their fourth to sixth academic years were randomly allocated to either a simulation-based training group using the Haptic Ultrasound Probe or a traditional teaching group. The simulation approach used a realistic probe replica and a software-based ultrasound environment, whereas the traditional method comprised a theoretical lecture and curated audiovisual materials. Two days after training, participants underwent a blinded assessment requiring the identification of peripheral nerve plexuses using an ultrasound device. The primary outcome measured was the successful identification of nerves, and the secondary outcome was the time taken to complete each procedure. Data were analyzed using an intention-to-treat approach. ResultsA total of 34 medical students (fourth to sixth years) were recruited to compare traditional teaching with simulation-based training in ultrasound-guided nerve localization. No statistically significant differences were found in the success rates between the groups. For the interscalene approach, the traditional teaching group achieved a 100% (17/17) success rate compared to 82% (14/17) in the simulation group (P=.07). The time to task completion was similar across most procedures. In the sciatic nerve division, the traditional teaching group was significantly faster, with a mean time of 42.4 (SD 39.5) seconds (P=.02). The regression models showed no significant interaction between the intervention type and academic year. Both teaching methods had positive educational impacts. ConclusionsSimulation-based learning effectively supports competency acquisition in RA and offers a safe, scalable alternative to traditional methods. Its integration into medical curricula may standardize training, improve skill consistency, and enhance patient safety. Further multicenter studies with larger, diverse cohorts are needed to validate these benefits and guide implementation in medical education.

IntroductionUltrasonography (US) plays a central role in modern diagnostic and interventional medicine, particularly in the management of facet-origin chronic low back pain, a highly prevalent condition in industrialized societies. However, its clinical effectiveness depends largely on the level of specialist training, requiring advanced skills in probe manipulation, sonoanatomy interpretation, brain-hand-eye coordination, and safe planning of interventional procedures. This work presents the development of a training simulator for ultrasound-guided treatment of lumbar facet syndrome; the simulator is implemented within a modular learning framework designed to support the flexible and efficient creation of procedure-specific simulators.MethodsThe developed simulator integrates a physical replica of an ultrasound probe, enabling trainees to practice realistic handling. Probe movements performed by the trainee along the scan path are continuously tracked and mapped to corresponding ultrasound images and videos, previously acquired by clinical experts from a real subject and displayed in real time on a computer screen. For interventional planning, a virtual syringe-and-needle component allows trainees to simulate needle orientation and insertion depth, with relevant anatomical structures highlighted as visual learning aids.ResultsA validation study was conducted involving 18 final-year medical students using an ad hoc questionnaire addressing usability, realism, learning support, and overall training experience. The results demonstrate a high level of student acceptance and a positive perceived impact on the acquisition of skills related to ultrasound-guided exploration and interventional planning. Most students reported accelerated skill acquisition in US examination (89% very satisfied, 11% satisfied) and high motivation (83% very satisfied, 17% satisfied). Overall performance and the likelihood of recommending the simulator received the highest rating from all participants (100%).DiscussionFrom the perspective of students, the simulator provides a realistic and supportive learning experience, particularly due to the realism of the physical probe replica, the quality of the graphical user interface, and the guided learning process. From the perspective of instructors, the effectiveness of the simulator depends on the quality of the learning resources and the scope of the training cases. Although the preparation and curation of high-quality ultrasound datasets and annotations remains time-consuming, the framework significantly facilitates and adds flexibility to the development of new case studies. This positions the approach as a valuable complementary training resource, helping to bridge the gap between theoretical instruction and supervised clinical practice in ultrasound-guided procedures.

In recent years, the field of robotics has undergone an exponential evolution, and the use of mobile robots has become increasingly common in various sectors of society, such as industry, medicine, agriculture and logistics. As a result, more and more developers are opting for the use of tools or resources that enable agile software development. One of the most prominent and widely used is the Robot Operating System (ROS) framework. In this article, a detailed analysis will be made of the different versions of ROS, from the first versions to the most recent ones. Furthermore, the process of integrating a mobile robot into ROS 1 and ROS 2 versions will be addressed. The process followed will be indicated, starting from the selection of main components, such as perception, control, software resources and motion planning. In this process, different packages, repositories and tools available in ROS that facilitate this integration will be explored.

