The inverse synthetic aperture radar (ISAR) image is a kind of target feature data acquired by radar for moving targets, which can reflect the shape, structure, and motion information of the target, and has attracted a great deal of attention from the radar automatic target recognition (RATR) community. The identification of ISAR image components in radar satellite identification missions has not been carried out in related research, and the relevant segmentation methods of optical images applied to the research of semantic segmentation of ISAR images do not achieve ideal segmentation results. To address this problem, this paper proposes an ISAR image part recognition method based on semantic segmentation and mask matching. Furthermore, a reliable automatic ISAR image component labeling method is designed, and the satellite target component labeling ISAR image samples are obtained accurately and efficiently, and the satellite target component labeling ISAR image data set is obtained. On this basis, an ISAR image component recognition method based on semantic segmentation and mask matching is proposed in this paper. U-Net and Siamese Network are designed to complete the ISAR image binary semantic segmentation and binary mask matching, respectively. The component label of the ISAR image is predicted by the mask matching results. Experiments based on satellite component labeling ISAR image datasets confirm that the proposed method is feasible and effective, and it has greater comparative advantages compared to other classical semantic segmentation networks. Space target recognition is the main function of space surveillance information systems, where satellites in orbit play an important role and are key targets for recognition. However, there are few published research results in this area. The difficulty of this problem is that satellites are too small or too far away to identify detailed information, and it is relatively difficult to obtain effective identification data. Compared with other methods of obtaining target characteristic information, radar, as a radio detection device, has inherent advantages such as all-weather, real-time, and long detection range. These advantages make it an important sensor to realize the automatic identification of sea, air, and sky targets. Moreover, with the rapid development of modern radar technology, the rapid emergence of broadband radar has greatly expanded the information acquisition capability of radar systems for targets and environments, making broadband radar an important means to obtain target characteristic data. These data mainly include high-resolution range images (HRRP), inverse synthetic aperture radar (ISAR) images, and synthetic aperture radar (SAR) images, which are widely used in radar automatic target recognition (RATR) research ,2. In saResearch on satellite target recognition technology based on ISAR images has been carried out, and deep learning and sample-less learning methods have been used to identify the specific model of satellite targets, and more satisfactory results have been achieved ,7. The rTo solve the above challenges, this paper adopts the designed annotation method to realize the automatic annotation of satellite target ISAR image components, generates a large number of component annotated ISAR image samples for satellite target simulation, and then constitutes the corresponding component annotated ISAR image dataset, which lays the data foundation for the subsequent research of satellite target component recognition technology based on ISAR images. Then, based on the characteristics of ISAR image data and the difficulties of component recognition, a method of ISAR image component recognition based on semantic segmentation and mask matching is proposed. Finally, the proposed network is trained and tested on the component labeled ISAR image dataset to improve the accuracy and generalization ability of the network, to realize the component recognition of satellite target ISAR images. Its features are summarized as follows:(1) An ISAR images component recognition method based on semantic segmentation and mask matching is proposed, which uses U-Net and Siamese network to realize ISAR images’ binary semantic segmentation and binary mask matching for the first time, respectively, and the mask matching result is used to predict ISAR images’ component labels. It is confirmed by experiments that the proposed method is feasible and effective for ISAR image component recognition and has significant advantages over classical image semantic segmentation models.(2) Designed a reliable automatic ISAR image component labeling method to obtain accurate and efficient satellite target component labeling ISAR image samples, and a satellite target component labeled ISAR image dataset is obtained with the difficulty of ISAR image component recognition as a theoretical guide.The rest of this paper is organized as follows: in In the field of RATR, there are basically no relevant literature reports on the study of semantic segmentation of ISAR images, and the research at this stage is still mainly focused on ISAR target recognition. However, in the field of optical image and SAR remote sensing, research on semantic segmentation is still a popular area of study, and there is a large amount of literature related to the research of target classification and localization , target Before deep neural networks were proposed, feature extraction and classification algorithm design were two of the most important topics in traditional semantic segmentation research. Scholars proposed or adopted many features for semantic segmentation research, such as pixel color, directional gradient histogram , scale-iSince the rise of deep neural networks, especially the proposed CNN-based Fully Convolutional Network (FCN) , the accThis class of methods uses an encoder structure consisting of convolution and pooling operations to extract the image feature mapping, and then uses a decoder structure consisting of upsampling to recover the extracted feature mapping to the initial spatial dimension of the input image. Typical networks of this class of methods include SegNet , U-Net , RefineNIn semantic segmentation research, conditional random field is usually executed as a post-processing step, which takes into account the underlying information of the image (such as inter-pixel relationships) when generating pixel labels, which is very beneficial to the local detail optimization of segmentation results; DeepLab v1 , DeepLabConvolution with holes, also known as null convolution, is introduced into CNNs to increase the CNN perceptual field exponentially without introducing additional parameters. The advantage of convolution with holes is that it can obtain multi-scale image information, which also makes it unnecessary for CNN to use large-scale pooling operations to expand the perceptual field, thus avoiding the problem of image fine-grained information loss caused by multiple pooling operations. The classical semantic segmentation network models applying convolution with holes include the DeepLab series ,31,32 anThe basic idea of multi-scale feature fusion is that by fusing features at different scales, the feature information of the target can be learned more effectively, which in turn is beneficial to further improve semantic segmentation accuracy. Typical multi-scale feature fusion methods contain atrous spatial pyramid pooling (ASPP), cascade structure, etc. Based on DeepLab v1 convolution with holes, DeepLab v2  and DeepThe basic idea of these methods is to integrate feature mappings at different scales and to find the optimal balance between local and global information. Existing studies have shown that to improve the recognition accuracy of semantic segmentation, both the integration of feature mappings at different scales and the balance between local and global information are required. In semantic segmentation research, using local information is beneficial to improve the classification accuracy at the pixel level, while introducing global information is a key way to solve the local ambiguity problem. Among them, typical methods include conditional random field, convolution with holes, multi-scale feature fusion, etc., which are described as follows:In image semantic segmentation tasks, scholars have proposed several kinds of semantic segmentation loss functions for different task situations, which play an important role in improving semantic segmentation performance. In the following, several commonly used semantic segmentation loss functions are briefly introduced to lay the foundation for the subsequent experiments of satellite target ISAR image component recognition.Cross-Entropy LossCross-entropy loss is usually used to measure the difference in the probability distribution of a given random variable or event and is widely used in classification tasks, including semantic segmentation tasks for pixel-level classification. It is used as a measure of the distance between the probability distributions  scatter , and the2.Weighted Cross-Entropy LossWhen the image pixels contain an unbalanced proportion of categories, the classifier tends to focus more on the category with the largest number of samples, making the classification performance biased towards a specific category. In semantic segmentation studies, the number of pixels in various categories is likely to be very different, and this category imbalance is a frequent occurrence. To solve the above problem, it is common practice to utilize the weighted cross-entropy loss, which is a variant of the standard cross-entropy loss with an additional category weight parameter 3. Dice lossIn the field of computer vision, the Dice coefficient is widely used to calculate the similarity of two images, and then it is further improved into a loss function , whose e4. Focal LossFocal loss is a new variant of weighed cross-entropy loss, as shown in Equation (4), whose important feature is to construct the weight coefficients as a function of the network prediction confidence. By doing this, the difficult-to-predict categories will obtain higher losses than the easy-to-predict categories.It uses two additional scaling factors, 5. Lovasz–Softmax LossIn image semantic segmentation tasks, the Jaccard metric (IoU) is usually used to evaluate image segmentation results. Compared with pixel accuracy (PA), the IoU metric gives proper attention to small targets and counts false negatives (FN) correctly, which is a more suitable semantic segmentation evaluation metric. For this reason, scholars considered optimizing the IoU loss directly during the network training process and designed and proposed a new semantic segmentation loss function, namely Lovasz–Softmax loss . AssuminThis metric gives the ratio of the intersection of the set of true and predicted labels to their concurrent sets and has a value in the range In addition, for