DistilIQA: Distilling Vision Transformers for no-reference perceptual CT image quality assessment.

No-reference image quality assessment (IQA) is a critical step in medical image analysis, with the objective of predicting perceptual image quality without the need for a pristine reference image. The application of no-reference IQA to CT scans is valuable in providing an automated and objective approach to assessing scan quality, optimizing radiation dose, and improving overall healthcare efficiency. In this paper, we introduce DistilIQA, a novel distilled Vision Transformer network designed for no-reference CT image quality assessment. DistilIQA integrates convolutional operations and multi-head self-attention mechanisms by incorporating a powerful convolutional stem at the beginning of the traditional ViT network. Additionally, we present a two-step distillation methodology aimed at improving network performance and efficiency. In the initial step, a "teacher ensemble network" is constructed by training five vision Transformer networks using a five-fold division schema. In the second step, a "student network", comprising of a single Vision Transformer, is trained using the original labeled dataset and the predictions generated by the teacher network as new labels. DistilIQA is evaluated in the task of quality score prediction from low-dose chest CT scans obtained from the LDCT and Projection data of the Cancer Imaging Archive, along with low-dose abdominal CT images from the LDCTIQAC2023 Grand Challenge. Our results demonstrate DistilIQA's remarkable performance in both benchmarks, surpassing the capabilities of various CNNs and Transformer architectures. Moreover, our comprehensive experimental analysis demonstrates the effectiveness of incorporating convolutional operations within the ViT architecture and highlights the advantages of our distillation methodology.

Rethinking the applicability domain analysis in QSAR models.

Notwithstanding the wide adoption of the OECD principles (or best practices) for QSAR modeling, disparities between in silico predictions and experimental results are frequent, suggesting that model predictions are often too optimistic. Of these OECD principles, the applicability domain (AD) estimation has been recognized in several reports in the literature to be one of the most challenging, implying that the actual reliability measures of model predictions are often unreliable. Applying tree-based error analysis workflows on 5 QSAR models reported in the literature and available in the QsarDB repository, i.e., androgen receptor bioactivity (agonists, antagonists, and binders, respectively) and membrane permeability (highest membrane permeability and the intrinsic permeability), we demonstrate that predictions erroneously tagged as reliable (AD prediction errors) overwhelmingly correspond to instances in subspaces (cohorts) with the highest prediction error rates, highlighting the inhomogeneity of the AD space. In this sense, we call for more stringent AD analysis guidelines which require the incorporation of model error analysis schemes, to provide critical insight on the reliability of underlying AD algorithms. Additionally, any selected AD method should be rigorously validated to demonstrate its suitability for the model space over which it is applied. These steps will ultimately contribute to more accurate estimations of the reliability of model predictions. Finally, error analysis may also be useful in "rational" model refinement in that data expansion efforts and model retraining are focused on cohorts with the highest error rates.

Hybrid Controller Based on Numerical Methods for Chemical Processes with a Long Time Delay.

A hybrid control framework is proposed as an alternative for long time delays in chemical processes. The hybrid approach mixes the numerical methods in an internal mode control (IMC) structure, which uses the particle swarm optimization (PSO) algorithm to improve the adjustment of the controller parameters. Simulation tests are carried out on linear systems of high order and inverse response, both with dominant delay, and tests on a nonlinear process (chemical reactor). The performance of the proposed controller is stable and satisfactory despite nonlinearities in various operating conditions, set-point changes, process disturbances, and modeling errors. In addition, experimental tests were performed on a setup composed of two heaters and two temperature sensors mounted on an Arduino microcontroller-based board called the Temperature Control Laboratory (TCLab), with an additional software delay introduced. The merits and drawbacks of each scheme are analyzed using radar charts, comparing the control methods with different performance measures for set-point and disturbance changes. Furthermore, the new controller uses PSO to improve the tuning parameters.

Complex Networks Analyses of Antibiofilm Peptides: An Emerging Tool for Next-Generation Antimicrobials' Discovery.