In an attempt to provide autonomy and mobility to a visually impaired per-on through an assisted system, such as in this case the robotic prototype, which integrates electronic devices with a system of obstacle detection mechanisms, information transfer, and communication, it is necessary for the functionality of all devices to be within an accepted minimum operational range. In other words, their efficiency (of the sensors) should be more accurate than achieving an approximate estimation. The aforementioned implies discarding the use of the HC-SR04 sensor and implementing the VL53L0X laser sensor instead. In order to demonstrate the validation of both sensors, they will be characterized through measurement tests. These tests, conducted to select the sensors to be used in the prototype construction, will show the need for precision to guarantee safety in the implementation of the proposed model (prototype). A detailed description of the tests that will be carried out to obtain experimental data on the different sensors will be presented, followed by an analysis of the results. This analysis will include data that reveals interesting details and provides demonstrable values.

This work proposes the development of a thermosensitive local drug release system based on Polaxamer 407, also known as Pluronic (R) F-127 (PF-127), Gellan Gum (GG) and the inclusion complex Sulfobutylated-beta-cyclodextrin (CD) with Farnesol (FOH). Rheological properties of the hydrogels and their degradation were studied. Ac-cording to the rheological results, a solution of 20% w/v of PF-127 forms a strong gel with a gelling temperature of about 25 degrees C (storage modulus of 15,000 Pa). The addition of the GG increased the storage modulus (optimal concentration of 0.5 % w/v) twofold without modifying the gelling temperature. Moreover, including 0.5% w/v of GG also increased 6 times the degradation time of the hydrogel. Regarding the inclusion complex, the addition of free CD decreased the viscosity and the gel strength since polymer chains were included in CD cavity without affecting the gelling temperature. Contrarily, the inclusion complex CD-FOH did not significantly modify any property of the formulation because the FOH was hosted in the CD. Furthermore, a mathematical model was developed to adjust the degradation time. This model highlights that the addition of the GG decreases the number of released chains from the polymeric network (which coincides with an increase in the storage modulus) and that the free CD reduces the degradation rate, protecting the polymeric chains. Finally, FOH release was quantified with a specific device, that was designed and printed for this type of system, observing a sustainable drug release (similar to FOH aqueous solubility, 8 mu M) dependent on polymer degradation.

Ultrasound imaging (USI) has become a disruptive element in medicine. To achieve a safer USI application, professionals need sound practical training. This chapter presents the keynotes in the development of a teaching-learning ecosystem, where students (future specialist physicians) and teachers participate, with the concrete educational contents. The ecosystem is supported by a LMS that includes recourses and activities for collaboration, implemented by a multidisciplinary team of health and ICT experts. Practical skills are acquired through a computer-based simulator for the training of the USI-based interventional procedures that the student can handle autonomously at any time and is highly realistic. As a main result, the ecosystem is applied to a real experience, focusing on the treatment of spasticity by botulinum toxin infiltration. Learning outcomes have been evaluated through academic grades and a survey of students who have participated in a specialist course.

Beta-lactam (BL) drugs are the antibiotics most prescribed worldwide due to their broad spectrum of action. They are also the most frequently implied in hypersensitivity reactions with a known specific immunological mechanism. Since the commercialization of benzylpenicillin, allergic reactions have been described; over the years, other new BL drugs provided alternative treatments to penicillin, and amoxicillin is now the most prescribed BL in Europe. Diagnosis of BL allergy is mainly based on skin tests and drug provocation tests, defining different sensitization patterns or phenotypes. In this study, we evaluated 619 patients with a confirmed diagnosis of BL-immediate allergy during the last 25 years, using the same diagnostic procedures with minor adaptations to the successive guidelines. The initial eliciting drug was benzylpenicillin, which changed to amoxicillin with or without clavulanic acid and cephalosporins in recent years. In skin tests, we found a decrease in sensitivity to major and minor penicillin determinants and an increase in sensitivity to amoxicillin and others; this might reflect that the changes in prescription could have influenced the sensitization patterns, thus increasing the incidence of specific reactions to side-chain selective reactions.