submodular functions such as Jaccard loss, they further propose to use Lovasz expansion to efficiently minimize the loss function.In this study, we propose a semantic segmentation and mask-matching-based ISAR image component recognition method, which is shown schematically in Currently, scholars generally adopt deep learning methods to carry out optical image semantic segmentation research, and various network structures have been designed and proposed to implement image semantic segmentation. Among these successfully applied semantic segmentation network models, the fully convolutional-based U-Net  uses a smth row and the nth column, b represents the bias parameter, the convolution kernel’s width and height are 3 and 3, respectively, ith + mth row and jth + nth columns of the input image, and i and j represent the convolution kernel sliding over the input image by row i and columns j, respectively, f(⋅) represents the nonlinear activation function, and sth row and tth column of the output feature map of the convolution operation.The network structure of U-Net is shown in The nonlinear activation function used in the convolutional neural network is the Sigmoid function, which is defined as follows:Each module starts by doubling the size of the feature mapping and halving the number of channels through an up-convolution operation, and then the resulting feature mapping is connected to the feature mapping of the symmetric compressed path on the left, and the same convolution is performed on the combined feature mapping. Since this subsection first considers only the distinction between the foreground and background of the ISAR image satellite target, which is a binary semantic segmentation task, the feature mapping with a channel number of 2 is finally input.After having the semantic segmentation network model of the ISAR image, the binary mask of the ISAR image of the satellite target can be predicted. On this basis, it is further required to match the mask with the samples in the part labeled ISAR image dataset to predict the part labels of the ISAR image test samples and realize the ISAR image part recognition. Siamese networks for computing sample similarity have been successfully used in application scenarios such as face recognition, fingerprint recognition, and signature verification ,40; therThe ISAR images mask matching Siamese network model designed in this study is shown in It should be emphasized that the feature extraction modules of the two input images have the same network structure and share the network parameters, so the similarity of the original images can be measured by using the distance of their feature vectors when the network models are the same.When carrying out Siamese network model training, it is also necessary to design the appropriate loss function to make the network model parameters optimal in the process of reducing the loss function. In this study, the contrastive loss (CL) function is used , which cThis section first prepares the satellite target ISAR images component annotation data for the network model training, then gives the evaluation metrics and loss functions of the network model, and finally presents the comparative analysis of the results with other classical images semantic segmentation models to highlight the advantages of the satellite target ISAR images component recognition method proposed in this study.Under the condition of having enough information about the satellite target, the designed automatic labeling technique is used to realize the satellite target ISAR image component labeling.Step 1: Satellite target 3D model component labeling. The 3Dmax-2020 software is used to be able to draw a reliable 3D model of the satellite target, based on which the software is further used to label the surface elements of each component of the satellite target, and the labeled 3D model file is converted and output into .3ds format. Step 2: Parsing the surface element information of each component of the 3D model. The OpenGL-based 3D model surface element analysis software is used for surface element analysis of the component labeled 3D model obtained from Step 1. The resolved file information not only contains the position coordinates and normal vector of each surface element in the satellite body coordinate system, but also the name of the component to which each surface element belongs.Step 3: Determine the imaging parameters of the ISAR system and carry out the ISAR imaging simulation of the satellite target. Based on the surface element information of the satellite target obtained from Step 2 and the observable arc segment information of the satellite target obtained from STK11.3 software simulation, the MATLAB-based radar data processing and imaging application software is used to perform ISAR imaging of the satellite target, including specifying the imaging parameters of the ISAR system, dividing the observable arc segment into multiple imaging sub-apertures according to the imaging time, calculating the observable elements and its scattering coefficient, as well as generating ISAR broadband signals and satellite target echoes, and using the echo information for range-doppler imaging. Set the ISAR system imaging parameters to a signal carrier frequency of 14 GHz, a bandwidth of 2 GHz, and an imaging buildup time of 20 s.Step 4: Determine the component names of the visible elements and the ISAR imaging coordinates. Based on Step 2 and Step 3, the component names of the visible elements of the satellite target 3D model can be determined; meanwhile, based on the distance between each face element in radial and azimuthal directions and the imaging center, as well as the distance unit length and doppler resolution of ISAR imaging, the coordinates of each face element in the ISAR image can be obtained, and then the pixel distribution of each component of the target in the ISAR image can be obtained. This enables the automatic labeling of satellite target ISAR image components.In this study, the following two principles are followed in the selection of satellite targets: the overall size of the satellite is as large as possible to increase the number of pixels of the target in the ISAR image; the number of satellite components is not too many, and the distribution is not particularly complex. Therefore, two satellites, Meteor-M-1 and RISAT-1, are selected for component identification in this study. Their 3D model component annotations are shown in In image semantic segmentation tasks, the Jaccard metric is commonly used to evaluate the segmentation results. This metric, also known as the Intersection-over-Union (IoU) score, is the ratio of the intersection of two sets to the concatenation, and for semantic segmentation tasks, is expressed as the intersection ratio between the predicted label set and the true label set. In addition, individual pixel-based evaluation metrics are also commonly used to assess the quality of semantic segmentation results, such as Pixel Accuracy (PA), which is the ratio of the number of correctly predicted pixels to the total number of pixels in a semantically segmented image. For a multi-category image semantic segmentation task, the IoU scores and PA of all categories are generally averaged, i.e., Mean Intersection-Over-Union (MIoU) and Mean Pixel Accuracy (MPA). Assuming that the semantic segmentation image has a total of The ISAR image component identification method proposed in this study includes two network models, i.e., semantic segmentation and mask-matching network models, and data partitioning of satellite target component-labeled ISAR images is required before carrying out network training. In this study, all the part-labeled ISAR image samples of each satellite target are equally divided into two components, i.e., ISAR image test samples and ISAR images matching datasets with known component labels, where the ISAR image test samples are only used in the testing phase of the Siamese network model, and since the component labels of the samples in the ISAR images matching database are known, they can be used in the U-Net training and testing phases, as well as the training phase of the Siamese network. In this paper, we used a total of 1360 ISAR image samples, which were divided into ISAR image test samples and ISAR image matching datasets. Among them, a dataset of ISAR images with known labels was used, consisting of 680 samples. During the training and testing phases of the models, 476 training images and 204 testing images were employed for the U-Net model, while 680 training images and 680 testing images were used for the Siamese network. The training process of the U-Net semantic segmentation model and Siamese mask matching model is described in detail below.When conducting the training of the U-Net semantic segmentation network model, all ISAR image samples are divided into ISAR image training samples and test samples in the ratio of 7:3, and 30% of the ISAR image training samples are used as the validation set to adjust the hyperparameters of the network training and to determine the best network model parameters. Since the U-Net semantic segmentation network designed in this study only outputs the binary classification mask of the ISAR image, the loss function used is the Binary Cross-Entropy loss function, whose expression is shown in Equation (3), and the Adam optimization algorithm with an initial learning rate of 0.001 is used to reduce this loss. The training process of the U-Net network model with 100 Epochs is recorded in During the training of the Siamese mask matching network model, similar to the U-Net semantic segmentation network model training, 30% of the ISAR image training samples were used as the validation set. Specifically, a total of 204 ISAR image training samples were selected as the validation set to fine-tune the hyperparameters of the S network training and determine the optimal network model parameters. When training the Siamese mask matching network model, an important task is to construct ISAR image binary mask matching sample pairs. In the pair construction process, the ISAR image binary mask sample is randomly rotated by a small angle and used as its own ISAR image binary mask matching sample; meanwhile, the binary mask sample of other ISAR images is randomly selected and randomly rotated by a larger angle and used as its ISAR image binary mask mismatch sample. At this point, pairs of ISAR image binary mask matched and unmatched samples are successfully constructed. The size of all ISAR image binary mask samples is 320 × 320, and the batch size is 8 at the time of sample collection. The loss function is the contrast loss (Equation (10)), and the Stochastic Gradient Descent (SGD) optimization algorithm with a learning rate of 0.001 and a momentum of 0.9 is used to reduce the training loss value to optimize the Siamese network model parameters. The training records of the Siamese network