Microbial biofilms cause several environmental and industrial issues, even affecting human health. Although they have long represented a threat due to their resistance to antibiotics, there are currently no approved antibiofilm agents for clinical treatments. The multi-functionality of antimicrobial peptides (AMPs), including their antibiofilm activity and their potential to target multiple microbes, has motivated the synthesis of AMPs and their relatives for developing antibiofilm agents for clinical purposes. Antibiofilm peptides (ABFPs) have been organized in databases that have allowed the building of prediction tools which have assisted in the discovery/design of new antibiofilm agents. However, the complex network approach has not yet been explored as an assistant tool for this aim. Herein, a kind of similarity network called the half-space proximal network (HSPN) is applied to represent/analyze the chemical space of ABFPs, aiming to identify privileged scaffolds for the development of next-generation antimicrobials that are able to target both planktonic and biofilm microbial forms. Such analyses also considered the metadata associated with the ABFPs, such as origin, other activities, targets, etc., in which the relationships were projected by multilayer networks called metadata networks (METNs). From the complex networks' mining, a reduced but informative set of 66 ABFPs was extracted, representing the original antibiofilm space. This subset contained the most central to atypical ABFPs, some of them having the desired properties for developing next-generation antimicrobials. Therefore, this subset is advisable for assisting the search for/design of both new antibiofilms and antimicrobial agents. The provided ABFP motifs list, discovered within the HSPN communities, is also useful for the same purpose.

Network Science and Group Fusion Similarity-Based Searching to Explore the Chemical Space of Antiparasitic Peptides.

Antimicrobial peptides (AMPs) have appeared as promising compounds to treat a wide range of diseases. Their clinical potentialities reside in the wide range of mechanisms they can use for both killing microbes and modulating immune responses. However, the hugeness of the AMPs' chemical space (AMPCS), represented by more than 1065 unique sequences, has represented a big challenge for the discovery of new promising therapeutic peptides and for the identification of common structural motifs. Here, we introduce network science and a similarity searching approach to discover new promising AMPs, specifically antiparasitic peptides (APPs). We exploited the network-based representation of APPs' chemical space (APPCS) to retrieve valuable information by using three network types: chemical space (CSN), half-space proximal (HSPN), and metadata (METN). Some centrality measures were applied to identify in each network the most important and nonredundant peptides. Then, these central peptides were considered as queries (Qs) in group fusion similarity-based searches against a comprehensive collection of known AMPs, stored in the graph database StarPepDB, to propose new potential APPs. The performance of the resulting multiquery similarity-based search models (mQSSMs) was evaluated in five benchmarking data sets of APP/non-APPs. The predictions performed by the best mQSSM showed a strong-to-very-strong performance since their external Matthews correlation coefficient (MCC) values ranged from 0.834 to 0.965. Outstanding MCC values (>0.85) were attained by the mQSSM with 219 Qs from both networks CSN and HSPN with 0.5 as similarity threshold in external data sets. Then, the performance of our best mQSSM was compared with the APPs prediction servers AMPDiscover and AMPFun. The proposed model showed its relevance by outperforming state-of-the-art machine learning models to predict APPs. After applying the best mQSSM and additional filters on the non-APP space from StarPepDB, 95 AMPs were repurposed as potential APP hits. Due to the high sequence diversity of these peptides, different computational approaches were applied to identify relevant motifs for searching and designing new APPs. Lastly, we identified 11 promising APP lead candidates by using our best mQSSMs together with diversity-based network analyses, and 24 web servers for activity/toxicity and drug-like properties. These results support that network-based similarity searches can be an effective and reliable strategy to identify APPs. The proposed models and pipeline are freely available through the StarPep toolbox software at http://mobiosd-hub.com/starpep.

Searching glycolate oxidase inhibitors based on QSAR, molecular docking, and molecular dynamic simulation approaches.

Primary hyperoxaluria type 1 (PHT1) treatment is mainly focused on inhibiting the enzyme glycolate oxidase, which plays a pivotal role in the production of glyoxylate, which undergoes oxidation to produce oxalate. When the renal secretion capacity exceeds, calcium oxalate forms stones that accumulate in the kidneys. In this respect, detailed QSAR analysis, molecular docking, and dynamics simulations of a series of inhibitors containing glycolic, glyoxylic, and salicylic acid groups have been performed employing different regression machine learning techniques. Three robust models with less than 9 descriptors-based on a tenfold cross (Q2 CV) and external (Q2 EXT) validation-were found i.e., MLR1 (Q2 CV = 0.893, Q2 EXT = 0.897), RF1 (Q2 CV = 0.889, Q2 EXT = 0.907), and IBK1 (Q2 CV = 0.891, Q2 EXT = 0.907). An ensemble model was built by averaging the predicted pIC50 of the three models, obtaining a Q2 EXT = 0.933. Physicochemical properties such as charge, electronegativity, hardness, softness, van der Waals volume, and polarizability were considered as attributes to build the models. To get more insight into the potential biological activity of the compouds studied herein, docking and dynamic analysis were carried out, finding the hydrophobic and polar residues show important interactions with the ligands. A screening of the DrugBank database V.5.1.7 was performed, leading to the proposal of seven commercial drugs within the applicability domain of the models, that can be suggested as possible PHT1 treatment.

