The Centre for the AIDS Programme of Research in South Africa (CAPRISA), performs world-leading research on the epidemiology, pathogenesis, prevention and treatment of HIV/AIDS, tuberculosis, and - more recently - COVID-19. A rigorous yet supportive academic culture has nurtured the careers of many successful health sciences researchers, some of whom have worked for the organization since its inception over 20 years ago. This focus on professional development is founded on a training programme that invests heavily in the individual with the payoff of strengthening the science base for HIV and tuberculosis research in South Africa. Those selected for mentorship are typically medical students from the University of KwaZulu-Natal, adjoining the headquarters of CAPRISA in Durban. Increasingly, however, the institute attracts international fellows from partnering organizations to experience the intellectually demanding, scientifically robust, cutting-edge research environment. The purpose of this voices piece is to narrate and critically evaluate the experience from the dual perspectives of host and visitor of a research training programme undertaken by three undergraduate health sciences students from Vietnam, enrolled at VinUniversity. This was the inaugural running of what is expected to be an annual summer trip to CAPRISA by Hanoi-based medical and nursing students. The formative educational experience in best practice tackling of infectious diseases in challenging clinical contexts demonstrated the importance of investing in research placement programmes for public health impact. The exchange has inspired each student to become a future leader in seeking bold, innovative, and strategic approaches to improve global health issues in their home country. This critical reflection describes the experiential learning of Vietnamese undergraduates conducting a two-week intensive research training visit to South Africa. The one nursing and two medical students came from the College of Health Sciences, VinUniversity, in Hanoi. Although operating for just two years this start-up university has already attained an overall 4-star Quacquarelli Symonds (QS) ranking, with 5-star teaching quality, the fastest tertiary education institution in the Asia-Pacific region to reach this educator provider milestone . This haIt is the last of these facets that is highlighted by the nascent partnership between VinUniversity and CAPRISA, the Centre for the AIDS Programme of Research in South Africa. In January 2022, Professors Salim and Quarraisha Abdool Karim, the Director and Associate Scientific Director, were jointly awarded the inaugural VinFuture Special Prize for Innovators from Developing Countries in recognition of CAPRISA's ground-breaking research on prevention and treatment of infection with human immunodeficiency virus (HIV), the cause of acquired immunodeficiency syndrome AIDS .Each selected student was a strong academic performer with a proven research interest in public health, community medicine, epidemiology and/or infectious diseases. They also demonstrated curiosity, collaboration, organization, conscientiousness, resilience and adaptability, all valued attributes of medical researchers in a clinical setting. The rigorous selection process involved the candidate´s submission of a written application including resume and a letter of interest in which they outlined their qualifications, experience and motivation. From over 30 applications, six students were shortlisted for a 30-minute interview. The four-member panel comprised the Dean of the College of Health Sciences, the Director of each of the Medicine and Nursing programs, plus the corresponding author as relevant discipline-specific faculty. The six selection criteria were: academic performance; research experience (preferably of direct relevance); subject-specific interest and knowledge; commitment and motivation; English proficiency ; social skills, collegiality and ambassadorship. Each was scored by all panel members on a scale of 1-5 with regard to expectations: i) not met; ii) partially met; iii) met; iv) partially exceeded; v) exceeded . Interviewed candidates were also asked to confirm their: availability for the planned travel dates (with scope for possible change of itinerary); possession of a valid passport with a minimum of six months to expiry, with visa to be arranged; possession of an authorized COVID-19 booster vaccination certificate.The primary aim of the trip was for the VinUniversity students to gain insights into CAPRISA's world class research in prevention, control and treatment of HIV and tuberculosis (TB), the most common opportunistic infection in patients, in urban and rural settings. South Africa remains at the epicentre of the HIV pandemic, with the world's highest number of new infections but also the largest treatment programme . TubercuAs research conducted by CAPRISA is not only locally responsive but also globally relevant, this initiated lively discussion on how to translate the outcomes of studies in KZN to Vietnam where cultural norms and social constructs are very different. There is a marked distinction between the sociodemographic groups of greatest vulnerability in the two settings. In South Africa, prevalence of HIV is highest in the heterosexual community, notably adolescent and young adult females in transactional relationships ; in VietRegardless of nation, for transmission of HIV undetectable equals untransmissible, U = U  . While aWith the advent of highly effective HIV biomedical treatment and prevention, persons living with HIV enjoy longer and much healthier lives than experienced by their counterparts in previous generations whose condition, without effective antiviral therapy, might have progressed to full-blown AIDS. Moreover, persons at risk for HIV have more options than they did for staying HIV negative. Nevertheless, those affected by HIV continue to face stigma and discrimination, hindering access to care. This appears to be more of an issue in Vietnam, where social and sexual mores are traditionally conservative, especially in the north of the country. Against this backdrop, healthcare workers play crucial roles in striving to create an equitable, supportive and friendly environment for key populations, particularly among the more sexually experimental and