on the binary mask samples of Meteor-M-1 and RISAT-1 satellites are shown in To prevent overfitting caused by limited datasets, we adopted two methods in this study. Firstly, we performed various data augmentation operations on the dataset, such as rotation, flipping, and folding. Through these operations, we extended the initial dataset. This data augmentation process helps to introduce more diversity and variability into the dataset, allowing the model to learn from a wider range of examples. Therefore, through data augmentation, we reduced the risk of overfitting and improved the model’s generalization ability for unseen examples. Secondly, satellites are generally composed of a body, solar panels, and sensors, so imaging them will generate similar ISAR satellite images. Based on this observation, we developed a specific ISAR image component automatic annotation technique to capture the features of such images. By training our model based on enhanced data and well-designed image processing techniques, we ensure that it has the necessary capabilities to process ISAR images.After the training of the two network models is completed, the binary mask samples from the ISAR image test samples and the ISAR image matching database are input to the U-Net semantic segmentation network model and the Siamese mask matching network model, respectively, and the binary mask prediction results of the ISAR image test samples, as well as the ISAR image binary mask matching results and the predicted part labels can be obtained, respectively. The experimental results of the proposed method are presented below in terms of both the ISAR image binary semantic segmentation results and the ISAR image mask matching and component identification results.In the previous subsection, the ISAR image training samples and validation samples have been used as inputs to train the network for the ISAR image U-Net semantic segmentation model, and the parameter-optimized binary U-Net semantic segmentation model has been obtained. Here, the divided ISAR image test samples are used as inputs to test the performance of the above model for binary classification semantic segmentation. The binary semantic segmentation results of the U-Net model on the ISAR image test samples are given in The U-Net model can predict the binary masks of ISAR image test samples more accurately, which in turn provides a good basis for ISAR image mask matching and component identification. As shown in To compare the superiority of the proposed semantic segmentation network, this subsection first uses traditional image segmentation methods to carry out a comparative study on the performance of binary semantic segmentation, including thresholding method, K-mean clustering, and gradient-based image segmentation. The performance of binary semantic segmentation with four methods for the ISAR images of Meteor-M-1 and RISAT-1 satellites is given in To further illustrate the advantages of the proposed method, In this subsection, three current classical image semantic segmentation neural network models, DeepLab v3+ , U-Net , and PSPCompared with the classical image semantic segmentation model, the experimental results of this study achieve higher IoU scores, which proves that the proposed method has obvious advantages in ISAR image components recognition. Meanwhile, subsequent scholars can realize more accurate ISAR image part recognition by improving the U-Net and Siamese networks or designing more appropriate semantic segmentation models and matching models. An important point is that the results of this study provide additions to the existing literature in the field of space engineering. Through our study, we propose a semantic segmentation and mask-matching-based ISAR image part recognition method, which employs the designed U-Net and Siamese networks to implement ISAR image binary semantic segmentation and binary mask matching, respectively, and use the mask matching results to predict the ISAR image part labels. The experimental results confirm the effectiveness and feasibility of this method, which provides new perspectives and ideas for researchers in the field of space engineering. The proposed method can also increase the number of samples in the satellite target component annotation ISAR image dataset, such as using ISAR image rotation, scaling, and flipping to enhance the data, to achieve the optimal matching between the ISAR image test samples and the dataset samples, which can further improve the ISAR image component recognition performance of the proposed method and can be successfully applied to the satellite target ISAR image component identification task.We recognize that our method may have some limitations when multiple satellite target components need to be identified simultaneously, especially when dealing with satellite targets with similar form factors and considering the possible negative impact of ISAR imaging parameters. In such cases, the recognition accuracy of our method may be degraded. At this point, it is necessary to improve the above U-Net and Siamese networks or design more appropriate semantic segmentation models and matching models to achieve more accurate ISAR component recognition. Meanwhile, the recognition accuracy of this research method also relies on the sample completeness of the satellite target component-labeled ISAR image dataset.In this paper, an automatic ISAR image component labeling method is designed to obtain satellite target component labeling ISAR image samples and datasets accurately and efficiently. Based on this, a semantic segmentation and mask-matching-based ISAR image component identification method is proposed, and the designed U-Net and Siamese networks are used to implement ISAR image binary semantic segmentation and binary mask matching, respectively, and the mask matching results are used to predict the component labels of ISAR images. Based on several classical semantic segmentation network models and loss functions, experiments on multiclassification semantic segmentation of ISAR images are conducted, and their experimental results are compared and analyzed with the proposed method in this study, and the results confirm that the proposed method has significant advantages.


The shortage of primary care physicians is a growing crisis that threatens the stability and effectiveness of healthcare systems. This paper explores a multi-pronged approach to addressing this issue by focusing on the modernization of medical curricula, the establishment of new medical schools, fostering collaboration between institutions, and implementing policy innovations. The cases of South Tyrol, Italy, and Tyrol, Austria, are examined to highlight the challenges faced in establishing new medical schools. This paper proposes that a comprehensive strategy, including the incorporation of general practice content and experience in medical education, is crucial for preparing future physicians for careers in primary care. Furthermore, intensifying efforts to establish new medical schools, particularly in regions such as South Tyrol, which lack native-language medical university education, can provide additional benefits in addressing regional needs and augmenting the number of graduates. Collaboration between existing and new medical schools, regional partnerships, and policy innovations are essential to support the establishment of institutions with a particular focus on general practice and the modernization of curricula at existing universities. By embracing this approach, stakeholders can collectively shape the medical education landscape and address the growing crisis of physician shortages in primary care. General practice is the cornerstone of healthcare, serving as the first point of contact for patients seeking medical attention . HoweverIn numerous nations, the primary issue revolves around the escalating scarcity of general practitioners (GPs), particularly in rural regions and other areas with inadequate services. While the total number of doctors per capita has risen in every country, the proportion of GPs has declined in the majority. As of 2020, a mere 20% of doctors were GPs, on average, throughout the European Union countries .Medical universities have a significant influence on shaping the career choices and expertise of future physicians ,5,6. In Medical-center-focused curricula in many medical universities may inadvertently contribute to a shortage of medical personnel in general practice . By emphThe Wass Report , titled By integrating general practice into medical curricula, students gain a comprehensive understanding of the healthcare system, including the critical role that primary care plays in promoting overall health and preventing diseases . This hoTo provide more primary care providers, medical schools must create an environment in which primary care is supported as a career choice. Medical schools should also consider educational models that incorporate regional campuses and rural educational settings . The sloImplementing a systemic approach often requires significant investments in infrastructure, technology, and faculty development. Public universities, which are typically reliant on government funding, may struggle to secure the resources necessary to support these changes, especially in the face of competing priorities and budget constraints .Although there is growing support for the adoption of systemic approaches in medical education, a consensus on the most effective methods and strategies is still emerging. This lack of agreement can make it difficult for universities to identify and implement the most appropriate changes to their curricula. While systemic approaches to medical education have been advocated for many years , robust In 2016, Switzerland increased the admission rate of medical students by 25 percent through new openings in five additional medical schools . A new mThe commercialization of medical education has led to the emergence of new medical schools that are primarily focused on generating profits. These money-making institutions may prioritize their financial interests over the quality of education and training, which can negatively impact the development of competent and dedicated physicians . While sIn South Tyrol, an autonomous province in Italy where the majority speaks German as a second national language  and healTo address these issues, the South Tyrolean government proposed the establishment of a publicly funded medical school in 2012 ,23. HoweThis outcome underscores the complex interplay between sociopolitical factors and educational policies in shaping the landscape of medical education and its impact on addressing the shortage of medical personnel in general practice.In Tyrol, Austria, the local government recognized the falling number of students at its medical university and, thus, the impending personnel shortage in primary care  and commThe proposed project