Search

Drug repurposing is a major field of value-added medicine. It involves investigating and evaluating existing drugs for new therapeutic purposes that address unmet healthcare needs. Several unmet needs in allergic rhinitis could be improved by drug repurposing. This could be game-changing for disease management. Current medications for allergic rhinitis are centered on continuous long-term treatment, and medication registration is based on randomized controlled trials carried out for a minimum of 14 days with adherence of 70% or greater. A new way of treating allergic rhinitis is to propose as-needed treatment depending on symptoms, rather than classical continuous treatment. This rostrum will discuss existing clinical trials on as-needed treatment for allergic rhinitis and real-world data obtained by the mobile health app MASK-air, which focuses on digitally-enabled, patient-centered care pathways.

Biomarkers for the diagnosis, treatment and follow-up of patients with rhinitis and/or asthma are urgently needed. Although some biologic biomarkers exist in specialist care for asthma, they cannot be largely used in primary care. There are no validated biomarkers in rhinitis or allergen immunotherapy (AIT) that can be used in clinical practice. The digital transformation of health and health care (including mHealth) places the patient at the center of the health system and is likely to optimize the practice of allergy. Allergic Rhinitis and its Impact on Asthma (ARIA) and EAACI (European Academy of Allergy and Clinical Immunology) developed a Task Force aimed at proposing patient-reported outcome measures (PROMs) as digital biomarkers that can be easily used for different purposes in rhinitis and asthma. It first defined control digital biomarkers that should make a bridge between clinical practice, randomized controlled trials, observational real-life studies and allergen challenges. Using the MASK-air app as a model, a daily electronic combined symptom-medication score for allergic diseases (CSMS) or for asthma (e-DASTHMA), combined with a monthly control questionnaire, was embedded in a strategy similar to the diabetes approach for disease control. To mimic real-life, it secondly proposed quality-of-life digital biomarkers including daily EQ-5D visual analogue scales and the bi-weekly RhinAsthma Patient Perspective (RAAP). The potential implications for the management of allergic respiratory diseases were proposed.

A doença de Crohn (DC) faz parte do grupo de doença inflamatória intestinal (DII). A DC pode ter comportamento desde mais leves (inflamatória), até as formas mais graves (estenosante e penetrante). O presente trabalho tem objetivo de caracterizar a DC de acordo com o comportamento da doença inflamatório, estenosante ou penetrante. Trata-se de um estudo transversal observacional, com entrevista dos pacientes e revisão dos prontuários após aplicação do TCLE. As variáveis foram analisadas pelo pacote estatístico SPSS versão 21.0. Foram incluídos 92 pacientes, 55,4% (51) eram do sexo feminino. A média de idade foi 44,4 anos (± 14,6) e a média de tempo de doença foi 114,6 meses. A idade de diagnóstico mais frequente nos três fenótipos foi entre 17 e 40 anos. Um total de 54,3% (50) apresentavam o comportamento não estenosante/não penetrante, 23,9% (22) estenosante e 22,2% (20) penetrante. Em todos os comportamentos da doença, a grande maioria se autodeclarou como pardo, seguido por negro. Os comportamentos estenosante e penetrante mantiveram predominância do sexo feminino, enquanto que na forma não estenosante/ não penetrante, do sexo masculino. Entre os pacientes com doença de comportamento não penetrante e não estenosante e com o comportamento penetrante, a principal localização foi a colônica, Entre os pacientes com doença estenosante, a maioria apresentou localização ileocônica. Os pacientes com a doença penetrante foram mais jovens no momento da entrevista, com localização mais frequente colônica e maior frequência de doença perianal associada.Nosso trabalho conclui que o comportamento não penetrante/não estenosante foi o mais observado na amostra estudada. Os pacientes com as formas complicadas, tiveram o diagnóstico mais tardio e apresentaram maior tempo de duração da doença. A forma penetrante apresentou mais frequente localização colônica e doença perianal associada. O comportamento estenosante teve predominante localização ileocolonica, enquanto na doença não penetrante/ não estenosante a localização colônica foi a mais frequente.