less easily affronted younger generations, thereby enabling Vietnam to meet ambitious HIV epidemic control targets by 2030 .From the post-trip testimonies of the three students this proved a transformative educational experience for them to witness best practice in HIV/AIDS awareness, treatment and prevention in KZN. It is from their pioneering cohort that VinUniversity envisages that tomorrow´s leaders of the healthcare professions will emerge to drive critically needed improvements to the public healthcare system in Vietnam, a nation with a median age of 32 years and whose population will pass 100 million in 2023. The CAPRISA ethos of excellence, be that in academic studies, clinical practice or research endeavours, is now instilled in each student. This philosophy compliments the aspirational graduate attributes of all VinUnians, the qualities of Excel: empathy; exceptional ability; creativity; entrepreneurial mindset; and leadership spirit.Peer-assisted learning is a broadly practised learner-led educational model in health sciences curricula. This aims to limit the negative power dynamics that may be even subtly experienced by undergraduates and foundation-year trainee doctors and nurses when interacting with a senior teacher, typically a professor or consultant (often unbeknown to that lecturer/supervisor/tutor/ mentor) . One parBroadened perspectives and experiences through international, interinstitutional, multi-disciplinary research placement programmes have the potential to develop leaders that generate innovative solutions to public health concerns. This research experience visit to CAPRISA was the first external outreach and engagement activity for the inaugural intake of VinUniversity health sciences students. Its critically acclaimed success from both stakeholders has secured the collaboration between the two organizations. Discussions are well advanced to make the annual exchange bidirectional, enabling CAPRISA-mentored UKZN students a reciprocal opportunity to visit Hanoi. This would involve public health research and community engagement activities at VinUniversity College of Health Sciences and its partner healthcare providers, principally the Joint Commission International-accredited Vinmec Times City International Hospital. Selected students would also experience the impactful work of the United States Centers for Disease Control and Prevention, which provide technical expertise to the Vietnam Ministry of Health to support programmes that target HIV and TB as priority infectious diseases.


Individuals with diabetes mellitus are at increasing risk for major lower-extremity amputations (LEAs). Poor quality of life and remarkable disabilities are associated with LEAs, determining a high economic burden for the healthcare systems. Reducing LEAs is therefore a primary marker of quality of care of the diabetic foot. At global level, between-countries comparisons of LEAs rates are basically hampered by differences in criteria used for data collection and analysis among studies. Significant variability in amputation rates exists between geographic areas, and also within specific regions of a country. Overall 5-year mortality rate after major amputations is reported to vary substantially across countries, from 50 to 80%. The odds of LEAs are substantially higher for Black, Native American and Hispanic ethnicities compared with White groups, with similar figures observed in the economically disadvantaged areas compared to more developed ones. Such discrepancies may reflect differences in diabetes prevalence as well as in financial resources, health-care system organization and management strategies of patients with diabetic foot ulcers. Looking at the experience of countries with lower rates of hospitalization and LEAs worldwide, a number of initiatives should be introduced to overcome these barriers. These include education and prevention programs for the early detection of diabetic foot at primary care levels, and the multidisciplinary team approach with established expertise in the treatment of the more advanced stage of disease. Such a coordinated system of support for both patients and physicians is highly required to reduce inequalities in the odd of diabetes-related amputations worldwide. Atherosclerotic vascular diseases and diabetic foot are the conditions that contribute most to the global burden of diabetes mellitus, as measured by disability-adjusted life-years in people aged 50 years and older , despiteAlarmingly, the number of total amputations has been constantly increasing in the US since the 2007 economic recession, providing an example of how broad economic and racial disparities can lead to disability, reduced quality of life and, eventually, increased mortality . These iA number of sociodemographic factors, including ethnicity, financial income, education level and insurance status are responsible for the substantial inequality in diabetes severity observed worldwide, in terms of likelihood of diabetic complications and LEAs , 11. In A number of determining factors can affect the odds of diabetes-related LEAs and figuring out the reasons why they vary so markedly across different geographic areas and ethnic groups worldwide is challenging. Moreover, between-countries comparisons of diabetes-related LEAs rates are somehow hampered by differences in criteria used for data collection and analysis. In particular, there is paucity of data regarding clinical outcomes determined by diabetes-related LEAs from some parts of the globe, especially from Latin-America, Africa and Oceania, and this represents a major barrier toward the need for increasing awareness of the substantial social, medical, and economic burdens attributable to the diabetic foot syndrome –16. Acco3 people at risk  study , obtaineFurther evidence confirming substantial between-countries inequalities in diabetes-related LEAs rates - even among industrialized high-income areas - comes from data collected on 21 countries from different geographic areas by the Organization for Economic Cooperation and Development (OECD) . By estihttps://diabetesatlas.org/). Given to financial disparities between developed and emerging countries in the access to insulin, these patients are the ones who suffer the most from the severe chronic complications that diabetes entails (Globally, the