adopted a cross-border collaborative approach involving both Tyrol and South Tyrol, integrating most of the public hospitals and services within the Tyrolean and South Tyrolean Health systems ,30. The However, despite comprehensive preparations, the project encountered opposition from Austria’s three public state medical universities at the time ,33, inclIn scientific terms, this case illustrates the challenges of implementing innovative medical education initiatives in the face of institutional resistance and complex sociopolitical dynamics. This underscores the need for strong collaboration and consensus building among stakeholders to overcome barriers and ensure the successful development of medical education programs that address the shortages of primary care physicians.To address the personnel shortage in general practice, existing state medical schools must undertake a comprehensive modernization of their curricula . This enCollaboration between established medical schools and new institutions focused on general practice can play a crucial role in addressing the shortage of medical personnel in primary care . EstabliIn turn, new medical schools can bring new perspectives and innovative approaches to medical education, benefiting the broader academic community. Policymakers must recognize the potential of these collaborations and implement measures that support the establishment of new medicine curricula dedicated to general practice and rural medicine . This coThe existing collaborations in South Tyrol, both with Cattolica University’s private medical school in Rome, Italy, for health professions  and withPartner institutions can collaborate to provide the necessary infrastructure and resources. They can also support the recruitment and training of high-quality faculty members, ensuring that the new school has access to a diverse range of medical-education expertise. The joint committee should work closely with relevant accreditation bodies and regulatory agencies to ensure that the new medical school meets all necessary requirements for accreditation and recognition, both nationally and internationally. Thus, by building on existing collaborations and utilizing the expertise of partner institutions, South Tyrol can establish a new medical school that addresses the region’s healthcare needs and contributes to a more robust primary care workforce.Policymakers can consider implementing policy changes to support the establishment of new medical schools focused on general practice . These mCollaboration between new medical schools and established institutions, such as universities or healthcare organizations, could involve creating formal partnership agreements, facilitating knowledge-sharing opportunities, or providing funding for joint initiatives focused on general-practice education .Policies that support the integration of interprofessional education and collaboration within new medical schools could include funding for interdisciplinary training programs, encouraging the development of shared curricula, or mandating interprofessional education as a requirement for accreditation . ResourcBy implementing these policy changes, governments can create a supportive environment for the establishment of new medical schools that prioritize general practice, ultimately contributing to a stronger primary care workforce and improved healthcare outcomes. Potential solutions are summarized in The solutions presented in this table provide general approaches to address the shortage of general practitioners. Region-specific solutions for South Tyrol can be found in The experiences of South Tyrol, Italy, and Tyrol, Austria, highlight the challenges faced in establishing new medical schools and modernizing curricula to prioritize general practice. These cases emphasize the importance of engaging stakeholders from various domains and fostering open dialogue and consensus-building to facilitate meaningful change in medical education. By involving a diverse range of voices and perspectives, we can address the sociopolitical factors, bureaucratic hurdles, and institutional resistance that often hinder the implementation of innovative medical-education initiatives.One crucial aspect of fostering collaborative change in medical education is the active involvement of stakeholders beyond traditional healthcare professionals. This includes education specialists, literature professors, social workers, philosophers, clergy, and local non-profit organizations. By bringing together professionals from these diverse fields, we can create a platform for interdisciplinary dialogues that enrich the educational experience and promote a comprehensive understanding of primary care.Education specialists contribute their expertise in pedagogical advancements and innovative teaching methodologies. Their input helps shape curricula that effectively prepare future physicians for the challenges of primary care, integrating practical skills, critical thinking, and lifelong learning strategies.Literature professors play a vital role in emphasizing the importance of effective communication skills, empathy, and the ability to understand patients’ narratives and experiences. By exploring literary works that highlight the humanistic dimensions of medicine, students gain insights into the complexities of patient care and develop a deeper appreciation for the patient’s perspective.Social workers offer valuable insights into the social determinants of health and the impact of socioeconomic factors on healthcare access. Their collaboration with medical schools allows for the integration of community-oriented approaches, fostering an understanding of the broader context in which primary care operates.Philosophers provide a philosophical foundation for medical practice, encouraging students to reflect on ethical considerations, professionalism, and the values that underpin their future roles as physicians. Their perspectives promote critical thinking, ethical decision-making, and the exploration of the moral dimensions of primary care.Clergy members contribute insights into the spiritual and psychosocial aspects of patient care, recognizing the significance of holistic approaches in promoting overall well-being. Their involvement helps foster a deeper understanding of the psychosocial factors that impact patients’ health and the importance of compassion and empathy in delivering patient-centered care.Local non-profit organizations play a crucial role in bridging the gap between medical education and community needs. By partnering with these organizations, medical schools gain practical insights into community-based healthcare initiatives, allowing students to engage directly with the local population and address the specific healthcare challenges of the region.Through collaborative dialogues that involve stakeholders from various disciplines, we can create an educational environment that fosters interdisciplinary learning, empathy, and a comprehensive understanding of primary care. By recognizing the valuable contributions of professionals from diverse fields, we enhance the relevance and quality of medical education, preparing future physicians to meet the complex healthcare needs of their communities.In conclusion, engaging stakeholders from education, literature, social work, philosophy, clergy, and non-profit organizations is essential for driving collaborative change in medical education. Their expertise and perspectives enrich the educational experience, ensuring the holistic preparation of future physicians for primary care practice. By embracing interdisciplinary dialogues and fostering open dialogue and consensus-building, we can overcome barriers and create a medical education system that aligns with the evolving healthcare landscape.Although the original concepts from Canada and Australia with a much stronger adaptation of curricula to primary care content have been proposed for more than 20 years to counter GP distribution disparity, including an increased admission of students from rural areas for later work in rural areas , the proThe engagement of stakeholders in open dialogue and consensus building helps address the complex challenges faced in establishing new medical schools and modernizing existing curricula. By embracing these calls for action, the medical education landscape can evolve to better address the needs of primary care and ensure the sustainability of the healthcare system.The growing crisis of medical personnel shortages in primary care poses a significant threat to the stability and effectiveness of healthcare systems. Addressing this issue requires a multi-pronged approach that encompasses various aspects, including those of medical-education-modernizing curricula at established medical universities, intensifying efforts to establish new medical schools , fostering collaboration between institutions, and implementing policy innovations to support these initiatives. This comprehensive strategy can effectively address the diverse challenges faced by the healthcare system and promote the development of a well-prepared primary care workforce. Modernizing existing curricula at established medical universities by incorporating general-practice content and experiences, emphasizing community-based settings, and integrating innovative teaching methodologies can better prepare future physicians for careers in primary care, ultimately helping address the shortage of medical personnel in general practice.However, the establishment of new medical schools offers additional advantages beyond curriculum modernization and improved didactics. New medical schools can accommodate more students, thus increasing the number of graduates, which is something that state universities may be limited in due to financial constraints. Additionally, access to new schools can be regulated to address regional needs by considering factors such as social characteristics. South Tyrol, in particular, faces challenges due to its dependence on foreign-state medical schools in the German language, as there are no local medical schools in the region. In contrast to Tyrol, which already has the Medical University of Innsbruck, efforts to establish a new medical school in Southern Tyrol should be intensified. This would help to address the unique challenges faced by the region and create opportunities for curriculum innovation and regional access to medical education.In summary, while modernizing existing curricula remains crucial, intensifying efforts to establish new medical schools, particularly in regions such as South Tyrol, can provide significant additional benefits in addressing the needs of primary care and ensuring the sustainability of the healthcare system. By embracing this call for action, stakeholders can collectively shape the medical education landscape and better address the growing crisis of medical-personnel shortages in primary care.