Automatic identification of intestinal parasites in reptiles using microscopic stool images and convolutional neural networks.

Captive environments trigger the propagation and multiplication of parasites among different reptile species, thus weakening their immune response and causing infections and diseases. Technological advances of convolutional neural networks have opened a new field for detecting and classifying diseases which have shown great potential to overcome the shortcomings of manual detection performed by experts. Therefore, we propose an approach to identify six captive reptiles parasitic agents (Ophionyssus natricis, Blastocystis sp, Oxiurdo egg, Rhytidoides similis, Strongyloides, Taenia) or the absence of such parasites from a microscope stool images dataset. Towards this end, we first use an image segmentation stage to detect the parasite within the image, which combines the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique, the OTSU binarization method, and morphological operations. Then, we carry out a classification stage through MobileNet CNN under a transfer learning scheme. This method was validated on a stool image dataset containing 3616 images data samples and 26 videos from the six parasites mentioned above. The results obtained indicate that our transfer learning-based approach can learn a helpful representation from the dataset. We obtained an average accuracy of 94.26% across the seven classes (i.e., six parasitic agents and the absence of parasites), which statistically outperformed, at a 95% confidence level, a custom CNN trained from scratch.

Topical NSAIDs, intravitreal dexamethasone and peribulbar triamcinolone for pseudophakic macular edema.

BACKGROUND: The purpose of this study is to assess the effectiveness of topical nonsteroidal anti-inflammatory drugs (NSAIDs) and corticosteroids (intravitreal dexamethasone and peribulbar triamcinolone) in treating pseudophakic macular edema (PME). METHODS: Retrospective study of 33 eyes. Variables included best corrected visual acuity (BCVA; logMAR scale) and central retinal thickness (CRT) and central choroidal thickness (CCT) assessed with swept-source OCT. All patients were initially prescribed topical NSAIDs and reevaluated after 2 months. If improvement in BCVA or CRT was noted, topical NSAIDs were continued until resolution. If no improvement was observed at 2 months or subsequent visits, intravitreal dexamethasone implant was performed. Patients who refused intravitreal treatment were offered peribulbar triamcinolone. RESULTS: After treatment with topical NSAIDs for a median of 2 months, BCVA increased significantly from 0.5 to 0.3 while CRT decreased significantly from 435 to 316 µm. PME resolved in 19 of the 33 eyes (57.6%). Of the 14 recalcitrant cases, 13 were treated with corticosteroids. Of these 13 cases, 9 (69.2%) resolved. BCVA increased non-significantly from 0.7 to 0.4. CRT and CCT decreased significantly from 492 to 317 µm and from 204 to 182 µm respectively. CONCLUSIONS: The overall success rate of the treatment algorithm was greater than 80%, a remarkable finding considering that no randomized study has yet been conducted to determine the optimal therapeutic protocol for PME. This is the first study to evaluate choroidal thickness in PME using SS-OCT, which could play a key role in its pathophysiology and provide useful information to improve the management of PME.

Technical advance in silico and in vitro development of a new bipolar radiofrequency ablation device for renal denervation.

BACKGROUND: Renal denervation with radiofrequency ablation has become an accepted treatment for drug-resistant hypertension. However, there is a continuing need to develop new catheters for high-accuracy, targeted ablation. We therefore developed a radiofrequency bipolar electrode for controlled, targeted ablation through Joule heating induction between 60 and 100 °C. The bipolar design can easily be assembled into a basket catheter for deployment inside the renal artery. METHODS: Finite element modeling was used to determine the optimum catheter design to deliver a minimum ablation zone of 4 mm (W) × 10 mm (L) × 4 mm (H) within 60 s with a 500 kHz, 60 Vp-p signal, and 3 W maximum. The in silico model was validated with in vitro experiments using a thermochromic phantom tissue prepared with polyacrylamide gel and a thermochromic ink additive that permanently changes from pink to magenta when heated over 60 °C. RESULTS: The in vitro ablation zone closely matched the size and shape of the simulated area. The new electrode design directs the current density towards the artery walls and tissue, reducing unwanted blood temperature increases by focusing energy on the ablation zone. In contrast, the basket catheter design does not block renal flow during renal denervation. CONCLUSIONS: This computational model of radiofrequency ablation can be used to estimate renal artery ablation zones for highly targeted renal denervation in patients with resistant hypertension. Furthermore, this innovative catheter has short ablation times and is one of the lowest power requirements of existing designs to perform the ablation.