number of people with diabetes who require insulin, as a measure of complexity of the disease, is estimated at 30-40 million worldwide ( entails . LEAs ar entails  and, cru entails , 33. The entails . It had In this scenario, there is much evidence confirming that a comprehensive evaluation and early intervention can help identify individuals at high risk of diabetic foot and reduce the possibility of hospitalization and LEAs , 36. HowIn conclusion, incidence of diabetes-related LEAs can be considered as a measure of quality of care for patients with diabetes, and relevant inequalities across different geographic areas and ethnicity groups unfortunately still exist. Actions should be undertaken to overcome these barriers and to guarantee the delivery of optimal care for the many individuals with diabetic foot disease. It is essential to remark that clinical outcomes of diabetic foot ulcers are not determined just by patient and ulcer characteristics themselves, but also by local healthcare organization, availability of resources, management strategies used and attitudes of the care providers Figure 1AB: Conceptualization, Methodology, Writing—original draft. MM: Conceptualization, Methodology, Writing—original draft. AA: Methodology, Writing—original draft. LU: Writing—review and editing. DL: Supervision. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The brain endothelial cell (BEC) glycocalyx (ecGCx) is a BEC surface coating consisting of a complex interwoven polysaccharide (sweet husk) mesh-like network of membrane-bound proteoglycans, glycoproteins, and glycosaminoglycans (GAGs) covering the apical luminal layer of the brain endothelial cells. The ecGCx may be considered as the first barrier of a tripartite blood–brain barrier (BBB) consisting of (1) ecGCx; (2) BECs; and (3) an extravascular compartment of pericytes, the extracellular matrix, and perivascular astrocytes. Perturbations of this barrier allow for increased permeability in the postcapillary venule that will be permissive to both fluids, solutes, and proinflammatory peripherally derived leukocytes into the perivascular spaces (PVS) which result in enlargement as well as increased neuroinflammation. The ecGCx is known to have multiple functions, which include its physical and charge barrier, mechanical transduction, regulation of vascular permeability, modulation of inflammatory response, and anticoagulation functions. This review discusses each of the listed functions in detail and utilizes multiple transmission electron micrographs and illustrations to allow for a better understanding of the ecGCx structural and functional roles as it relates to enlarged perivascular spaces (EPVS). This is the fifth review of a quintet series that discuss the importance of EPVS from the perspective of the cells of brain barriers. Attenuation and/or loss of the ecGCx results in brain barrier disruption with increased permeability to proinflammatory leukocytes, fluids, and solutes, which accumulate in the postcapillary venule perivascular spaces. This accumulation results in obstruction and results in EPVS with impaired waste removal of the recently recognized glymphatic system. Importantly, EPVS are increasingly being regarded as a marker of cerebrovascular and neurodegenerative pathology. The brain endothelial cell (BEC) glycocalyx (ecGCx) is a BEC surface apical coating consisting of a complex polysaccharide (sweet husk) gel, a mesh-like network of membrane-bound proteoglycans, glycoproteins, and glycosaminoglycans (GAGs) covering the apical luminal surface of the BECs. Importantly, the ecGCx may be considered as a gatekeeper of BECs since it is important in maintaining physiological and homeostasis functions, which include the prevention of vascular permeability, inflammation, blood coagulation, and promoting the synthesis of the vasculoprotective nitric oxide (NO) via its mechano-sensing and transduction capabilities ,2,3. The ecGCx was first identified via transmission electron microscopy (TEM) by Luft utilizing ruthenium red staining to visualize the ecGCx in 1966 . RecentlEven at the ultra nano-structure level the net negatively charged ecGCx stains are so intensively electron-dense, due to the ecGCx attracting the highly positively-charged lanthanum La(3+) nitrate (LAN) of the electron-dense staining lanthanum nitrite, that the proteins cannot be seen. Therefore, the following illustration is utilized in order to better understand and illustrate the proteoglycan, glycoproteins, and GAGs including hyaluronan (HA). Also, the GAGs covalently bonded to proteoglycans and glycoproteins’ side chains with their large quantity of negatively charged sulfate residues are included  6,7,11,,13,14.Previously, the brain endothelial cells have been examined as central players in the development of EPVS and as a result, this also places the ecGCx at a critical point in the development of EPVS . The ecGEPVS are thought to have numerous causes from multiple mechanisms including: 1) Increased fluid and neurotoxic proteins that enter the PVS due to BBB dysfunction/disruption as a result of increased permeability. 2) Increased fluid inflow into the PVS due to increased BBB permeability as a result of BBB disruption and concurrent astrocyte endfeet (ACef) dysfunction, detachment, separation, and aquaporin-4 dysfunction with decreased water uptake allowing for the accumulation of water in the PVS. 3) The stalling or obstruction of impaired glymphatic efflux of the PVS conduit due to inflammation as a result of the accumulation of excess leukocytes with phagocytosis and the accumulation of excessive phagocytic debris and oxidative stress, in addition to the activation of increased matrix metalloproteinases (MMPs) that result in stagnation, stalling, and/or varying degrees of PVS conduit glymphatic system obstruction of the waste removal mechanisms. (4) Microvascular arteriole or venule vascular stiffening and/or aberrant spiraling of arterioles that are associated with decreased vascular pulsatility, which result in decreased fluid flow within the PVS, which contributes to PVS enlargement  in addition to vascular dementia (VaD). Additionally, SVD and VAD are known to be associated with lacunar stroke and white matter hyperintensities (WMH) ,25,26,29The ecGCx is known to maintain normal neuronal homeostasis. It acts