The present review attempts to discuss how some of the central concepts from the Lurian corpus of theories are relevant to the modern neuropsychology of epilepsy and epilepsy surgery. Through the lenses of the main Lurian concepts , we discuss the barriers to clinical reasoning imposed by quadrant-based views of the brain, or even atheoretical, statistically-based and data-driven approaches. We further advice towards a systemic view inspired by Luria's clinical work and theorizing, given their importance towards our clinical practice, by contrasting it to the modular views when appropriate. Luria provided theory-guided methods of assessment and rehabilitation of higher cortical functions. Although his work did not specifically address epilepsy, his theory and clinical approaches actually apply to the whole neuropathology spectrum and accounting for the whole panorama of neurocognition. This holistic and systemic approach to the brain is consistent with the network approach of the neuroimaging era. As to epilepsy, the logic of cognitive functions organized into complex functional systems, contrary to modular views of the brain, heralds current knowledge of epilepsy as a network disease, as well as the concept of the functional deficit zone. Epilepsy surgery (ES) constitutes an effective alternative treatment option for patients with refractory focal epilepsy. Patients are characterized as pharmacoresistant according to the International League Against Epilepsy (ILAE), definition of drug-resistant epilepsy,Advances in surgical techniques have improved seizure control outcomes and the quality of life of patients.Although advanced electrophysiological and neuroimaging methodologies provide high-quality diagnostic data, the information provided by preoperative neuropsychological evaluations may significantly contribute to the characterization of the functional deficit zone,It is precisely here that the mettle of Luria's contribution to neuropsychology is tested and emerges from the fire as true cultural neuropsychology….”Despite this shift in the state of clinical affairs, the neuropsychology of ES is a domain in which serious concerns are raised as to the effectiveness of both biomedical technology and mainstream neuropsychology in solving diagnostic dilemmas. A strong criticIt should not be omitted that besides neuropsychological monitoring, psychiatric assessment is another major although often neglected issue in epilepsy presurgical workup, since active psychopathology may affect postoperative seizure freedom; surgery may lead to the development of de novo psychiatric disorders, and in many instances reverse a preexisting psychiatric condition. This calls for a comprehensive psychiatric assessment of ES candidates pre- and postsurgery to minimize the risk of postsurgical psychiatric morbidities and/or poor quality of life.neuropsychologyANDepilepsysurgery, ANDLuria/Neolurian/ ANDtheory/model/approachANDcognitive/neuropsychologicalANDmemory/learningANDexecutiveANDfunction/performanceANDpresurgical/preoperativeANDpostsurgical/postoperativeANDassessment/evaluation/monitoringANDtemporal lobe epilepsy/frontal lobe epilepsyANDneuroimaging. From this search, 7 epilepsy surgery studies, 3 on frontal lobe epilepsy, 11 on temporal lobe epilepsy, 7 on memory and learning, 4 on neuroimaging, 3 on neural networks, and 10 on Luria's work and implications, were fully screened, and related accidents were included.The literature search conducted in the present paper comprised empirical research studies, reviews, books and chapters along with references included within identified works. The Scopus, PubMed, and Google scholar databases were used as search engines. Only papers in English were considered. The last search was conducted in February 2022. The search terms were the following:With the present review, we summarize the central concepts of the Lurian theory , their relevance and implications to the modern neuropsychology of epilepsy and ES underscoring our Lurian-based practice, as well as the barriers to clinical reasoning imposed by quadrant-based views of the brain, or even atheoretical, statistically-based and data-driven approaches. We further advise towards a systemic view inspired by Luria's clinical work and theorizing, given their importance towards our clinical practice, by contrasting it to the modular views when appropriate.As already mentioned, one of the main goals of presurgical neuropsychological assessment is to highlight the so-called functional deficit zone, that is, the array of brain areas showing cognitive dysfunction and/or deficits between seizures. This may involve an extensive constellation of regions with either intrahemispheric and/or interhemispheric anatomical distributions. Thus, informing neurosurgeons and neurologists as to the main nodes making up the epileptic network may increase surgical outcome.Importantly, this is consistent with the current epistemological trends suggesting a shift from the concept of epileptic focus to that of epileptic network, looking at epilepsy as a neuronal network disease.Evidence has documented the potential of the preoperative neuropsychological assessment aiding in seizure lateralization when EEG and MRI findings fail to provide clear lateralization data.For decades, the neuropsychology of epilepsy has been dominated by modular brain theories , which on the one hand have greatly contributed to the understanding of epilepsy-related neurocognitive impairment and its neurobiological determinants, but on the other have posed considerable obstacles to clinical understanding.(see below). Consequently, they are of great applicability in the context of the preoperative neuropsychological monitoring of patients-candidates for ES, where neuropsychologists strive towards the anatomical mapping of neuropsychological deficits to aid surgeons.Luria produced a theory corpus that, although not exclusively focused on epilepsy, appears to apply to the complete range of neuropathology. His holistic and systemic approach of the brain is consistent and to some extent foretold modern network approaches stemming from neuroimaging. Regarding epilepsy, the logic of cognitive functions organized into complex functional networks, contrary to modular views of the brain, seems to herald current knowledge of epilepsy as a network disease, as well as the concept of the functional deficit zone. These contributions seem to be of capital importance for the neuropsychology of ES, since they provide valuable methods and theories to aid in the localization – and lateralization – of cognitive deficitsTo some authors, conventional neuropsychological measures can hardly differentiate seizures with different focal onseţ with specific associations between neuropsychological deficits and types of epilepsy representing mostly exceptions,This failure in topographic distinction of seizure onset on the basis of test performance-related impairments further emphasizes the need for a wider view of cerebral functional organization extending beyond mere ¨localizationism¨, performance-based, or even preconceived and/or psychometrically-based construct validity assumptions of cognitive measures.A quick glance at the historical studies of surgical patients suffering from intractable temporolimbic epilepsiesThe complex inferential reasoning that drives diagnostic hypotheses has progressively been substituted by almost reflexive clinical automatisms, such as those seen when administering standardized cognitive tests and interpreting the data collected, stemming from modular and performance-based neuropsychological approaches. This reflects a rather mechanistic and nontheoretical perspective. Reliance upon diagnostic hypotheses formed on the basis of clinical observation, as well as scientific contextualization of findings and assumptions in the light of a theory are lacking. As LuriaInterestingly, the current construct of the functional deficit zone, that is, the part of the brain showing dysfunction interictally, as suggested by objective neurological examination, neuropsychological screening, and functional neuroimaging (including fMRIs and FDG (fluorodeoxyglucose) PET (positron emission tomography)-scanning) is consistent with Luria's systemic view. This is of great importance for the neuropsychology of epilepsy and the preoperative neuropsychological monitoring of patients who are candidates for ES, in particular. This is because the neuropsychologist is called to offer the anatomical distribution of cognitive and neurobehavioral dysfunction which manifests itself during the assessment conducted interictally. The areas identified as participating in the expression of neuropsychological impairment/s are thus informative of the functional deficit zone.During the early 1940s, when imaging technologies were not yet available, neuropsychology played a decisive role in helping clinicians localize and/or lateralize cognitive and neurobehavioral impairments caused by brain injuries, thus contributing to clinical decisions in neurology and neurosurgery. The “blind” conditions under which neuropsychological diagnosis was taking place, as imposed by technological gaps, instead of being an obstacle to the development of neuropsychology, often was the very thing that propelled the refinement of clinical acumen and the cultivation of theorizing.The neuropsychological phenomena that arise spontaneously or are elicited in the context of either the initial clinical history taking or during test performance provide a starting point for theory-based causal attributions regarding the anatomical distribution of deficits. When interpreting neuropsychological findings to provide localizing neuropsychological diagnosis, neuropsychologists often tend to rely on a sort of basic judgment strategy, an “availability heuristic,” that is, group studies that “profile” cognitive dysfunction and/or neuroimaging studies . In contrast, psychometric data should be used to construct a clinical history in the light of a theory model to allow neuropsychologists to reach a scientifically based hypothesis. It is imperative to observe the specific conditions under which a given deficit manifests itself; to conduct qualitative analysis; to theoretically