Cefazolin 15 Urticaria (-) (-) (+) ND 2 F 46 Cefazolin 60 Urticaria (+) (-) (-) ND 3 F 50 Cefazolin 10 Anaphylaxis (-) (-) (+) ND 4 F 80 Cefazolin UK Anaphylaxis (-) (+) (+) ND 5 F 48 Cefazolin 2 Urticaria (-) (-) (+) (-) Cefuroxime AX/cefuroxime/meropenem 6 F 53 Cefazolin 5 Urticaria (-) (-) (+) (-) Cefuroxime AX/cefuroxime 7 F 34 Cefazolin 1 Anaphylaxis (-) (-) (-) † (-) Cefuroxime AX/cefuroxime 8 F 78 Cefazolin 1 Urticaria (-) (-) (+) (-) Cefuroxime AX/cefuroxime 9 F 62 Cefazolin 15 Urticaria (-) (-) (+) ND 10 M 55 Cefazolin 5 Urticaria (-) (-) (+) (-) Cefuroxime 11 F 53 Cefazolin 5 Urticaria (-) (-) (+) (-) Cefuroxime AX/cefuroxime 12 F 43 Cefazolin 50 Anaphylaxis (-) (-) (+) (-) Cefuroxime AX/cefuroxime 13 M 35 Cefazolin 15 Urticaria (-) (-) (+) (-) Cefuroxime AX/meropenem 14 M 73 Cefazolin 15 Urticaria (-) (-) (-) † (-) Cefuroxime AX/meropenem 15 M 51 Cefazolin 5 Anaphylaxis (-) (-) (+) (-) Cefuroxime 16 F 30 Cefazolin UK Urticaria (-) (-) (+) (-) Cefuroxime AX/cefuroxime 17 F 70 Cefazolin 5 Anaphylaxis (-) (-) (+) (-) Cefuroxime 18 M 70 Cefazolin 1 Urticaria (-) (-) (+) (-) Cefuroxime/ceftriaxone AX/cefuroxime/ceftriaxone 19 M 30 Cephalexin 20 Urticaria (-) (-) (-) † (-) Cefuroxime Cefuroxime 20 M 69 Cefadroxil 22 Urticaria (-) (+) ND (-) Cefazolin 21 F 40 Cefadroxil 30 Urticaria (-) (-) (-) † (-) Cefazolin 22 F 42 Cefadroxil 45 Urticaria (-) (-) (-) † (-) Cefazolin 23 F 62 Cephalothin 15 Anaphylaxis (+) (+) (+) (-) Cefuroxime (+) Cefotaxime 24 F 37 Cefaclor 20 Anaphylaxis (-) (-) ND (+) Cefotaxime/ceftriaxone (-) Cefazolin 25 F 17 Cefaclor 120 Urticaria (-) (-) (-) † (-) Cefazolin/cefuroxime/ceftriaxone 26 F 16 Cefaclor 180 Urticaria (+) (+) (+)* (-) Cefuroxime 27 F 15 Cefaclor 30 Anaphylaxis (-) (-) (-) † (-) Cefazolin/Cefuroxime AX/meropenem 28 F 46 Cefaclor 30 Urticaria (+) (+) ND (-) Cefuroxime/ceftriaxone 29 M 57 Cefonicid UK Anaphylaxis (-) (-) (-) † (-) Cefazolin

Within the framework of Industry 4.0, robotics is experiencing a rapprochement between humans and robots. To achieve this, the barriers that have historically separated humans from industrial robots are being eliminated with the main objective of increasing performance by taking advantage of the capabilities in which each one excels. The success of collaborative robotics is possible once safety is ensured, adding the need and importance of a proper human-robot communication interface. In this context, there are many safety techniques in current collaborative robots, but these are not sufficient to guarantee high industrial production and quality products. Non-verbal communication using the hands is one of the most widely used techniques in the industrial environment for the exchange of human-human information. Thus, this work presents a safety solution based on the creation of a new layer that allows the avoidance of human-robot collisions and includes a robust and safe human-robot communication interface. Advanced automatic learning and computer vision techniques have been used in its development, which was experimental analyses and validated in a co-work scenario involving an industrial collaborative robot and an operator.