specifically as a physical and charge barrier, a mechanical transducer, a regulator of vascular permeability, a modulator of inflammatory response, and in anticoagulation ,16. An iob/ob genetic model at 20 weeks of age . Th. Th65]. PVS are critical conduits for interstitial fluid and metabolic waste into the CSF , and EPVThe ecGCx is a key and critical regulator of permeability, cerebral blood flow, capillary perfusion, and cell adhesion, all of which are necessary for maintenance of CNS homeostasis. Therefore, we should do all we can to maintain the integrity of the ecGCx to decrease the development of EPVS as a result of NVU BBB disruption with increased permeability due to the attenuation and/or loss of the ecGCx. For this reason, the possible mechanisms of how ecGCx perturbations with attenuation and/or loss might result in EPVS are shared . The theory that the glia limitans  serve as a second barrier for the passage of proinflammatory leukocytes is substantiated by Horng et al. . We now live in one of the oldest global populations ever recorded, that will continue to increase over at least the next two decades until the so-called global ‘baby boomer generation’ has passed ,20. AddiWhen discussing health with older individuals over the past 30 plus years of the author’s clinical practice in family medicine, three of the most feared diseases are the development of stroke, dementia, and cancer because they so often result in an inability for older people to care of themselves. However, cognitive impairment and dementia are primarily the result of a combination of vascular brain injury and neurodegeneration. Also, vascular injury manifests as a highly prevalent pathologic clinical disease of SVD over a long period time instead of acute onset transient ischemic episodes and strokes . ImportaIntriguingly, enlarged perivascular spaces, the glymphatic system, and the ecGCx are in their early stages of an exponential growth in the scientific understanding of how they relate to various injurious processes resulting in neuroinflammation. Neuroinflammation and the multiple associated neurologic diseases that affect the brain through neurovascular and neurodegenerative mechanisms have many future questions that need to be asked and answered. It is hoped that this review will provide the stimulus for future research to ask these questions in order to provide the necessary answers.


As one of the important components of Earth observation technology, land use and land cover (LULC) image classification plays an essential role. It uses remote sensing techniques to classify specific categories of ground cover as a means of analyzing and understanding the natural attributes of the Earth’s surface and the state of land use. It provides important information for applications in environmental protection, urban planning, and land resource management. However, remote sensing images are usually high-dimensional data and have limited available labeled samples, so performing the LULC classification task faces great challenges. In recent years, due to the emergence of deep learning technology, remote sensing data processing methods based on deep learning have achieved remarkable results, bringing new possibilities for the research and development of LULC classification. In this paper, we present a systematic review of deep-learning-based LULC classification, mainly covering the following five aspects: (1) introduction of the main components of five typical deep learning networks, how they work, and their unique benefits; (2) summary of two baseline datasets for LULC classification  and performance metrics for evaluating different models ; (3) review of deep learning strategies in LULC classification studies, including convolutional neural networks (CNNs), autoencoders (AEs), generative adversarial networks (GANs), and recurrent neural networks (RNNs); (4) challenges faced by LULC classification and processing schemes under limited training samples; (5) outlooks on the future development of deep-learning-based LULC classification. Land use and land cover (LULC) is the expression of the transformation of the natural ecosystem of land into an artificial ecosystem and the naturally occurring or human-induced cover on the surface. It serves an important role in the fields of disaster management, urban planning, environmental protection, and agricultural production, and has been a popular theme in Earth observation research. Remote sensing images have become the main data source for LULC classification due to their advantages, such as wide coverage and continuous monitoring to obtain time series data. Since the introduction of deep learning, excellent results have been achieved in the field of image processing, and thus, LULC classification has become a popular research topic ,2,3.The initial classification technique was visual interpretation classification, which was judged by the expertise of the interpreter. The advantage is that the accuracy is high, generally higher than the computer classification accuracy, so the visual interpretation classification is still applied to high-precision, high-resolution remote sensing image classification . HoweverIn contrast to classic machine learning algorithms, deep learning (DL) demonstrates unique advantages in image classification. While traditional machine learning algorithms require the manual design of features for classification tasks, deep learning eliminates the need for manual intervention; it automatically learns and extracts features relevant to the target task, and this automatic feature extraction capability endows deep learning models with strong robustness and makes it easier to migrate the models across different datasets ,12,13,14The rest of this paper is organized as follows: Deep learning , a uniquConvolutional neural network (CNN) is a neural network with convolutional operational feedback and deep structure in the network. Simply put, it is a system that simulates the vision of the human brain. It is a multilayer neural network structure consisting of three parts: input layer, intermediate layer, and output layer, where the intermediate layer contains some hidden layers ,18. AfteRecurrent neural network (RNN) ,24,25, aGenerative adversarial network (GAN) is one of the most promising unsupervised learnings in recent years. It contains