contextualize neuropsychological data; and to coevaluate the data through the “filter” of the patient's clinical-demographic, cultural, and idiosyncratic  background to establish cause and effect relationships.Memory and its neurobiological foundations could serve to gain insight into Luria's concept of brain function. More specifically, the material-specific theory of anterograde memory was developed and dominated the neuropsychology of epilepsy,Consequently, illusory assumptions of a compartmentalization of cognition, that is, verbal versus nonverbal memory and mnemonic versus nonmnemonic functions) led scholars to think for the former a clear-cut left to right temporal lobe functional distinction, while for the latter a frontal to extra frontal dichotomy. Such quadrant-base views, although on one hand being initially an aid to understand brain function, progressively limited clinical thought, often leading to oversimplified and fragmentary views of higher cortical functions. The implications for the presurgical neuropsychological assessment  were translated into a sort of rigid clinical automatisms dictating a “left to right” and “anterior to posterior” diagnosis of cognitive dysfunction, thus restricting finer and patient-tailored qualitative views. The aforementioned assumptions constitute the main reason for the pressing need of more systematic views, like syndrome analysis stemming from Lurian theory. It means that the anatomical basis of neuropsychological deficits is no longer identified within specific brain sites of the dominant or nondominant hemisphere responsible for the symptom, but rather through a qualitative neuropsychological analysis of the syndrome or symptom-complex.Syndrome analysis is a process of analytic comparison of neuropsychological evidence accessed through various tests and the determination of general signs among them, hopefully defining a unified syndrome. To Luria, a syndrome is conceived as a structure emerged by constellations of causally related, multi-level symptoms (primary and secondary); thus, understanding the different nature of the latter is of crucial importance in determining the source of a neurological breakdown and also in advancing the localization hypotheses.Historically, neuropsychological methods of investigation have been linked to three different scientific traditions  that have influenced clinical neuropsychological praxis. While each of them places the emphasis on different constructs, they are hardly independent from each other.The North American approach makes use of selected tests because of their assumed relation to some element of a scheme of psychological abilities. It has been originally developed to assist psychological assessment of individual differences, a topic that received lots of interest by American psychologists. A strong point of such an approach, adopting test batteries, is a comprehensive coverage of functions, allowing the use of scores emerging from different test results. Instead, a weak point links to the extreme length of assessment procedures, their impracticality, as well as the fact of being derived from models of normal mental function, and thus probably not suitable for clinical settings.On the contrary, the Russian approach to neuropsychology has mainly adopted a single case study methodology stemming from behavioral neurology and consistent with the Lurian theory of brain functions organized in terms of functional systems distributed to particular brain regions. It is based on models of abnormal function, and aiming at the assessment of the functional systems (searching the exact factor/s breakdown leading to the particular disorder) and therefore more suitable for clinical settings. Moreover, tests selection is case sensitive contrasting with the massive use of neuropsychological batteries often administered in an acritical fashion. Tests were rather informal, and overall unstandardized, making little if no use of standardized procedures or normative data , while successful diagnosis heavily relied on the level of expertise of the clinician.British neuropsychology stands between these two approaches, investigating individual cases with selected standardized measures. The main advantage of this approach is its special focus on individual patients and the parallel use of statistical analysis, thus being able to profile the disability of individual patients. However, the investigative process may be fragmentary and unsystematic, often placing huge reliance on tests procedures that happen to be available and, probably, therefore insensitive and/or inadequate.It is important to note that all approaches rely to some extent on the clinical expertise and acumen of the neuropsychologist. During the past two decades, the British approach was the dominant international style, mostly because of the growing influence of cognitive neuropsychology.Diagnostic approaches in neuropsychology are likely to vary in accordance to their theory framework of reference. On our opinion there is a general state of confusion regarding the very meaning and implications of neuropsychological theories and the methodological foundations of neuropsychology. Many people believe that by making use of normative data, validated methods of investigation or evidence-based knowledge may suffice or even remedy for the lack of theory. To this inconvenience may contribute the current state of affairs in clinical neuropsychology being dominated by statistically-based, data-guided or even atheoretical views. Instead, Luria proposed a conceptually-driven clinical praxis in neuropsychology. He was inclined to strongly reject an approach in which “auxiliary aids become the central method and in which their role as servant to clinical thought is reversed so that clinical reasoning follows instrumental data as a slave follows its master”The Lurian approach has been widely criticized since the lack of a direct evaluation of the tests and of controlled scoring, with the latter mainly based upon the level of expertise of the clinician rather than on normative data. Standardized measures have proven to be more valid and reliable in cases of focal, well-defined brain insults, but with limited capacity when assessing patients suffering ill-defined brain impairments (as in many epilepsy patients) and severe or diffuse neurobehavioral disorders.The dichotomy between quantitative and qualitative neuropsychology is an illusory one, since the qualitative approach is nothing more than an approach based on productive, positive symptomatology, which is susceptible to quantification. A necessary compromise of psychometric tradition with the qualitative approach is expected by modern neuropsychological procedures offering both symptom elicitation and quantification.the scientific method applied at the level of the individual hypothesis formation, literature review, study design and data collection, analysis, and conclusion…”. Thus, it is a mistake confounding EBCNP with the adoption of a particular neuropsychological theory, whatever it is. Evidence-based clinical neuropsychological practice refers to a method rather than a theory framework. When it comes to epilepsy, evidence-based neuropsychological knowledge (largely based on group studies) indicates, for instance, a lack or even absence of differentiation in terms of cognitive performance between focal epilepsy syndromes (such as FLE versus TLE), despite some significant differences found in individual subtests,According to Chelune,In recent times, the functional systems theory of cerebral organization has been in part refined and further elaborated from neolurians who continued this school of thought in neuropsychology,cognitscomprise cognitive functions being intended as information exchange within and between cognits. The crucial element here is that different cognits  have an identifiable cortical distribution, but cognitive functions do not, since the later are mediated both by shared and/or similar circuits.Goldberg's gradiental theory constitutes a rejection of the modular view proposing a distributed-emergent principle, accounting for cortical functional organization.There are also studies replicating early theoretical notions as those put forward by Elkhonon Goldberg (one of Luria's students), who hypothesized differences in hemispheric specialization arising as a function of the practical acquisition and use of descriptive systems (such as including language and other symbolic systems).Aversi-FerreiraCurrent neuroimaging research points to the failure of strict modularity assumptions in more complex integrative tasks, frequently employing analyses of functional connectivity presenting huge analogies and actually formalizing Lurian concepts.syndrome analysis. Luria's idiographic neuropsychological approach, through thorough and insightful observations and theory-driven methods, may be of considerable aid in the context of the preoperative work-up of ES candidates, particularly when lateralization and localization of seizures is required. We encourage people working in the area of neuropsychology of epilepsy to become familiar with theories of brain function and implement this knowledge to support their clinical decisions.The present paper, through the lenses of the Lurian approach, points to the necessity for the neuropsychology of ES to bypass the quadrant-based views of brain function, in favor of systemic ones like theIn conclusion, a neuropsychological approach to epilepsy, consistent with the Lurian view of higher cortical functions organized into functional systems, may deepen the understanding of neurocognitive impairments in patients with epilepsy and surpass the limits imposed by quadrant-based approaches to the brain.Manifold evidence such as hemispheric specialization, neocortical functional organization, functional reorganization and neuroplasticity, visual perception processing styles, bilingualism and neuroplasticity, focused activities and long-lasting neuroplastic changes research and cognitive phenotypes, and temporal lobes neuropsychological diagnostics, legalize current applications of Lurian theory penetrating the whole neuropathology spectrum and accounting for the whole panorama of neurocognition.