A Mirror-Based Active Vision System for Underwater Robots: From the Design to Active Object Tracking Application.

A mirror-based active system capable of changing the view's direction of a pre-existing fixed camera is presented. The aim of this research work is to extend the perceptual tracking capabilities of an underwater robot without altering its structure. The ability to control the view's direction allows the robot to explore its entire surroundings without any actual displacement, which can be useful for more effective motion planning and for different navigation strategies, such as object tracking and/or obstacle evasion, which are of great importance for natural preservation in environments as complex and fragile as coral reefs. Active vision systems based on mirrors had been used mainly in terrestrial platforms to capture the motion of fast projectiles using high-speed cameras of considerable size and weight, but they had not been used on underwater platforms. In this sense, our approach incorporates a lightweight design adapted to an underwater robot using affordable and easy-access technology (i.e., 3D printing). Our active system consists of two arranged mirrors, one of which remains static in front of the robot's camera, while the orientation of the second mirror is controlled by two servomotors. Object tracking is performed by using only the pixels contained on the homography of a defined area in the active mirror. HSV color space is used to reduce lighting change effects. Since color and geometry information of the tracking object are previously known, a window filter is applied over the H-channel for color blobs detection, then, noise is filtered and the object's centroid is estimated. If the object is lost, a Kalman filter is applied to predict its position. Finally, with this information, an image PD controller computes the servomotor articular values. We have carried out experiments in real environments, testing our active vision system in an object-tracking application where an artificial object is manually displaced on the periphery of the robot and the mirror system is automatically reconfigured to keep such object focused by the camera, having satisfactory results in real time for detecting objects of low complexity and in poor lighting conditions.

Automatic ladybird beetle detection using deep-learning models.

Fast and accurate taxonomic identification of invasive trans-located ladybird beetle species is essential to prevent significant impacts on biological communities, ecosystem functions, and agricultural business economics. Therefore, in this work we propose a two-step automatic detector for ladybird beetles in random environment images as the first stage towards an automated classification system. First, an image processing module composed of a saliency map representation, simple linear iterative clustering superpixels segmentation, and active contour methods allowed us to generate bounding boxes with possible ladybird beetles locations within an image. Subsequently, a deep convolutional neural network-based classifier selects only the bounding boxes with ladybird beetles as the final output. This method was validated on a 2, 300 ladybird beetle image data set from Ecuador and Colombia obtained from the iNaturalist project. The proposed approach achieved an accuracy score of 92% and an area under the receiver operating characteristic curve of 0.977 for the bounding box generation and classification tasks. These successful results enable the proposed detector as a valuable tool for helping specialists in the ladybird beetle detection problem.

Coronavirus disease 2019, allergic diseases, and allergen immunotherapy: Possible favorable mechanisms of interaction.

Background: Both, allergen immunotherapy (AIT) and SARS-COV-2 infection cause a set of immunologic changes that respectively vary during the course of the treatment or the disease. Objective: To review immune changes brought along by each of these entities and how they might interrelate. Methods: We start presenting a brief review of the structure of the new coronavirus and how it alters the functioning of the human immune system. Subsequently, we describe the immune changes induced by AIT and how these changes could be favorable or unfavorable in the allergic patient infected with SARS-CoV-2 at a particular point of time during the evolving infection. Results: We describe how a healthy immune response against SARS-CoV-2 develops, versus an immune response that is initially suppressed by the virus, but ultimately overactivated, leading to an excessive production of cytokines (cytokine-storm-like). These changes are then linked to the clinical manifestations and outcomes of the patient. Reviewing the immune changes secondary to AIT, it becomes clear how AIT is capable of restoring a healthy innate immunity. Investigators have previously shown that the frequency of respiratory infections is reduced in allergic patients treated with AIT. On the other hand it also increases immunoregulation. Conclusion: As there are many variables involved, it is hard to predict how AIT could influence the allergic patient's reaction to a SARS-CoV-2 infection. In any case, AIT is likely to be beneficial for the patient with allergic rhinitis and/or allergic asthma in the context of the SARS-CoV-2 pandemic as controlling allergic diseases leads to a reduced need for contact with healthcare professionals. The authors remind the reader that everything in this article is still theoretical, since at the moment, there are no published clinical trials on the outcome of COVID-19 in allergic patients under AIT.

