Image based system for the qualification of skin erythema

Skin allergy tests are the main procedure for diagnosing IgE-mediated reactions; which are commonly known as allergy. These reactions are produced by an overreaction of the immune system to substances that get in contact with the body, called allergens. From the skin allergy tests, the most commonly performed procedures are the skin prick tests (SPTs), which are based on introducing a small drop of allergen within the epidermis. If an allergic reaction occurs, histamine is released, causing local blood vessels to dilate, and thus, increasing the concentration of RBCs. Consequently, the local skin region becomes red in appearance, which is known as erythema. In addition, another symptom of this reaction is the increase in venules’ permeability, which produces a leakage of plasma (mainly composed by water). Then, extracellular fluid accumulates and a wheal appears. The dimensions of this wheal are used to determine if an specific allergen provokes a hypersensitivity reaction or not. This diagnostic method is based on visual appearance, which is subjected to userdependency. There is not an standardized way for measuring the wheal diameter, nor the erythematous area dimensions. Therefore, the objective of this work is to implement an image based system for performing an automatic diagnosis for hypersensitivity reactions. To achieve this, skin optical properties and light propagation within tissue are studied. Besides, for this purpose, absorbance and scattering coefficients for hemoglobin (contained in erythema) and water (contained in edema) are used for determining the illumination setup of the system. The specified wavelengths enhance visual appearance of erythema’s and edema’s light reflectance. From these values, absorption maps are built, which are then used to quantify both chromophore’s concentrations. The implementation of this system would mean an standardization for allergy tests diagnosis, as well as a cost reduction in experts training.Ingeniería Biomédic

Structural networks for brain age prediction

Biological networks have gained considerable attention within the Deep Learning community because of the promising framework of Graph Neural Networks (GNN), neural models that operate in complex networks. In the context of neuroimaging, GNNs have successfully been employed for functional MRI processing but their application to ROI-level structural MRI (sMRI) remains mostly unexplored. In this work we analyze the implementation of these geometric models with sMRI by building graphs of ROIs (ROI graphs) using tools from Graph Signal Processing literature and evaluate their performance in a downstream supervised task, age prediction. We first make a qualitative and quantitative comparison of the resulting networks obtained with common graph topology learning strategies. In a second stage, we train GNN-based models for brain age prediction. Since the order of every ROI graph is exactly the same and each vertex is an entity by itself (a ROI), we evaluate whether including ROI information during message-passing or global pooling operations is beneficial and compare the performance of GNNs against a Fully-Connected Neural Network baseline. The results show that ROI-level information is needed during the global pooling operation in order to achieve competitive results. However, no relevant improvement has been detected when it is incorporated during the message passing. These models achieve a MAE of 4.27 in hold-out test data, which is a performance very similar to the baseline, suggesting that the inductive bias included with the obtained graph connectivity is relevant and useful to reduce the dimensionality of the problem.This work has been supported by the Spanish Research Agency (AEI) under project PID2020-116907RB-I00 of the call MCIN/ AEI /10.13039/501100011033 and the FI-AGAUR grant funded by Direcció General de Recerca (DGR) of Departament de Recerca i Universitats (REU) of the Generalitat de Catalunya.Peer ReviewedPostprint (published version

Biological brain age prediction using machine learning on structural neuroimaging data: Multi-cohort validation against biomarkers of Alzheimer’s disease and neurodegeneration stratified by sex