two kinds of neural networks, generator network and discriminator network, and the two neural networks learn against each other. During the training process, the generator constantly adjusts its parameters to allow the generated samples to fool the discriminator . The disHidden and Woutput denote the weight of input to hidden and hidden to output, respectively. Bhidden denotes hidden layer bias and Boutput denotes output layer bias. AF is the activation function. SAE is widely used in LULC classification due to the fact that it can help to extract the low-dimensional features in remote sensing images and capture the key information. Autoencoder (AE) is an unsupervised deep learning model consisting of an encoder and a decoder. The functionality is similar to PCA; however, in contrast to PCA, AE not only handles linear mapping but also performs nonlinear mappings for better performance. The input data will be mapped to a lower dimension than the original data for the coded representation while the encoder reconstructs the data as much as possible so that the coded representation maps back to the original dimension. Thus, the ultimate goal of AE is to reconstruct the output similar to the original data, i.e., to minimize the reconstruction error of the decoder as much as possible . When thFully convolutional neural networks (FCNs) belong to a special variant of CNNs and are mainly used for image semantic segmentation. Unlike CNN, it does not contain fully connected layers, but replaces fully connected layers with fully convolutional layers. Some FCNs also contain some local perceptual layers, e.g., transposed convolutional layers, upsampling layers, etc. Unlike traditional CNN-based semantic segmentation methods that take image chunks as input, FCN samples a more direct approach, instead taking the entire image as input and manually labeling the graph as output. This end-to-end training approach greatly improves the computational efficiency of the network as well as more accurate segmentation results, making FCN stand out in semantic segmentation tasks. FCN can be a favorable tool for LULC classification because it not only provides pixel-level prediction, it also captures the spatial information in remote sensing images well in order to achieve multi-scale feature learning and other advantages, so as to achieve efficient and accurate classification ,32. FiguIn deep learning, the accuracy of classification relies heavily on high-quality datasets as well as proper class categorization. Especially with the introduction of CNNs, it becomes crucial to have a large amount of training data to support the training of the network. With the continuous development of deep learning, various deep neural network models are applied to LULC classification, which makes comparing the gaps between models a research hotspot, so it is important to construct an open-source sample dataset. In recent years, researchers and organizations have released a variety of LULC classification datasets. These datasets cover a rich variety of data samples, including multi-scale images, data collected by different sensors, and multimodal features, providing a more reliable basis for data comparison in related studies. These datasets are broadly categorized into two types: patch-level datasets and pixel-level datasets. Patch-level datasets refer to the assignment of a fixed-size image to a specific feature class, and are mostly used for remote sensing image scene classification. Pixel-level samples, on the other hand, where each pixel point is considered a sample and assigned to the respective category, are mostly used for semantic segmentation of remote sensing images.The Indian Pines ,34 was cPavia University  was publLoveDA  is an adLandCoverNet  was creaPixel-level datasets are used for LULC classification whose ultimate goal is the segmented map, i.e., land cover mapping. Such pixel-level datasets are mostly used as training semantic segmentation models, which is a huge amount of work involved in segmenting an image into several constituent regions with specific labeling of all pixels covered by the segmented regions. Therefore, producing such datasets can take a lot of time and labor on the part of the researcher. Four pixel-level benchmark datasets for LULC classification are described below.In recent years, LULC mapping has achieved remarkable results in various fields, such as ecological change detection and land use trend prediction, which has increased the interest of scholars in various countries in making sample datasets, resulting in an increasing number of pixel-level benchmark datasets.Several commonly used pixel-level benchmark datasets are shown in The UC Merced  dataset The AID  dataset The NWPU-RESISC45  is a datThe EuroSAT  dataset Instead of labeling and categorizing each pixel, patch-level datasets are produced in image segments, i.e., entire blocks of images are labeled into a scene category. These image segments have a fixed size and location and thus are similar to remote sensing scene recognition datasets. Compared to the process of making pixel-level datasets, the production of patch-level datasets is much simpler, and it does not require a lot of labor to label the images. Therefore, the disadvantage becomes very obvious: such labeling is coarse and does not capture the details inside the image blocks. Four patch-level benchmark datasets for LULC classification are described below.These datasets mentioned above are large in scale, covering many regions and scenes around the world, with rich data diversity. At the same time, the images in these datasets have high spatial resolution and can be capable of high-precision feature classification tasks. Despite the large size of these datasets, there is still the problem of the relatively limited amount of data compared to the complexity and diversity of reality. On the other hand, some datasets, e.g., EuroSAT, have too few feature categories, making it difficult for the network to capture subtle differences between categories and reducing the generalization ability of the model.In general, the pixel-level samples lack a large and high-quality dataset like the patch-level. Moreover, the areas covered are relatively homogeneous and only suitable for specific regions, and the generalizability needs to be improved. The patch-level