Cutaneous Manifestations Related to COVID-19 Immune Dysregulation in the Pediatric Age Group.

PURPOSE OF REVIEW: At the juncture of the COVID-19 pandemic, the world is currently in an early phase of collecting clinical data and reports of its skin manifestations, and its pathophysiology is still highly conjectural. We reviewed cutaneous manifestations associated with COVID-19 in the pediatric age group. RECENT FINDINGS: Children infected by SARS-CoV-2 usually develop milder respiratory symptoms, but cutaneous manifestations seem a little more prevalent than in adults. These skin features of infection by the coronavirus can be similar to those produced by other common viruses, but there are also reports of cases with more heterogeneous clinical pictures, which have made their classification difficult. To date, the more frequently reported skin variants featured in pediatric cases are purpuric (pseudo-chilblain, necrotic-acral ischemia, hemorrhagic macules, and/or cutaneous necrosis), morbilliform/maculopapular, erythema multiforme, urticarial, vesicular, Kawasaki-like, and miscellaneous (highly variable in both frequency and severity). Their pathophysiological mechanism is still elusive and is likely to be the result of the complex involvement of one or more mechanisms, like direct virus-induced skin damage, vasculitis-like reactions, and/or indirect injury as a consequence of a systemic inflammatory reaction. In this review, we presented and discussed clinical cases as examples of different cutaneous responses reported in some children with SARS-CoV-2 infection, differential diagnosis considerations, and a preliminary conceptual approach to some of their probable associated pathologic mechanisms.

Global Asthma Network Phase I study in Mexico: prevalence of asthma symptoms, risk factors and altitude associations-a cross-sectional study.

BACKGROUND: Global Asthma Network (GAN) was established in 2012 as a development to the International Study of Asthma and Allergies in Childhood to improve asthma care globally. OBJECTIVE: To survey asthma, allergic rhinitis and atopic dermatitis in primary and secondary school children and to investigate and evaluate its prevalence, severity, management and risk factors in Mexico. METHODS: GAN Phase I is a cross-sectional, multicentre survey carried out in 15 centres corresponding to 14 Mexican cities throughout 2016-2019 using the validated Spanish language version of the GAN Phase I questionnaires. The questionnaires were completed by parents of 6-7-year-old primary school pupils (school children) and by 13-14-year-old adolescents. RESULTS: A total of 35 780 school children and 41 399 adolescents participated. Wheezing ever prevalence was 26.2% (95% CI 25.8% to 26.7%) in school children and 23.9% (95% CI 23.4% to 24.3%) in adolescents. The corresponding frequencies for current wheeze were 10.2% (95% CI 9.9% to 10.5%) and 11.6% (95% CI 11.2% to 11.9%). In school children, the risk factors for current wheeze were rhinitis (OR 4.484; 95% CI 3.915% to 5.134%) and rash symptoms (OR 1.735; 95% CI 1.461% to 2.059%). For adolescents, rhinitis symptoms (OR 3.492; 95% CI 3.188% to 3.825%) and allergic rhinitis diagnosis (OR 2.144; 95% CI 1.787% to 2.572%) were the most significant. For both groups, there was a negative relation with centres' sea level altitude higher than 1500 m above mean sea level (p<0.005). CONCLUSIONS: The most important risk factors for asthma symptoms in both age groups were the presence of rhinitis and rash symptoms or diagnosis. On the other hand, sea level altitude higher than 1500 metres was a protective factor.

An approach to automatic classification of Culicoides species by learning the wing morphology.

Fast and accurate identification of biting midges is crucial in the study of Culicoides-borne diseases. In this work, we propose a two-stage method for automatically analyzing Culicoides (Diptera: Ceratopogonidae) species. First, an image preprocessing task composed of median and Wiener filters followed by equalization and morphological operations is used to improve the quality of the wing image in order to allow an adequate segmentation of particles of interest. Then, the segmentation of the zones of interest inside the biting midge wing is made using the watershed transform. The proposed method is able to produce optimal feature vectors that help to identify Culicoides species. A database containing wing images of C. obsoletus, C. pusillus, C. foxi, and C. insignis species was used to test its performance. Feature relevance analysis indicated that the mean of hydraulic radius and eccentricity were relevant for the decision boundary between C. obsoletus and C. pusillus species. In contrast, the number of particles and the mean of the hydraulic radius was relevant for deciding between C. foxi and C. insignis species. Meanwhile, for distinguishing among the four species, the number of particles and zones, and the mean of circularity were the most relevant features. The linear discriminant analysis classifier was the best model for the three experimental classification scenarios previously described, achieving averaged areas under the receiver operating characteristic curve of 0.98, 0.90, and 0.96, respectively.