Brain-age can be inferred from structural neuroimaging and compared to chronological age (brain-age delta) as a marker of biological brain aging. Accelerated aging has been found in neurodegenerative disorders like Alzheimer’s disease (AD), but its validation against markers of neurodegeneration and AD is lacking. Here, imaging-derived measures from the UK Biobank dataset (N=22,661) were used to predict brain-age in 2,314 cognitively unimpaired (CU) individuals at higher risk of AD and mild cognitive impaired (MCI) patients from four independent cohorts with available biomarker data: ALFA+, ADNI, EPAD and OASIS. Brain-age delta was associated with abnormal amyloid-b, more advanced stages (AT) of AD pathology and APOE-e4 status. Brain-age delta was positively associated with plasma neurofilament light, a marker of neurodegeneration, and sex differences in the brain effects of this marker were found. These results validate brain-age delta as a non-invasive marker of biological brain aging related to markers of AD and neurodegeneration.The project leading to these results has received funding from “la Caixa” Foundation (ID 100010434), under agreement LCF/PR/GN17/50300004 and the Alzheimer’s Association and an international anonymous charity foundation through the TriBEKa Imaging Platform project (TriBEKa-17-519007). Additional support has been received from the Universities and Research Secretariat, Ministry of Business and Knowledge of the Catalan Government under the grant no. 2017-SGR-892 and the Spanish Research Agency (AEI) under project PID2020-116907RB-I00 of the call MCIN/ AEI /10.13039/501100011033. FB is supported by the NIHR biomedical research center at UCLH. MSC receives funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 948677), the Instituto de Salud Carlos III (PI19/00155), and from a fellowship from ”la Caixa” Foundation (ID 100010434) and from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 847648 (LCF/BQ/PR21/11840004).Report de recerca signat per 27 autors/es: Irene Cumplido-Mayoral 1,2; Marina García-Prat 1; Grégory Operto 1,3,4; Carles Falcon 1,3,5; Mahnaz Shekari 1,2,3; Raffaele Cacciaglia 1,3,4; Marta Milà-Alomà 1,2,3,4; Luigi Lorenzini 6; Silvia Ingala 6; Alle Meije Wink 6; Henk JMM Mutsaerts 6; Carolina Minguillón 1,3,4; Karine Fauria 1,4; José Luis Molinuevo 1; Sven Haller 7; Gael Chetelat 8,10; Adam Waldman 9; Adam Schwarz 10; Frederik Barkhof 6,11; Ivonne Suridjan 12, 11; Gwendlyn Kollmorgen 13; Anna Bayfield 13; Henrik Zetterberg 14,15,16,17,18; Kaj Blennow 14,15 12; Marc Suárez-Calvet 1,3,4,19; Verónica Vilaplana 20; Juan Domingo Gispert 1,3,5; ALFA study; EPAD study; ADNI study; OASIS study // 1) Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain; 2) Universitat Pompeu Fabra, Barcelona, Spain; 3) IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain; 4) CIBER Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain; 5) Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain; 6) Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands; 7) CIRD Centre d'Imagerie Rive Droite, Geneva, Switzerland; 8) Normandie Univ, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Cyceron, Caen, France; 9) Centre for Dementia Prevention, Edinburgh Imaging, and UK Dementia Research Institute at The University of Edinburgh, Edinburgh, UK; 10) Takeda Pharmaceutical Company Ltd, Cambridge, MA, USA; 11) Institutes of Neurology and Healthcare Engineering, University College London, London, UK; 12) Roche Diagnostics International Ltd, Rotkreuz, Switzerland; 13) Roche Diagnostics GmbH, Penzberg, Germany; 14) Institute of Neuroscience and Physiology, University of Gothenburg, Mölndal, Sweden; 15) Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden; 16) Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, United Kingdom; 17) UK Dementia Research Institute at UCL, London, United Kingdom; 18) Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China; 19) Servei de Neurologia, Hospital del Mar, Barcelona, Spain; 20) Department of Signal Theory and Communications, Universitat Politècnica de Catalunya, Barcelona, Spain.Preprin

Biological brain age prediction using machine learning on structural neuroimaging data: Multi-cohort validation against biomarkers of Alzheimer's disease and neurodegeneration stratified by sex