In recent decades, meat consumption has increased globally due to increased incomes. A consequence of increased red meat consumption has been the rise in greenhouse gas emissions and nutrition-related diseases. Consumption of antibiotic-free (ABF) poultry meat is a viable healthy and sustainable substitute that will cause less damage to humans and the environment in the long run. This study was undertaken due to the increasing importance of healthy food consumption to preventing nutrition-related diseases. The health food industry is still in its preliminary stages; for market development of organic broiler meat and movement toward sustainable production of ABF meat, the first necessary step is conducting empirical research on ABF poultry meat consumption and identifying factors that influence household consumption patterns of ABF poultry meat. Therefore, the objectives of this study were the investigation of factors affecting poultry meat consumption by consumers and ABF poultry meat preference. Comparing the results could reveal what percentage of consumers are able to buy healthier higher-priced antibiotic-free poultry meat. Data were collected from 360 completed questionnaires completed by households from the city of Mashhad, Iran via simple random sampling in 2021. To investigate the first objective, an ordered logit model was used. The results showed that age, the head of household’s education, awareness of the nutritional benefits of poultry meat, advertising, and family income were statistically significant determinants of poultry meat consumption. To investigate the second objective, since some consumers cannot buy ABF poultry meat due to the higher prices, we used a two-step Heckman model. The results showed that the awareness of the nutritional benefits of ABF poultry meat, the head of household’s education, monthly family income, and advertising had positive impacts, with prices having a negative impact on the amount of antibiotic-free poultry meat consumed by the households. Comparing the results of the two models revealed that only about 30% of consumers could buy ABF poultry meat, mainly due to the higher prices. This study recommends improving consumer awareness, targeted distribution of ABF poultry meat according to customers’ economic and demographic characteristics, affordable prices, and appropriate marketing tools for sustainable consumption of ABF poultry meat.  Nutritionists and informed consumers are concerned about food security, with this concern including the potential risks from consuming animal-based food products . Food seMany consumers are seeking alternatives to conventional meat products that are produced without routine use of antibiotics; this change is driven by consumer demand for meats without antibiotics. In general, poultry meat contains lower amounts of environmental pollutants . Rising According to the United States Organic Trade Association, sales of antibiotic-free and organic foods have increased by 20% per year since 1990; however, there are numerous challenges faced by companies attempting to produce ABF poultry meat, such as production, management, health and animal welfare challenges [ABF poultry meat production is becoming increasingly popular due to consumers’ emerging perceptions of it as superior to conventional broiler meat. In the poultry sector, ABF farms are increasing in number and addressing consumer concerns and new EU regulations . This trAnimal feeding regimes and nutrition are important in the livestock industry; however, higher than standard doses of antibiotics and artificial growth stimulants in feed can contribute to a build-up of residue in meat, animal products, and the environment (water and soil pollution) .The Centers for Disease Control and Prevention (CDC) and the FAO have declared antimicrobial resistance as a serious threat to world health ,13. AntiThere is a growing movement towards production and consumption of ABF products and consumer demand is rapidly growing for meat raised without routine use of antibiotics . AccordiThe main ABF groups include standard ABF programs that allow use of chemical antibacterial, chemical anticoccidials, and ionophores, but no antibiotics. Organic production allows no antibiotics, ionophores, coccidiostats, chemical anticoccidials, or chemical antibacterial products. Therefore, antibiotic-free poultry is somewhat different from organic poultry because only the use of antibiotics is prohibited in ABF poultry, whereas antibiotics are prohibited in organic poultry and no other chemical additives are allowed in their water or poultry feed throughout their growth period. Thus, organic is ABF, but not all ABF is organic. Moreover, organic broiler meat has variable costs that are 70% higher and fixed costs that are 86% higher than conventional production. This higher production cost for organic occurs due to feed, labor, certification, and outdoor maintenance costs . HoweverNowadays, it seems that the consumption of organic and healthy products is increasing in major cities, albeit mostly by high-income people . There iThe aim of this research was to investigate the factors affecting poultry meat consumption and then investigate the factors affecting consumption of ABF poultry meat. Since the factors affecting the consumption of poultry meat may be different from the factors affecting the consumption of ABF poultry meat, in this study two different models were used to investigate these two issues. Previous studies have investigated factors affecting consumption of organic or healthy products in the agricultural sector. For example, previous studies identified socio-economic factors, such as education and age , gender According to FAO (2022), global poultry meat production in 2022 is more than 138.8 million tones; however, a decline in poultry meat production in Iran is anticipated due to diminishing profit margins for poultry meat producers amid high feed prices and farm-gate price caps imposed by the government to control food price inflation . The proIn the next section, we present the details of the methodology and methods of estimation; in later sections, the results, discussions, and policy implications are presented. This study aimed to first identify and evaluate factors affecting the consumption level of poultry meat among households in Mashhad, Iran, using ordered logit model. In the second stage, we investigated the factors affecting the consumption of ABF poultry meat using a two-step Heckman Tobit regression model.An ordered logit model (OLM) was employed to analyze the factors affecting the level of poultry meat consumption among the households. OLM is based on a latent variable and, in this study, it constituted the amount of poultry meat consumed by households. This latent variable is defined as follows:plicable . If yi in is sample size and In these relationships, In Equation (3), F is a cumulative distribution function (CDF) for Model (3) was estimated via using maximum likelihood (ML) method; marginal effects of each variable were calculated at the means of independent variables, with the summation of marginal effects for each variable being zero. The marginal change in the probability can be computed as follows:The real amount of poultry meat consumed by a household is a continuous variable. Most consumers did not know the exact amount of their per capita monthly consumption; thus, poultry meat consumption was categorized according to three levels. The three categories for consumption of poultry were established according to monthly minimum and maximum per capita consumption in the sample and the standard deviation of data. Households with per capita consumption between 0.5 to 1.33 Kg per month were classified as low-level consumers, while households with per capita consumption between 1.33 to 3.16 Kg per month were classified as medium-level consumers; households with per capita consumption of 3.16 to 5 Kg per month were considered high-level consumers. In addition, after specifying the model, variables of age and education level of the household head, monthly family income, price paid for poultry meat, price paid for red meat, advertising, awareness of the nutritional benefits of poultry, and the number of children under 10 years old in the family were selected as variables to explain the ordinal regression.The Tobit model was used with the Heckman’s two-step model to investigate factors influencing consumption of antibiotic-free poultry meat. The Tobit model was applied because logit and probit models cannot distinguish between factors that influence decisions to consume ABF poultry meat, as well as factors that influence level of consumption of ABF poultry meat. The structure of the Tobit model is expressed as follows:rvations . For obsThe Tobit model utilized observations of potential and actual consumers to resolve Type I error (non-random sampling). Nevertheless, it did include the risk of Type II error, the lack of differentiation between the factors affecting decisions to consume, and factors affecting consumption level. Heckman suggested a two-step method for solving the second problem. Heckman’s two-step method assumes that one set of variables affects the decision to engage in a specific activity, while another set of variables affects the volume of participation in the activity .Accordingly, the first step is estimating a model that shows the probability of consuming antibiotic-free poultry meat and, for this part, the probit regression model was used as below: In the second step, the relationship between the independent variables and antibiotic-free poultry meat consumption is estimated using observations of The second estimation shows how the explanatory variables affect consumption levels for antibiotic-free poultry meat. The IMR coefficient measures errors resulting from sampling and, if they is significantly different than zero, it indicates bias in the sampling . The preData for this study were collected from questionnaires completed by 360 household heads in the city of Mashhad, Iran, during 2021. The population of Mashhad was approximately 3.2 million people in 2021; the sample size was determined in accordance with Cochran’s formula for a representative sample . The resInformation about research variables and descriptive statistics of data are presented in The survey results indicated that 55% of the households were low-level poultry consumers, while 18% were medium-level and 28% were high-level consumers. Only 33% of the sample purchased ABF poultry in 2021. According to the information obtained through the questionnaire, the reasons for non-consumption of antibiotic-free poultry meat were higher prices (18%), lack of information and knowledge about the product (43%), shortage of adequate centers that supply the product (36%), and the low weight of antibiotic-free poultry meat in comparison to traditional poultry meat (3%). Households that consumed antibiotic-free poultry meat paid more attention to price (25%), packaging (10%), brand (38%), weight (11%), and source of supply (16%). Results presented in The coefficients in The results in The results of the probit model for determining factors that effectively influence consumption of antibiotic-free poultry meat are reported in According to results of the estimated probit model in In addition, results presented in Moreover, the price of the product had a negative and significant effect on consumption of antibiotic-free poultry meat, such that if the price increased by one unit, then purchases decreased by 0.19%. In , it was The last variable in Many consumers across the world are concerned about food safety. This concern