Data de publicació electrònica: 17-04-2023

ObjectivesAnxiety and depression are relevant comorbidities in asthma, but, in Portugal and Spain, data on this topic are scarce. We assessed, in patients with asthma, the frequency of anxiety and depression using the Hospital Anxiety and Depression Scale (HADS) and the European Quality of Life Five Dimension Questionnaire (EQ-5D); the level of agreement between these questionnaires, and the factors associated with these symptoms.MethodsThis is a secondary analysis of the INSPIRERS studies. A total of 614 adolescents and adults with persistent asthma (32.6±16.9 years, 64.7% female) were recruited from 30 primary care centres and 32 allergy, pulmonology and paediatric clinics. Demographic and clinical characteristics, HADS and EQ-5D were collected. A score ≥8 on Hospital Anxiety and Depression Scale-Anxiety/Hospital Anxiety and Depression Scale-Depression or a positive answer to EQ-5D item 5 indicated the presence of these symptoms. Agreement was determined by Cohen’s kappa. Two multivariable logistic regressions were built.ResultsAccording to HADS, 36% of the participants had symptoms of anxiety and 12% of depression. According to EQ-5D, 36% of the participants had anxiety/depression. The agreement between questionnaires in identifying anxiety/depression was moderate (k=0.55, 95% CI 0.48 to 0.62). Late asthma diagnosis, comorbidities and female gender were predictors of anxiety/depression, while better asthma control, health-related quality of life and perception of health were associated with lower odds for anxiety/depression.ConclusionAt least 1/3 of the patients with persistent asthma experience symptoms of anxiety/depression, showing the relevance of screening these disorders in patients with asthma. EQ-5D and HADS questionnaires showed a moderate agreement in the identification of anxiety/depression symptoms. The identified associated factors need to be further investigated in long-term studies.

In clinical and epidemiological studies, cut-offs of Patient-Reported Outcome Measures (PROMs) can be used to classify patients into groups of statistical and clinical relevance. However, visual analog scale (VAS) cut-offs in MASK-air® have not been tested.To calculate cut-offs for VAS global, nasal, ocular, and asthma symptoms.In a cross-sectional study design of all MASK-air® participants, we compared (i) approaches based on the percentiles (tertiles or quartiles) of VAS distributions, and (ii) data-driven approaches based on clusters of data from two comparators (VAS work and VAS sleep). We then performed sensitivity analyses for individual countries and for VAS levels corresponding to full allergy control. Finally, we tested the different approaches using MASK-air® real-world cross-sectional and longitudinal data to assess the most relevant cut-offs.We assessed 395,223 days from 23,201 MASK-air® users with self-reported allergic rhinitis. The percentile-oriented approach resulted in lower cut-off values than the data-driven approach. We obtained consistent results in the data-driven approach. Following the latter, the proposed cut-off differentiating "controlled" and "partly-controlled" patients was similar to the cut-off value which had been arbitrarily used (20/100). However, a lower cut-off was obtained to differentiate between "partly-controlled" and "uncontrolled" patients (35 versus the arbitrarily-used value of 50/100).Using a data-driven approach, we were able to define cut-off values for MASK-air® VASs on allergy and asthma symptoms. This may allow for a better classification of rhinitis and asthma patients according to different levels of control, supporting improved disease management.