Enhancing innate immunity against virus in times of COVID-19: Trying to untangle facts from fictions.

INTRODUCTION: In light of the current COVID-19 pandemic, during which the world is confronted with a new, highly contagious virus that suppresses innate immunity as one of its initial virulence mechanisms, thus escaping from first-line human defense mechanisms, enhancing innate immunity seems a good preventive strategy. METHODS: Without the intention to write an official systematic review, but more to give an overview of possible strategies, in this review article we discuss several interventions that might stimulate innate immunity and thus our defense against (viral) respiratory tract infections. Some of these interventions can also stimulate the adaptive T- and B-cell responses, but our main focus is on the innate part of immunity. We divide the reviewed interventions into: 1) lifestyle related (exercise, >7 h sleep, forest walking, meditation/mindfulness, vitamin supplementation); 2) Non-specific immune stimulants (letting fever advance, bacterial vaccines, probiotics, dialyzable leukocyte extract, pidotimod), and 3) specific vaccines with heterologous effect (BCG vaccine, mumps-measles-rubeola vaccine, etc). RESULTS: For each of these interventions we briefly comment on their definition, possible mechanisms and evidence of clinical efficacy or lack of it, especially focusing on respiratory tract infections, viral infections, and eventually a reduced mortality in severe respiratory infections in the intensive care unit. At the end, a summary table demonstrates the best trials supporting (or not) clinical evidence. CONCLUSION: Several interventions have some degree of evidence for enhancing the innate immune response and thus conveying possible benefit, but specific trials in COVID-19 should be conducted to support solid recommendations.

Compromising between European and US allergen immunotherapy schools: Discussions from GUIMIT, the Mexican immunotherapy guidelines.

BACKGROUND: Allergen immunotherapy (AIT) has a longstanding history and still remains the only disease-changing treatment for allergic rhinitis and asthma. Over the years 2 different schools have developed their strategies: the United States (US) and the European. Allergen extracts available in these regions are adapted to local practice. In other parts of the world, extracts from both regions and local ones are commercialized, as in Mexico. Here, local experts developed a national AIT guideline (GUIMIT 2019) searching for compromises between both schools. METHODS: Using ADAPTE methodology for transculturizing guidelines and AGREE-II for evaluating guideline quality, GUIMIT selected 3 high-quality Main Reference Guidelines (MRGs): the European Academy of Allergy, Asthma and Immunology (EAACI) guideines, the S2k guideline of various German-speaking medical societies (2014), and the US Practice Parameters on Allergen Immunotherapy 2011. We formulated clinical questions and based responses on the fused evidence available in the MRGs, combined with local possibilities, patient's preference, and costs. We came across several issues on which the MRGs disagreed. These are presented here along with arguments of GUIMIT members to resolve them. GUIMIT (for a complete English version, Supplementary data) concluded the following. RESULTS: Related to the diagnosis of IgE-mediated respiratory allergy, apart from skin prick testing complementary tests (challenges, in vitro testing and molecular such as species-specific allergens) might be useful in selected cases to inform AIT composition. AIT is indicated in allergic rhinitis and suggested in allergic asthma (once controlled) and IgE-mediated atopic dermatitis. Concerning the correct subcutaneous AIT dose for compounding vials according to the US school: dosing tables and formula are given; up to 4 non-related allergens can be mixed, refraining from mixing high with low protease extracts. When using European extracts: the manufacturer's indications should be followed; in multi-allergic patients 2 simultaneous injections can be given (100% consensus); mixing is discouraged. In Mexico only allergoid tablets are available; based on doses used in all sublingual immunotherapy (SLIT) publications referenced in MRGs, GUIMIT suggests a probable effective dose related to subcutaneous immunotherapy (SCIT) might be: 50-200% of the monthly SCIT dose given daily, maximum mixing 4 allergens. Also, a table with practical suggestions on non-evidence-existing issues, developed with a simplified Delphi method, is added. Finally, dissemination and implementation of guidelines is briefly discussed, explaining how we used online tools for this in Mexico. CONCLUSIONS: Countries where European and American AIT extracts are available should adjust AIT according to which school is followed.