includes the potential risks of consuming animal-based food products. Food safety and security has received much attention and has been one of the main concerns of the Food and Agriculture Organization (FAO). Food security includes physical, social, and economic access to an adequate and safe supply of nutritious food to maintain an active and healthy lifestyle. The most common health problems worldwide are caused by diseases related to food contamination, which impose limitations on economic productivity, especially in developing countries. Due to the increase in per capita income and population growth, the average consumption of meat has increased globally. The consumption of various types of meat has a significant impact on the health of people and the environment.Many consumers are looking for alternatives to conventional meat products that are produced without the routine use of antibiotics; these changes are drivers of consumer demand for antibiotic-free meats. There is a growing movement towards the production and consumption of ABF products, and consumer demand for meat produced without the routine use of antibiotics is growing rapidly. Poultry meat production is more sustainable in the long run compared to red meat production in meeting consumer protein needs. In addition, the production of ABF poultry meat is a response to environmental considerations. Increased consumer interest in safer food has led to a growth in ABF meat production; several producers have shifted their marketing towards this direction. While consumer demand for ABF broiler meat is increasing, there are challenges in adopting appropriate strategies for ABF meat production. This study contributes to the literature by examining the effects of marketing factors, such as advertising, along with the effects of consumer-related factors, such as consumer awareness, in the framework of a Tobit model to investigate factors affecting the consumption of ABF poultry meat. The Tobit model is applied because other related models cannot distinguish between the factors that influence decisions to consume ABF poultry meat and the factors that influence level of consumption of ABF poultry meat. In this study, we have addressed this research gap in ABF poultry meat. For this purpose, two groups of factors—marketing and consumer characteristics—were used and their effect on poultry meat consumption and consumption of ABF poultry meat was investigated.The survey results of the questionnaires showed that 33% of households consumed antibiotic-free poultry meat. The lack of information about the product and shortage of adequate centers supplying the product were the main reasons for the low consumption levels. Moreover, brand, price, and supply centers were important factors for those households that consumed antibiotic-free poultry meat. The results of the ordered logit model indicated that the age and education level of the household head, awareness of the nutritional benefits of poultry meat, and advertising all had a positive effect on the probability that a household had a higher per capita consumption level of poultry meat. Monthly household income and the price of poultry had an adverse effect on probability of poultry consumption, though only family income was statistically significant. Increasing family income probably allows households to substitute for higher-priced ABF poultry meat.The results of the probit model, which was to determine factors influencing consumption of antibiotic-free poultry meat, showed that the level of education household head, family income, quality of antibiotic-free poultry meat, awareness of the benefits of antibiotic-free meat, and advertisement had significant effects on consumption of ABF poultry meat. Education, income, quality, awareness of benefits, and advertisement had direct and positive effects on consumption; however, age and price had indirect negative but insignificant effects on consumption of ABF poultry meat. Highly educated men were the main consumers of pollution-free foods. Improved living standards and a rising middle class increase demand for green food. Moreover, low demand for organic products and sales problems were the most significant impediments to producing ABF chicken. There was a positive relationship between the attitude to production of ABF chicken and level of education. Furthermore, the estimated coefficients of the linear regression model showed that advertisement and awareness of nutritional benefits of ABF poultry meat had a positive and significant effect; however, the price of the product had a significant negative effect on consumption of ABF poultry meat; there was also a large price differential between conventional and green food products. Hence, targeted market segmentation and wider distribution of antibiotic-free poultry meat among the wealthy and more educated consumers in the society can serve boost production and enhance consumption of ABF products. Due to the limitations of this study, the random sampling method was used to collect data. Considering the different economic and social characteristics of different urban areas and different cities, it is better to use other sampling methods, such as random classification sampling, in order to more closely examine the effects of differences in urban areas on the consumption pattern of poultry meat.According to the results of this study, it can be concluded that a significant percentage of households do not consume antibiotic-free poultry meat because of unfamiliarity with the product and lack of information on its nutritional benefits. With increased consumption and demand for the product, its production will also increase. Improving the awareness of antibiotic-free poultry meat and advertisement had a positive and significant effect on the level of consumption. Hence, policymakers focusing on increasing the production of antibiotic-free and healthy products need to consider the factors that affect consumption of these products, including the use of appropriate marketing tools. It is recommended that by using appropriate promotional tools, government and NGOs that promote production and consumption of antibiotic-free products create programs that increase consumer awareness of the health and environmental benefits of antibiotic-free products, as well as the detrimental effects of products containing antibiotics. Furthermore, the supply center shortages of antibiotic-free products had a significant effect on the low-level consumption of antibiotic-free poultry meat. Therefore, targeted distribution of and sales centers for antibiotic-free products could increase sales and consumption. In addition, prices played an important role in determining the consumption level of antibiotic-free poultry meat. Efforts to supply antibiotic-free products with reduced production costs could lead to lower prices, while other policies, such as brand development, could have a positive effect on the consumption of antibiotic-free products. Having more brands entering the market and brand competition could improve not only the quality of antibiotic-free meat products, but also decrease prices, which may in turn contribute to an increase in consumption levels.


The properties and behaviour of adult or somatic neural stem cells (NSCs) are governed by a complex network of intrinsic molecules and pathways along with a milieu of extrinsic signals hailed from the extremely controlled environment or niche in which they reside. These NSCs are able to self-sustain while dividing to produce highly proliferative intermediate progenitors that, in turn, give rise to neuroblasts and neurons. Plasticity in the adult brain is mainly fostered by the generation of new neurons and their functional integration, a process that remains far from being well understood. This phenomenon has been shown to occur in vertebrates: fish, amphibians, reptiles, birds and mammals, with conflicting results regarding humans, leading to a yet unresolved debate. However, a consensus exists over the notion that the extent and functional relevance of adult neurogenesis is different throughout vertebrate classes .canonical subependymal  and hippocampal niches has also been reported but it remains deeply controversial  gene is a novel post-transcriptional mechanism by which hippocampal NSC maintain quiescence, revealing another layer to the already complex network of signals involved in NSC regulation. Dormancy is a strategy by which cancer cells bypass cytotoxic therapies and drive tumour recurrence, underscoring the role of quiescence in oncology. Moreover, NSC from the subependymal niche have been hypothesised to be the cells of origin of glioma  making use of elegant strategies and gain and loss of function approaches. In vitro analyses pointed out the role of LRIG1 in cell proliferation and responsiveness to BMP signalling. Furthermore, transplantation of LRIG1-deficient GSCs in mice results in more aggressive tumours and decreased survival as compared with control GSCs, whereas LRIG1 overexpression elicited opposite effects, indicating that LRIG1 expression in GSCs enforces quiescence and represses tumour growth, paving the way to develop LRIG1-based anti-cancer therapies.Single-cell analysis combined with retrospective isolation has allowed for the first time the identification of NSC in three proliferative states that coexist in the neurogenic niche. Quiescence is a strictly regulated property that allows cells to reversibly exit proliferation. This ability protects NSC pools from eventual exhaustion and DNA damage accumulation through adulthood and is currently regarded as one of the hallmarks of stem cell ageing . To undef glioma . In thisMazzitelli-Fuentes et al. describe for the first time that in adult teleosteans learning induces neurogenesis, adding insight to the relationship between these two processes across species, and showing that learning-induced neurogenesis is relevant in zebrafish specifically in two pallial regions: the caudal lateral pallium and the rostral medial pallium, involving NSCs proliferation and activity-dependent survival. In addition to those already mentioned, in the last decades, increasing evidence has pointed to social behaviour as an additional factor affecting adult neurogenesis. However, if and how do both phenomena influence each other is far from being well understood. In their review, Garcia-Gomez et al. nicely summarise the available evidence about bi-directional relationships between social behaviours and neurogenesis. The authors discuss how neurogenic changes induced either genetically or environmentally influence behaviour, and how social behaviours such as social living conditions, social enrichment, and social stress affect neurogenesis.Exercise, diet, environmental enrichment and activities affecting cognition and learning exert a strong influence on neurogenesis . MazziteThe regulation of the phenomenon of adult neurogenesis is complex and multifactorial, and affects all stages of the process, from NSC maintenance and self-perpetuation, differentiation into committed progeny, to the migration, survival and functional integration of new-born neurons. This comes as no surprise since being able to orchestrate the correct balance between quiescence and activation to ensure lifelong maintenance of an NSC pool, while producing the proper rate of differentiated progeny to meet the demands of an ever-adapting system as the brain, poses a formidable task. Surely, the list of NSC regulators, both intrinsic and contextual, will continue to expand in the following years as will the resolution and versatility of the techniques to study them.