One in every eight women will get breast cancer during their lifetime. Therefore, the early diagnosis of the lesions is fundamental to improve the chances of recovery. To find breast cancers, breast screening using techniques such as mammography and ultrasound (US) imaging scans are often used. When a lesion is found, a breast biopsy is performed to extract a tissue sample for analysis. The breast biopsy is usually assisted by an US to help find the lesion and guide the needle to its location. However, the identification of the needle tip in US image is challenging, possibly resulting in puncture failures. In this paper, we intend to study the potential of a sensorized needle guide system that provides information about the needle angle and displacement in respect to the US probe. Laboratory tests were initially conducted to evaluate the accuracy of each sensor in controlled conditions. After, a practical experiment with the US probe, working as a proof of concept, was performed. The angle sensor showed a root mean square error (RMSE) of 0.48 degrees and the displacement sensor showed a RMSE of 0.26mm after being calibrated. For the US probe tests, the displacement sensor shows high errors in the range of 1.19mm to 2.05mm due to mechanical reasons. Overall, the proposed system showed its potential to be used to accurately estimate needle tip localization throughout breast biopsies guided by US, corroborating its potential clinical application. Clinical relevance - Potential for clinical application where precise needle localization in ultrasound image is required.

Ultrasound (US) imaging despite being safe, cost-effective, and radiation-free, presents poor quality and artifacts, requiring specific medical training in US probe handling and image evaluation. The use of simulators to train physicians has proven its effectiveness, but most of them require specific facilities and hardware. In the last few years, augmented reality has gained relevance to simulate real scenarios which can avoid large setups and broaden medical training to more physicians. This work proposes a new framework for the training of US images acquisition. It consists of a custom-made application that runs on AR glasses (Microsoft HoloLens 2) and interacts with a US simulator application. The AR glasses track the orientation of a QR code mounted on a US probe, communicating its orientation with the US simulator application. This allows the physician to interact with a US probe seeing in real-time the US image in the physician's field of view. The QR code tracking assessment of the AR glasses was conducted by measuring the orientation accuracy and precision when compared with the measures of an electromagnetic tracking device (i.e., NDI Aurora). The proposed solution presented a good performance, rendering the US image in AR glasses with real-time feedback. Moreover, it can track the QR code on the US probe with an accuracy of 0.755°, and a precision of 0.018°. Overall, the proposed framework presents promising results and the use of AR glasses as a tracking device reached a good performance. Clinical Relevance- Simulation is a relevant tool to train physicians, especially in US imaging. AR glasses can broaden the training to less trained physicians by reducing the need for complex setups. This technology allows the implementation of a more natural user interface, which can be relevant in scenarios where good coordination between the eyes and hands of the physician is necessary (i.e., Biopsies).

Ultrasound (US) is a medical imaging modality widely used for diagnosis, monitoring, and guidance of surgical procedures. However, the accurate interpretation of US images is a challenging task. Recently, portable 2D US devices enhanced with Artificial intelligence (AI) methods to identify, in real-time, specific organs are widely spreading worldwide. Nevertheless, the number of available methods that effectively work in such devices is still limited. In this work, we evaluate the performance of the U-NET architecture to segment the kidney in 2D US images. To accomplish this task, we studied the possibility of using multiple sliced images extracted from 3D US volumes to achieve a large, variable, and multi-view dataset of 2D images. The proposed methodology was tested with a dataset of 66 3D US volumes, divided in 51 for training, 5 for validation, and 10 for testing. From the volumes, 3792 2D sliced images were extracted. Two experiments were conducted, namely: (i) using the entire database (WWKD); and (ii) using images where the kidney area is500 mm2 (500KD). As a proof-of-concept, the potential of our strategy was tested in real 2D images (acquired with 2D probes). An average error of 2.88 ± 2.63 mm in the testing dataset was registered. Moreover, satisfactory results were obtained in our initial proof-of-concept using pure 2D images. In short, the proposed method proved, in this preliminary study, its potential interest for clinical practice. Further studies are required to evaluate the real performance of the proposed methodology. Clinical Relevance- In this work a deep learning methodology to segment the kidney in 2D US images is presented. It may be a relevant feature to be included in the recent portable US ecosystems easing the interpretation of image and consequently the clinical analysis.