Brain-age can be inferred from structural neuroimaging and compared to chronological age (brain-age delta) as a marker of biological brain aging. Accelerated aging has been found in neurodegenerative disorders like Alzheimer's disease (AD), but its validation against markers of neurodegeneration and AD is lacking. Here, imaging-derived measures from the UK Biobank dataset (N=22,661) were used to predict brain-age in 2,314 cognitively unimpaired (CU) individuals at higher risk of AD and mild cognitive impaired (MCI) patients from four independent cohorts with available biomarker data: ALFA+, ADNI, EPAD, and OASIS. Brain-age delta was associated with abnormal amyloid-β, more advanced stages (AT) of AD pathology and APOE-ε4 status. Brain-age delta was positively associated with plasma neurofilament light, a marker of neurodegeneration, and sex differences in the brain effects of this marker were found. These results validate brain-age delta as a non-invasive marker of biological brain aging in non-demented individuals with abnormal levels of biomarkers of AD and axonal injury

Genetically predicted telomere length and Alzheimer’s disease endophenotypes: a Mendelian randomization study

Telomere length (TL) is associated with biological aging, consequently influencing the risk of age-related diseases such as Alzheimer's disease (AD). We aimed to evaluate the potential causal role of TL in AD endophenotypes (i.e., cognitive performance, N = 2233; brain age and AD-related signatures, N = 1134; and cerebrospinal fluid biomarkers (CSF) of AD and neurodegeneration, N = 304) through a Mendelian randomization (MR) analysis. Our analysis was conducted in the context of the ALFA (ALzheimer and FAmilies) study, a population of cognitively healthy individuals at risk of AD. A total of 20 single nucleotide polymorphisms associated with TL were used to determine the effect of TL on AD endophenotypes. Analyses were adjusted by age, sex, and years of education. Stratified analyses by APOE-epsilon 4 status and polygenic risk score of AD were conducted. MR analysis revealed significant associations between genetically predicted longer TL and lower levels of CSF A beta and higher levels of CSF NfL only in APOE-epsilon 4 non-carriers. Moreover, inheriting longer TL was associated with greater cortical thickness in age and AD-related brain signatures and lower levels of CSF p-tau among individuals at a high genetic predisposition to AD. Further observational analyses are warranted to better understand these associations

Spatiotemporal Dynamics of Single and Paired Pulse TMS-EEG Responses

For physiological brain function a particular balance between excitation and inhibition is essential. Paired pulse transcranial magnetic stimulation (TMS) can estimate cortical excitability and the relative contribution of inhibitory and excitatory networks. Combining TMS with electroencephalography (EEG) enables additional assessment of the spatiotemporal dynamics of neuronal responses in the stimulated brain. This study aims to evaluate the spatiotemporal dynamics and stability of single and paired pulse TMS-EEG responses, and assess long intracortical inhibition (LICI) at the cortical level. Twenty-five healthy subjects were studied twice, approximately one week apart. Manual coil positioning was applied in sixteen subjects and robot-guided positioning in nine. Both motor cortices were stimulated with 50 single pulses and 50 paired pulses at each of the five interstimulus intervals (ISIs): 100, 150, 200, 250 and 300 ms. To assess stability and LICI, the intraclass correlation coefficient and cluster-based permutation analysis were used. We found great resemblance in the topographical distribution of the characteristic TMS-EEG components for single and paired pulse TMS. Stimulation of the dominant and non-dominant hemisphere resulted in a mirrored spatiotemporal dynamics. No significant effect on the TMS-EEG responses was found for either stimulated hemisphere, time or coil positioning method, indicating the stability of both single and paired pulse TMS-EEG responses. For all ISIs, LICI was characterized by significant suppression of the late N100 and P180 components in the central areas, without affecting the early P30, N45 and P60 components. These observations in healthy subjects can serve as reference values for future neuropsychiatric and pharmacological studies

Brain structural alterations in cognitively unimpaired individuals with discordant amyloid-ß PET and CSF Aß42 status: findings using machine learning