dataset has fewer spectral bands, which limits the model’s ability to extract spectral information from features, thus limiting the scope of the application. Therefore, some hyperspectral and multispectral data need to be included. Hyperspectral/multispectral images can provide a more accurate estimation of mixing ratios, which leads to more accurate mixing analysis results. On the other hand, hyperspectral/multispectral imagery also allows for better differentiation of spectral characteristics between different features. The reflectance of different features at different wavelengths has unique characteristics, and hyperspectral imagery can capture these subtle differences, thus providing a better ability to identify and classify substances. This is important for applications such as LULC classification, vegetation type identification, and water monitoring.The main performance metrics commonly used to evaluate LULC classification models are overall accuracy (OA), average accuracy (AA), F1-score (F1), and mean intersection and parallel ratio (MIOU) .Overall accuracy (OA) is the most used performance metric in LULC classification to measure the rate at which the model correctly predicts the samples in the dataset, which is represented by Equation (2).Average accuracy (AA) indicates the average of the rate of correct predictions by the model in each category sample, as represented by Equation (3).F1-score (F1) is the average value after calculating the precision rate and recall rate reconciliation; the larger the F1, the better the model performance, represented by Equation (4).Mean intersection and unity ratio (MIOU) represents the average of the prediction accuracies of all the categories in the dataset and is commonly used to evaluate semantic segmentation, As expressed by Equation (5).CNNs can effectively capture textural, spatial, edge, and contextual features of feature classes in remote sensing images and can be adapted to remote sensing images at different scales, which makes them very suitable for processing LULC classification tasks. However, the training of CNN relies on a large number of labeled samples, which is prone to overfitting and difficulty in dealing with some small-sized features in the case of scarce labeled samples. Temenos et al.  proposedFor complex scenarios, Khan et al.  proposedGAN is widely used for land cover classification in the case of sparse samples due to its unique adversarial mechanism that generates realistic images to act as samples and to increase the training samples of the network, thus improving the accuracy of classification. GAN can generate images for categories with fewer sample data, solving the problems of low classification accuracy and overfitting in some categories caused by unbalanced sample data. In addition, the images generated by GAN are cleaner, reducing the negative impact of noise in the original data. Ansith et al.  changed Although convolutional neural networks are most common in LULC, RNNs are better in special cases, especially when dealing with time-series data, where they can accurately capture the sequence information. The RNN is hidden in each time step due to its special hidden state, and this hidden state is propagative and can be passed from the previous time step to the next, whereby the RNN can capture rich contextual information. In addition, RNN can adaptively extract features when training data, eliminating the complicated process of designing a feature extractor. Ma et al.  incorporAutoencoders have excellent downscaling and are, therefore, often used to process hyperspectral data in LULC. AE has a unique encoding–decoding mechanism to mine the potential features in the original data, as well as to reconstruct the original data, which helps the model to learn the data at a deeper level to generate discriminative features with higher discriminative properties, thus realizing more accurate classification. As an unsupervised classification method, AE is also often used in the case of limited labeled samples, which can learn feature characteristics of features on unlabeled samples and then use the unsupervised pre-training results for supervised classification, effectively alleviating the problem of sample scarcity. Ibañez et al.  proposedData diversity: Remote sensing data may change according to different times, different regions, different sensors, different climates, etc., so our model needs to be constantly updated to adapt to the variability caused by data diversity.Category imbalance: When making LULC datasets, some categories will have too many or too few data samples due to factors such as differences in geographical distribution, human intervention, and labeling difficulties. Therefore, the model tends to favor the categories with more data when training these data, while the categories with fewer samples lead to poorer classification accuracy due to too few training samples.Sample labeling problem: The labeling of LULC training samples not only requires researchers to have a high level of expertise and be familiar with the characteristics of the vegetation, urban, and water categories, it also requires a lot of manpower and time to classify the category areas and classifications. Some features are highly similar to each other, e.g., swamps and wetlands, and researchers may make subjective errors in judgment that lead to relatively fuzzy boundary delineation between categories, thus reducing the classification accuracy of the model.Generalization ability of the model: A model may achieve high classification accuracy in a specific region; however, if it is changed to a region or area in other countries, the generalization ability of the model will be reduced due to different distribution of features, differences in the labeling of the samples, and differences in the characteristics of the categories, among other factors.Contextual modeling: Remote sensing data are spatially correlated, i.e., there are dependencies between data in nearby areas. However, traditional deep learning methods do not mine the contextual information between data well, making the model unable to learn and understand the data comprehensively, which reduces the classification accuracy of the model.The problem of limited training samples is one of the main challenges faced in LULC classification, and in