Automatic lesion segmentation in breast ultrasound (BUS) images aids in the diagnosis of breast cancer, the most common type of cancer in women. Accurate lesion segmentation in ultrasound images is a challenging task due to speckle noise, artifacts, shadows, and lesion variability in size and shape. Recently, convolutional neural networks have demonstrated impressive results in medical image segmentation tasks. However, the lack of public benchmarks and a standardized evaluation method hampers the networks' performance comparison. This work presents a benchmark of seven state-of-the-art methods for the automatic breast lesion segmentation task. The methods were evaluated on a multi-center BUS dataset composed of three public datasets. Specifically, the U-Net, Dynamic U-Net, Semantic Segmentation Deep Residual Network with Variational Autoencoder (SegResNetVAE), U-Net Transformers, Residual Feedback Network, Multiscale Dual Attention-Based Network, and Global Guidance Network (GG-Net) architectures were evaluated. The training was performed with a combination of the cross-entropy and Dice loss functions and the overall performance of the networks was assessed using the Dice coefficient, Jaccard index, accuracy, recall, specificity, and precision. Despite all networks having obtained Dice scores superior to 75%, the GG-Net and SegResNetVAE architectures outperform the remaining methods, achieving 82.56% and 81.90%, respectively. Clinical Relevance- The results of this study allowed to prove the potential of deep neural networks to be used in clinical practice for breast lesion segmentation also suggesting the best model choices.

Automatic lesion segmentation in mammography images assists in the diagnosis of breast cancer, which is the most common type of cancer especially among women. The robust segmentation of mammography images has been considered a backbreaking task due to: i) the low contrast of the lesion boundaries; ii) the extremely variable lesions' sizes and shapes; and iii) some extremely small lesions on the mammogram image. To overcome these drawbacks, Deep Learning methods have been implemented and have shown impressive results when applied to medical image segmentation. This work presents a benchmark for breast lesion segmentation in mammography images, where six state-of-the-art methods were evaluated on 1692 mammograms from a public dataset (CBIS-DDSM), and compared considering the following six metrics: i) Dice coefficient; ii) Jaccard index; iii) accuracy; iv) recall; v) specificity; and vi) precision. The base U-Net, UNETR, DynUNet, SegResNetVAE, RF-Net, MDA-Net architectures were trained with a combination of the cross-entropy and Dice loss functions. Although the networks presented Dice scores superior to 86%, two of them managed to distinguish themselves. In general, the results demonstrate the efficiency of the MDA-Net and DynUnet with Dice scores of 90.25% and 89.67%, and accuracy of 93.48% and 93.03%, respectively. Clinical Relevance--- The presented comparative study allowed to identify the current performance of deep learning strategies on the segmentation of breast lesions.