Background: CSF Aß42 is thought to show AD-related alterations earlier than amyloid-ß PET. Therefore, cognitively unimpaired (CU) individuals with abnormal CSF Aß42 and normal amyloid-ß PET are believed to be in the earliest stages of the AD continuum. In this work, we sought to detect structural cerebral alterations in CU individuals with discordant status in these amyloid-ß biomarkers using Machine Learning techniques. Method: We included 498 CU individuals from the ALFA+ and ADNI studies with available MRI, amyloid-ß PET and CSF Aß42 measurements, the latter measured with the exploratory Roche NeuroToolKit assays, a panel of automated robust prototype immunoassays. In addition, we calculated Centiloid (CL) values for the PET measurements. Individuals were categorized as CSF-/PET-, CSF+/PET- and CSF+/PET+ according to established cut-offs (CSF Aß42<1098pg/mL for ALFA+ and <880pg/mL for ADNI, and CL<17 for PET). We trained XGBoost classifiers to predict amyloid-ß positivity using as features age, sex, APOE-¿4 status, brain volumes and cortical thicknesses, obtained with Freesurfer 6.0 and the Desikan-Kiliany atlas. Relevant features for pairwise-group classification were sought (CSF-/PET- vs CSF+/PET-; CSF+/PET- vs CSF+/PET+; CSF-/PET- vs CSF+/PET+), calculating SHAP values to determine the most important features for prediction. Result: With respect the CSF-/PET- group, the CSF+/PET- showed decreased gray matter volumes in the anterior and posterior cingulate/precuneus and increases in the lateral ventricles and bilateral parahippocampal gyri, among other regions (Figure 1A). Unexpectedly, the posterior cingulate/precuneus showed the opposite effect in cortical thickness measurements. These patterns were similar but more prominent in the comparison between the CSF-/PET- vs CSF+/PET+ group (Figure 1B). Finally, CSF+/PET- group was characterized, with respect the CSF+/PET+ group by higher volume of the bilateral supramarginal gyri and lower cortical thickness in the posterior cingulate/precuneus (Figure 1C). Regarding the other variables in the model, APOE-¿4 status was the most predictive variable in models with respect the CSF-/PET- group and age in the CSF+/PET- vs CSF+/PET+ comparison. Conclusion: Our results show that model-free machine learning techniques can detect complex brain morphological alterations in the earliest stages of the AD continuum. Interestingly, some regions showed increases in volume and/or cortical thickness which may reflect compensatory or inflammatory effects.Peer ReviewedArticle signat per 18 autors/autores: Irene Cumplido-Mayoral, Mahnaz Shekari, Gemma Salvadó, Grégory Operto, Raffaele Cacciaglia, Carles Falcon, Aida Niñerola-Baizán, Andrés Perissinotti, Carolina Minguillón, Karine Fauria, Maryline Simon, Gwendlyn Kollmorgen, José Luis Molinuevo, Henrik Zetterberg, Kaj Blennow, Marc Suárez-Calvet, Verónica Vilaplana, and Juan Domingo Gispert.Postprint (author's final draft

Biological brain age prediction using machine learning on structural neuroimaging data: Multi-cohort validation against biomarkers of Alzheimer’s disease and neurodegeneration