BACKGROUND: An accurate diagnosis of beta-lactam (BL) allergy improves the use of antibiotics, increases patients' safety, and reduces costs to health systems. Nevertheless, it requires skin and drug provocation tests, which are time-consuming and put the patient at risk. Furthermore, allergy testing is not available in circumstances such as the urgent need for antibiotic therapy. OBJECTIVE: To evaluate the usefulness of an artificial neural network (ANN) in the prediction of hypersensitivity to BLs, and compare it with logistic regression (LR) analysis. METHODS: In a single-center study, 656 patients evaluated for BL allergy between 1994 and 2000 were retrospectively analyzed, and the data were used to construct an ANN. The ANN predictive capabilities were compared with LR and then prospectively evaluated in 615 patients who underwent BL evaluation between 2011 and 2017. RESULTS: A total of 1271 patients were evaluated. All patients had a definite diagnosis as allergic or nonallergic to BL. The prospective sample showed a lower percentage of patients with allergy than the retrospective sample (20.7% vs 25.8%; P = .018). In the retrospective and prospective series, the ANN reached a sensitivity of 89.5% and 81.1%, a specificity of 86.1% and 97.9%, a positive predictive value of 82.1% and 91.1%, and a negative predictive value of 92.1% and 95.2%, respectively. The ANN's performance was far superior to that of the LR, whose best performance reached a sensitivity of 31.9% and a specificity of 98.8%. CONCLUSIONS: This ANN demonstrated a superior performance than the LR in predicting BL hypersensitivity without misdiagnosing severe allergic reactions. The ANN could be a helpful tool to classify the reaction risk, particularly in the identification of low-risk patients, in which an open challenge could be done to delabel patients. (C) 2020 American Academy of Allergy, Asthma & Immunology

The acceptance of a food product by the consumer depends, as the most important factor, on its sensory properties. Therefore, it is clear that the food industry needs to know the perceptions of sensory attributes to know the acceptability of a product. There exist procedures that systematically allows measurement of these property perceptions that are performed by professional panels. However, systematic evaluations of attributes by these tasting panels, which avoid the subjective character for an individual taster, have a high economic, temporal and organizational cost. The process is only applied in a sampled way so that its result cannot be used on a sound and complete quality system. In this paper, we present a method that allows making use of a non-destructive measurement of physical–chemical properties of the target product to obtain an estimation of the sensory description given by QDA-based procedure. More concisely, we propose that through Artificial Neural Networks (ANNs), we will obtain a reliable prediction that will relate the near-infrared (NIR) spectrum of a complete set of cheese samples with a complete image of the sensory attributes that describe taste, texture, aspect, smell and other relevant sensations.

In this paper, we present a new procedure to solve the global localization of mobile robots called Environmental Stimulus Localization (ESL). We propose that the presence of common facts on the environment around the robot can be considered as stimuli for the procedure. The robust performance of our approach is supported by two concurrent particle filters. A primary particle filter estimates and tracks the robot position, while a secondary filter is fired by environmental stimuli, helps to reduce the influence of measurement errors and allows an earlier recovery from localization failures. We have successfully used this method in a 5000 m 2 real indoor environment using as inputs the available environment information from a Geographical Information System (GIS) map, the robot's odometry and the output of an algorithm for the perception of facts from the environment. We present a case study and the result of different tests, showing the performance of our method under the influence of errors in real applications.