Funding Information: IA reports personal fees from Roxall, Menarini, UCB, Faes Farma, Sanofi, Bial, Amgen, Abbott, Bayer, Organon. SBA reports grants from TEVA, personal fees from TEVA, AstraZeneca, Boehringer Ingelheim, GSK, Sanofi, Mylan. JB reports personal fees from Chiesi, Cipla, Hikma, Menarini, Mundipharma, Mylan, Novartis, Sanofi‐Aventis, Takeda, Teva, Uriach, other from KYomed‐Innov, personal fees from Purina, other from MASK‐air. VC reports personal fees from Thermofisher. PCM reports personal fees from Abbvie, AZ, Bial, GSK, Mylan, Medinfar, Novartis, Sanofi. LC reports personal fees from Malesci, Menarini, Astra Zeneca, Novartis. AC reports grants and personal fees from Astrazeneca, GSK, Sanofi, personal fees from Boehringer‐Ingelheim, Chiesi, Glenmark, Novartis, personal fees from Mylan, Abdi‐Ibrahim. PD reports personal fees and non‐financial support from Stallergenes Greer, ALK‐Abello, Astra Zeneca, CHIESI, MYLAN/Meda Pharma, Novartis, GlaxoSmithKline, Sanofi, IQVIA personal fees from MENARINI. JAFonseca reports participation in SME that has mHealth technologies for patients with asthma. JCI reports personal fees from Abbott Ecuador, Bago Bolivia, Faes Farma, Laboratorios Casasco, Sanofi. LK reports grants and personal fees from Allergopharma, LETI Pharma,MEDA/Mylan, Sanofi, personal fees from HAL Allergie, Allergy Therapeut., Cassella med, grants from ALK Abelló, Stallergenes, Quintiles, ASIT biotech, Lofarma, AstraZeneca, GSK, Inmunotk, and Membership: AeDA, DGHNO, Deutsche Akademie für Allergologie und klinische Immunologie, HNO‐BV, GPA, EAACI. VK reports other from Norameda, BerlinCHemie Menarini. PK reports personal fees from Adamed, AstraZeneca, Berlin Chemie Menarini, Boehringer Ingelheim, Chiesi, GSK, Novartis, Polpharma. DLL reports personal fees from Allakos, Amstrong, Astrazeneca, Chiesi, DBV Technologies, Grunenthal, GSK, Mylan/Viatris, Menarini, MSD, Novartis, Pfizer, Sanofi, Siegfried, UCB, Alakos, Gossamer, Carnot, grants from Sanofi, Astrazeneca, Novartis, Circassia, UCB, GSK, Purina institute, Abvvie, Lilly, Pfizer. BL reports grants and personal fees from Meda, personal fees from Glenamrk. RL reports grants and personal fees from GSK, AZ, Chiesi, personal fees from Novartis, Sanofi. MM reports personal fees from Novartis, Gsk, Menarini, Az, Chiesi, Sanofi, Pfizer. RM reports personal fees from Angelini Pharma ALK, Allergopharma, Allergy Therapeutics, Friulchem, Hexal, Servier, Klosterfrau, Bayer, FAES, GSK, MSD, Johnson&Johnson, Meda, Stada, UCB, Nuvo, Menarini, Mundipharma, Pohl‐Boskamp, Laboratoire de la Mer, Sidroga, Lek, PRO‐AdWise, grants and personal fees from Bencard, Stallergenes, Ursapharm, HAL BV, grants from Leti, Optima, BitopAG, Hulka, Inmunotek, Cassella‐med GmbH & Co. KG, ASIT biotech, grants, personal fees and non‐financial support from Lofarma, non‐financial support from Roxall, Atmos, Bionorica, Otonomy, Ferrero, personal fees and non‐financial support from Novartis. OP reports grants and personal fees from ALK‐Abelló, Allergopharma, Stallergenes Greer HAL Allergy Holding B.V./HAL Allergie GmbH, Bencard Allergie GmbH/Allergy Therapeutics, Lofarma, ASIT Biotech Tools S.A., Laboratorios LETI/LETI Pharma, Anergis S.A., GlaxoSmithKline, personal fees from MEDA Pharma/MYLAN, Mobile Chamber Experts (a GA2LEN Partner), Indoor Biotechnologies, Astellas Pharma Global, EUFOREA, ROXALL Medizin, Novartis, Sanofi‐Aventis and Sanofi‐Genzyme, Med Update Europe GmbH, streamedup! GmbH, John Wiley and Sons, AS, Paul‐Martini‐Stiftung (PMS), Regeneron Pharmaceuticals Inc., RG Aerztefortbildung, Institut für Disease Management, Springer GmbH, AstraZeneca, IQVIA Commercial, Ingress Health, grants from Pohl‐Boskamp, Inmunotek S.L., Biomay, Circassia. NGPapadopoulos reports personal fees from Novartis, Nutricia, HAL, MENARINI/FAES FARMA, SANOFI, MYLAN/MEDA, BIOMAY, AstraZeneca, GSK, MSD, ASIT BIOTECH, Boehringer Ingelheim, grants from Gerolymatos International SA, Capricare. ATB reports personal fees from AstraZeneca, GSK, Novartis, IQVIA/Abbvie, Mylan, Bial, Leti, grants and personal fees from Teva. STS reports personal fees from ERT, Roche products, Novartis, Sanofi Pharma, AstraZeneca, ALK‐ Abelló grants from Glaxo Smith Kline. IT reports grants from GSK, Boehringer Ingelheim, AZ, personal fees from Novartis, Astra Zeneca, Chiesi, TZ reports Organizational affiliations: Committee member: WHO‐Initiative “Allergic Rhinitis and Its Impact on Asthma” (ARIA); Member of the Board: German Society for Allergy and Clinical Immunology (DGAKI); Head: European Centre for Allergy Research Foundation (ECARF). President: Global Allergy and Asthma European Network (GA2LEN); Member: Committee on Allergy Diagnosis and Molecular Allergology, World Allergy Organization (WAO). Funding Information: MASK‐air® has been supported by EU grants (POLLAR, EIT Health; Structural and Development Funds, Twinning, EIP on AHA and H2020) and educational grants from Mylan‐Viatris, ALK, GSK, Novartis and Uriach 1 1 Publisher Copyright: © 2022 European Academy of Allergy and Clinical Immunology and John Wiley & Sons Ltd. Background: Different treatments exist for allergic rhinitis (AR), including pharmacotherapy and allergen immunotherapy (AIT), but they have not been compared using direct patient data (i.e., “real-world data”). We aimed to compare AR pharmacological treatments on (i) daily symptoms, (ii) frequency of use in co-medication, (iii) visual analogue scales (VASs) on allergy symptom control considering the minimal important difference (MID) and (iv) the effect of AIT. Methods: We assessed the MASK-air® app data (May 2015–December 2020) by users self-reporting AR (16–90 years). We compared eight AR medication schemes on reported VAS of allergy symptoms, clustering data by the patient and controlling for confounding factors. We compared (i) allergy symptoms between patients with and without AIT and (ii) different drug classes used in co-medication. Results: We analysed 269,837 days from 10,860 users. Most days (52.7%) involved medication use. Median VAS levels were significantly higher in co-medication than in monotherapy (including the fixed combination azelastine-fluticasone) schemes. In adjusted models, azelastine-fluticasone was associated with lower average VAS global allergy symptoms than all other medication schemes, while the contrary was observed for oral corticosteroids. AIT was associated with a decrease in allergy symptoms in some medication schemes. A difference larger than the MID compared to no treatment was observed for oral steroids. Azelastine-fluticasone was the drug class with the lowest chance of being used in co-medication (adjusted OR = 0.75; 95% CI = 0.71–0.80). Conclusion: Median VAS levels were higher in co-medication than in monotherapy. Patients with more severe symptoms report a higher treatment, which is currently not reflected in guidelines. publishersversion published