Background: Brain-age can be inferred from structural neuroimaging and compared to chronological age (brain-age delta), as a marker of accelerated/decelerated biological brain aging. Accelerated biological aging has been found in Alzheimer’s disease (AD), but validation against biomarkers of AD and neurodegeneration is lacking. We studied the association between brain-age delta vs biomarkers and risk factors for AD, neurodegeneration, and cerebrovascular disease in non-demented individuals. Furthermore, between-sex differences in the brain areas that better predicted age were sought. Method: We trained XGBoost regressor models to predict brain-age separately for females and males using volumes and cortical thickness in regions of the Desikan-Kiliany atlas (obtained with Freesurfer 6.0) from the UKBioBank cohort (n=22,661). Using this trained model, we estimated brain-age delta in cognitively unimpaired (CU) and mild cognitive impaired (MCI) individuals four independent cohorts: ALFA+ (nCU=380), ADNI (nCU=253, nMCI=498), EPAD (nCU=653, nMCI=155) and OASIS (nCU=407). Chronological age, sex, MMSE and APOE categories were available for all subjects. ALFA+, ADNI and EPAD cohorts included data for AD CSF biomarkers (Aß42 and p-tau) and amyloid-b/tau (AT) staging was performed using pre-established cut-off values, whereas for OASIS amyloid-b was determined by PET. White Matter Hyperintensities (WMH) were available as a marker of small vessel disease and plasma (ALFA+ and ADNI) neurofilament light (NfL) as of neurodegeneration. Linear regression models, including chronological age and sex as covariates were used to identify associations between brain-age delta and biomarkers. We identified the individuals at the 10th and 90th deciles to select those with higher (accelerated) and lower (decelerated) brain-age delta and tested for interactions between age and all the variables on brain-age delta. Result: Between-sex differences were found in the most predictive brain regions (Figure 1). Brain-age delta was positively associated with abnormal amyloid-ß status, advanced AT stages and APOE-e4 carriership. Furthermore, brain-age delta was positively associated with plasma NfL in MCI patients and an interaction between age and plasma NfL was found on brain-age delta of CU individuals (Figure 2). Conclusion: Biological brain-age can be estimated from structural neuroimaging and is associated with biomarkers and risk factors of AD pathology and neurodegeneration in non-demented individuals.This project has received support from European Prevention of Alzheimer’s Dementia (EPAD) grant no. 115736, Edinburgh, United Kingdom.Peer ReviewedLa publicació està signada per 27 autors/autores: Irene Cumplido-Mayoral 1,2; Marina Garcia 1; Grégory Operto 1,3,4; Carles Falcon 1,3,5; Mahnaz Shekari 1,2,6; Raffaele Cacciaglia 1,3,4; Marta Milà-Alomà 1,2,3,4; Luigi Lorenzini 7; Silvia Ingala 7; Alle Meije Wink 7; Henk-Jan Mutsaerts 7; Carolina Minguillón 1,3,4; Karine Fauria 1,4; Jose Luis Molinuevo 1,3,4,8; Sven Haller 9; Gael Chetelat 10; Adam Waldman 11; Adam J. Schwarz 12; Frederik Barkhof 7,13; Gwendlyn Kollmorgen 14; Ivonne Suridjan 14; Norbert Wild 14; Henrik Zetterberg 15,16,17,18,19; Kaj Blennow 15,19; Marc Suárez-Calvet 1,3,4,20; Verónica Vilaplana 21; Juan Domingo Gispert 1,3,5; ALFA study 22; ADNI study 23 on Behalf Of The EPAD Consortium 24 // 1 Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain; 2 Universitat Pompeu Fabra, Barcelona, Spain; 3 IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain; 4 Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain; 5 Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain; 6 Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain; 7 Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands; 8 Lundbeck A/S, Copenhagen, Denmark; 9 CIRD Centre d’Imagerie Rive Droite, Geneva, Switzerland; 10 Normandie Univ, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Cyceron, Caen, France; 11 Centre for Dementia Prevention, Edinburgh Imaging, and UK Dementia Research Institute at The University of Edinburgh, Edinburgh, United Kingdom; 12 Takeda Pharmaceutical Company Ltd, Cambridge, MA, USA; 13 Institutes of Neurology and Healthcare Engineering, University College London, London, United Kingdom; 14 Roche Diagnostics International Ltd, Rotkreuz, Switzerland; 15 Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden; 16 UK Dementia Research Institute at UCL, London, United Kingdom; 17 Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong; 18 Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; 19 Institute of Neuroscience and Physiology, University of Gothenburg, Mölndal, Sweden; 20 Servei de Neurologia, Hospital del Mar, Barcelona, Spain; 21 Department of Signal Theory and Communications, Universitat Politècnica de Catalunya, Barcelona, Spain; 22 BarcelonaBeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain; 23 Laboratory of Neuroimaging (LONI), University of Southern California, Los Angeles, CA, USA.Postprint (author's final draft