this section, we will discuss the solutions to this challenge in terms of four methods: transfer learning, data augmentation, weakly supervised learning, and active learning.Transfer learning (TL) is the application of knowledge learned in the old domain (source domain) to the new domain (target domain), which compensates for the negative impact of sample scarcity and improves the generalization ability of the model, leading to more accurate classification. Therefore, TL is often used to deal with LULC under limited samples. Domain adaptation (DA), as a subfield in TL, is even more favored by researchers because it can solve the discrepancy problem between the source and target domains. Liu et al.  proposedData augmentation is one of the important means of solving the finite sample problem, which involves various transformations such as rotating, scaling, and cropping of the original data as a way of generating new training samples and increasing the training volume of the model. In addition, data augmentation makes the training samples more diverse and improves the generalization ability of the model. Scott et al.  trained As a kind of iterative learning, active learning (AL) adaptively selects higher quality samples from unlabeled samples to train the model and improve the performance of the model and iterates by selecting informative and low redundancy samples from them for labeling, thus reducing the need for training samples. Haut et al.  proposedTraditional deep learning methods need to rely on a large number of training samples to achieve accurate classification. However, the labeling of remote sensing images is time-consuming and labor-intensive, and the limited training samples make it difficult for the model to capture deep and complex relationships. Therefore, weakly supervised learning, which is less dependent on labeled samples, becomes one of the most effective methods to solve the sample scarcity problem. In the following sections, we will explore the use of zero-shot learning and few-shot learning methods to deal with the limited sample problem in LULC.Zero-shot learning (ZSL) allows knowledge migration, i.e., learning semantic information from visible categories and transferring it to invisible categories, thus reducing the need for labeled samples. Li et al.  proposedFew-shot learning aims to utilize a very small number of training samples to achieve effective training and fast learning. Chen  and otheCross-modal data fusion: With the rapid development of deep learning methods in the field of LULC classification, more research will be conducted in the future to fuse effective information from multiple modalities  of remote sensing imagery and combine them with deep learning models to improve the accuracy and robustness of the network model classification.High-resolution data: The resolution of remote sensing image data will increase in the future, which puts more requirements on LULC classification. Therefore, one of the focuses of future research is to develop better algorithms and models to adapt to high-resolution data to capture the details and changes in feature characteristics more accurately.Expert knowledge: Although deep learning and other technologies have achieved excellent results in LULC classification, it is still very important to integrate the experience and knowledge of human experts. It can not only be converted into an interpretable form of the model to improve the explanation ability of the classification results but also correct the misclassification of the model to achieve higher robustness and accuracy, which is in line with the needs of practical applications.Transfer learning and adaptive learning: The combination of the two can help solve the problem of low generalization ability of models due to domain differences. Future research can explore how to narrow the gap between the source and target domains due to transfer learning and optimize the model using adaptive learning to enable higher adaptation on the target domain for higher classification performance and generalization ability.Combined with the most advanced deep learning technologies and innovations in recent years, LULC classification will face the following important trends:In the future, LULC classification will continuously improve the generalization ability and robustness of the model, as well as the accuracy and explainability of the classification with the help of cross-modal data fusion, high-resolution data, transfer learning and adaptive learning, among other technical means. This will provide more accurate and reliable information support for resource management, environmental protection, and sustainable development.LULC classification plays an important role in urban and rural land planning, disaster prediction, monitoring of environmental change, and sustainable development. Early LULC classification methods based on low-level feature extraction unfolding can achieve good results on small-scale datasets, yet have limited results for large-scale as well as complex datasets. Therefore, as the variety and number of LULC sample datasets continue to expand, deep-learning-based classification methods have stepped onto the stage, bringing automation, adaptability, and accuracy to the LULC task, allowing us to better learn and manage surface resources. In this paper, we briefly introduce some commonly used deep learning models, including; CNN, RNN, AE, GAN, and FCN. After that, we systematically summarize the common public datasets commonly used for LULC classification in terms of samples and categorize them as pixel-level datasets and patch-level datasets, respectively. We also summarize the evaluation criteria used in LULC classification. In addition, we overview the excellent performance demonstrated by deep learning methods in LULC classification and briefly clarify the advantages and disadvantages of each method. Despite the fact that DL is very effective in handling LULC classification tasks, there are some limitations and challenges faced by LULC classification that prevent DL from realizing its full potential. Thus, in this paper, we summarize the challenges faced by LULC classification, elaborate on the treatment scheme in the case of scarcity of labeled samples, and finally point out the corresponding future directions to face these challenges.