As a consequence of the huge development of IMU (Inertial Measurement Unit) sensors based on MEMS (Micro-Electromechanical Systems), innovative applications related to the analysis of human motion are now possible. In this paper, we present one of these applications: a portable platform for training in Ultrasound Imaging-based musculoskeletal (MSK) exploration in rehabilitation settings. Ultrasound Imaging (USI) in the diagnostic and treatment of MSK pathologies offers various advantages, but it is a strongly operator-dependent technique, so training and experience become of fundamental relevance for rehabilitation specialists. The key element of our platform is a replica of a real transducer (HUSP-Haptic US Probe), equipped with MEMS based IMU sensors, an embedded computing board to calculate its 3D orientation and a mouse board to obtain its relative position in the 2D plane. The sensor fusion algorithm used to resolve in real-time the 3D orientation (roll, pitch and yaw angles) of the probe from accelerometer, gyroscope and magnetometer data will be presented. Thanks to the results obtained, the integration of the probe into the learning platform allows a haptic sensation to be recreated in the rehabilitation trainee, with an attractive performance/cost ratio.

The need for continuous training in our current society in order to keep up to date knowledge, together with the low availability of hours and the low possibility of face-to-face of professionals, makes it very difficult to learn through university models of classroom teaching. Because of this, the Virtual Learning Environment presented in this paper is an innovative educational solution with which students will be trained with a high realism, accessibility and availability in techniques of spasticity treatment.

Nowadays, one of the most commonly used techniques for the approach to spasticity is the ultrasound-guided infiltration of botulinum toxin, because it has the most advantages. In order to medical professionals can properly use this technique, they need training and education. One of the ways to train and teach professionals is through simulators, because it is a safe and unlimited technique that makes the students' learning tasks much easier. In this paper is presented a screen-based computer simulator that will allow professionals to receive an autonomous training in the technique of ultrasound-guided Spasticity treatment. The simulation platform will guide students in learning all the tasks involved in the treatment of Spasticity by infiltration of Botulinum Toxin.

The iberian ham is a high valued product, due to this fact, it is very important to offer to the costumer a high quality food product and to ensure its organoleptic properties. Producers have to evaluate, periodically, its sensorial attributes by a professional tasting panel. Due to high elevated organizational and economics costs, in addition to, the sensory fatigue and the subjectivity of the panel members, only a few product lots are sam-

Nowadays, a major challenge in the development of advanced robotic systems is the creation of complex missions for groups of robots, with two main restrictions: complex programming activities not needed and the mission configuration time should be short (e.g. Urban Search And Rescue). With these ideas in mind, we analysed several robotic development environments, such as Robot Operating System (ROS), Open Robot Control Software (OROCOS), MissionLab, Carnegie Mellon Robot Navigation Toolkit (CARMEN) and Player/Stage, which are helpful when creating autonomous robots. MissionLab provides high-level features (automatic mission creation, code generation) and a graphical mission editor that are unavailable in other significant robotic development environments. It has however some weaknesses regarding its map-based capabilities. Creating, managing and taking advantage of maps for localization and navigation tasks are among CARMEN's most significant features. This fact makes the integration of MissionLab with CARMEN both possible and interesting. This article describes the resulting robotic development environment, which makes it possible to work with several robots, and makes use of their map-based navigation capabilities. It will be shown that the proposed platform solves the proposed goal, that is, it simplifies the programmer's job when developing control software for robot teams, and it further facilitates multi-robot deployment task in mission-critical situations.

Nowadays, accurate maps from mostly anywhere in the world can be obtained for free, with the exception of indoor spaces. However, the evidence seems to suggest that in the next few years indoor maps will be more and more available for anyone. Thus, profiting from the idea of easily obtainable indoor maps, we present a novel approach for real-time mobile robot localization that focuses on spatial reasoning at a high abstraction level. In order to manage and query existing indoor spatial models, we rely on the power of Geographic Information Systems (GIS) and spatial databases. Moreover, to extract symbolic information from the environment, we have developed a door detection system that fuses 2D laser and vision data. We have integrated these two ideas into an extended Kalman filter localization framework. Our proposal has been implemented and tested through autonomous navigation missions in real-world scenarios. Extensive experimental results are provided, which show robustness and accuracy concerning both door detection and localization.