Background: Only a small number of apps addressing allergic rhinitis (AR) patients have been evaluated. This makes their selection difficult. We aimed to introduce a new approach to market research for AR apps, based on the automatic screening of Apple App and Google Play stores. Methods: A JavaScript programme was devised for automatic app screening, and applied in a market assessment of AR self-management apps. We searched the Google Play and Apple App stores of three countries (USA, UK and Australia) with the following search terms: "hay fever", "hayfever", "asthma", "rhinitis", "allergic rhinitis". Apps were eligible if symptoms were evaluated. Results obtained with the automatic programme were compared to those of a blinded manual search. As an example, we used the search to assess apps that can be used to design a combined medication score for AR. Results: The automatic search programme identified 39 potentially eligible apps out of a total of 1593 retrieved apps. Each of the 39 apps was individually checked, with 20 being classified as relevant. The manual search identified 19 relevant apps (out of 6750 screened apps). Combining both methods, a total of 21 relevant apps were identified, pointing to a sensitivity of 95% and a specificity of 99% for the automatic method. Among these 21 apps, only two could be used for the combined symptom-medication score for AR. Conclusions: The programmed algorithm presented herein is able to continuously retrieve all relevant AR apps in the Apple App and Google Play stores, with high sensitivity and specificity. This approach has the potential to unveil the gaps and unmet needs of the apps developed so far.