The protective gene dose effect of the APOE ε2 allele on gray matter volume in cognitively unimpaired individuals

Data de publicació electrònica: 08-12-2021Introduction: Harboring two copies of the apolipoprotein E (APOE) ε2 allele strongly protects against Alzheimer's disease (AD). However, the effect of this genotype on gray matter (GM) volume in cognitively unimpaired individuals has not yet been described. Methods: Multicenter brain magnetic resonance images (MRIs) from cognitively unimpaired ε2 homozygotes were matched (1:1) against all other APOE genotypes for relevant confounders (n = 223). GM volumes of ε2 genotypic groups were compared to each other and to the reference group (APOE ε3/ε3). Results: Carrying at least one ε2 allele was associated with larger GM volumes in brain areas typically affected by AD and also in areas associated with cognitive resilience. APOE ε2 homozygotes, but not APOE ε2 heterozygotes, showed larger GM volumes in areas related to successful aging. Discussion: In addition to the known resistance against amyloid-β deposition, the larger GM volumes in key brain regions may confer APOE ε2 homozygotes additional protection against AD-related cognitive decline.The project leading to this study has received funding from “la Caixa” Foundation (ID 100010434), under the agreement LCF/PR/GN17/50300004. Additional support has been received from the Universities and Research Secretariat, Ministry of Business and Knowledge of the Catalan Government under the grant number 2017-SGR-892. EMAU is supported by the Spanish Ministry of Science, Innovation and Universities–Spanish State Research Agency (RYC2018-026053-I)

Biological brain age prediction using machine learning on structural neuroimaging data: Multi-cohort validation against biomarkers of Alzheimer's disease and neurodegeneration stratified by sex

Brain-age can be inferred from structural neuroimaging and compared to chronological age (brain-age delta) as a marker of biological brain aging. Accelerated aging has been found in neurodegenerative disorders like Alzheimer's disease (AD), but its validation against markers of neurodegeneration and AD is lacking. Here, imaging-derived measures from the UK Biobank dataset (N=22,661) were used to predict brain-age in 2,314 cognitively unimpaired (CU) individuals at higher risk of AD and mild cognitive impaired (MCI) patients from four independent cohorts with available biomarker data: ALFA+, ADNI, EPAD, and OASIS. Brain-age delta was associated with abnormal amyloid-β, more advanced stages (AT) of AD pathology and APOE-ε4 status. Brain-age delta was positively associated with plasma neurofilament light, a marker of neurodegeneration, and sex differences in the brain effects of this marker were found. These results validate brain-age delta as a non-invasive marker of biological brain aging in non-demented individuals with abnormal levels of biomarkers of AD and axonal injury.The project leading to these results has received funding from “la Caixa” Foundation (ID 100010434), under agreement LCF/PR/GN17/50300004 and the Alzheimer’s Association and an international anonymous charity foundation through the TriBEKa Imaging Platform project (TriBEKa-17–519007). Additional support has been received from the Universities and Research Secretariat, Ministry of Business and Knowledge of the Catalan Government under the grant no. 2017-SGR-892 and the Spanish Research Agency (AEI) under project PID2020-116907RB-I00 of the call MCIN/ AEI /10.13039/501100011033. FB is supported by the NIHR biomedical research center at UCLH. MSC receives funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 948677), the Instituto de Salud Carlos III (PI19/00155), and from a fellowship from ”la Caixa” Foundation (ID 100010434) and from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 847648 (LCF/BQ/PR21/11840004)