Prematurity is becoming a real public health issue as more and more children are being born prematurely, alongside a higher prevalence of neurodevelopmental disorders. Early intervention programs in Neonatal Intensive Care Units (NICUs) correspond to these uni- or multi-sensorial solicitations aiming to prevent and detect complications in order to support the development of preterm infants. This article aims to distinguish sensory intervention programs according to the gradient of the type of solicitations, uni- or multi-modal, and according to the function of the person who performs these interventions. Uni-sensorial interventions are essentially based on proprioceptive, gustatory, or odorant solicitations. They allow, in particular, a reduction of apneas that support the vegetative states of the preterm infant. On the other hand, the benefits of multi-sensory interventions seem to have a longer-term impact. Most of them allow the support of the transition from passive to active feeding, an increase in weight, and the improvement of sleep-wake cycles. These solicitations are often practiced by caregivers, but the intervention of parents appears optimal since they are the main co-regulators of their preterm child’s needs. Thus, it is necessary to co-construct and train the parents in this neonatal care. Each year, it is estimated that 1 in 10 births worldwide, or 15 million children, are born prematurely . Children born prematurely have sensory and perceptual peculiarities, with atypicalities depending on their experience in utero, their age, their birth weight, and the number of days spent in the neonatology department . These aEarly interventions in Neonatal Intensive Care Units (NICUs) seek to limit deviant developmental trajectories. Since the 1980s, the introduction of early, individualized developmental care has shifted the focus from child survival to supporting the well-being of the child and the parents. In the incubator, the preterm newborn is exposed to inappropriate stimulation while in a critical period of physiological immaturity and cerebral development associated with atypical sensoriality. The technical environment of the NICU and the developmental fragility of preterm infant lead to disturbed parent–child interactions . ConsequThis work examines both the incubator environments and the type of multi-sensory solicitations most favorable to the sensoriality of preterm infant while questioning the place of caregivers and parents. Neonatal Intensive Care Units (NICUs) for very preterm infant do not provide the same living and development conditions as the intrauterine environment. preterm infant in incubators are cut off from the prenatal period; in the first moments of their aerial life, they are at odds with the normal biological continuum and immersed in an environment far removed from the intrauterine sensory world . During their first days of life, these preterm infants will be confronted with numerous stress factors, such as painful care procedures and frequent and uncomfortable manipulations . The preMuch of the physical contact  is not soothing and is associated with mostly unpleasant and unfamiliar odors such as disinfectant ,22,23,24Recognition that sensory stimulation can overwhelm preterm infants and increase physiological signs of stress has led to attempts to reduce sensory input . BecauseSome other early intervention programs, such as that of Becker et al. , aim to Other programs, on the other hand, offer soothing sensory enrichment in the NICU such as therapeutic touch, soft music, etc. . These pSeveral sensory enrichment programs have measured improved cognitive development in infants who received these sensory stimuli. Scarr-Salapateck and Williams  offered Introducing a soothing solicitation such as lullabies sung by the parent during a skin-to-skin session induces a better quality of parent–child interactions from the first session . Indeed,The Supporting and Enhancing Sensory Experiences (SENSE) program  has studIt may seem contradictory that programs aiming at either a reduction of stimuli or an increase in stimuli have favorable consequences for the preterm child . StudiesExisting interventions based on opposing theoretical underpinnings  have led to opposing recommendations , which eWhatever the theoretical basis adopted, the aim of these interventions is not to provide a preterm child with experiences that he or she has missed, but to help him or her to extract information from his or her environment by organizing active cycles of receiving adapted stimulation and rest . We must try to take into account all of the recommendations and ensure that they are applied over a longer period of time while not overloading the immature neurological system of the preterm infant . In ordeAnother way to distinguish intervention programs is to focus on the gradient of the type of stimulation, uni- or multi-modal, and/or the function of the person performing the intervention(s), the caregiver or the parent. The objectives of these programs are always to compensate for inappropriate contexts of early development by creating a developmental niche ,63,64,65Early intervention programs focusing on uni-modal sensory input have demonstrated specific benefits on neurobehavioral functions in the preterm infant ,25,50,66Programs supporting proprioceptive development and rhythmicity have used hydraulic mattresses that reduce apnea and improve sleep in preterm infants . The useMassage therapy is a program based on tactile stimulation of the preterm infant ,69. DataThe positioning of preterm infants in the incubator provides proprioceptive sensory enrichment that can have major effects on sensorimotor development. Preterm infants placed alternately in symmetrical  and asymmetrical  positions showed less asymmetry  at discharge from the NICU than infants placed exclusively in symmetrical positions .To palliate painful procedures, oral glucose administration has been recommended, resulting in a decrease in facial and vocal expressions of pain. Nevertheless, several studies have questioned the analgesic properties of sweet taste and warn about the long-term effects of cumulative glucose exposure . Indeed,Early intervention programs that focus on a single sensory modality also have their limitations. Being centered on a predominant modality, it can sometimes induce over-stimulation, and stress can then result. It seems more natural to stimulate all the senses. More generally, these uni-sensorial stimulations cannot replace social interactions, especially those of the parents.Multi-sensory solicitation programs, using a combination of visual, auditory, and tactile (and/or kinesthetic) stimuli, also aim to stimulate the neurobehavioral development of preterm infant. This sensory redundancy (or intersensory redundancy) is defined when information is perceived by at least two temporally synchronized sensory modalities  and appeIn the 1980s, the first multi-sensory interventions appeared in neonatal services in Bolivia with the Kangaroo Maternal Care method or skin-to-skin care ,33 The aMulti-sensory care was introduced in neonatal units with a pioneering intervention by caregivers to evaluate the contribution of individualized early care ,33,77. NThe systematic review by Cha et al. , which eAnother study compared four groups of preterm infant whose intervention, performed by nurses, focused solely on physical activity demands . The firIn the literature, it is sometimes difficult to know what place is offered to the parent in certain care programs : parent A specific program was developed—Maternal Participation Program (MPP)—integrating the three components of psychosocial support, education, and active participation of parents in the care provided by the nurse . This MPIn this sense, Yu et al.  clearly The proprioceptive and rhythmic contact of skin-to-skin provides an experience that minimizes visual and auditory stimuli overload. Skin-to-skin contact allows the preterm infant to benefit from the full range of “parental closeness” experiences, including the mother’s smell, touch, rhythm of movement, voice, and unique social style. This care is a particularly beneficial method because with minimal manipulation, it offers near-maximal sensory enrichment (vestibular containment) while supporting the preterm baby’s self-regulatory abilities and the organization of the biological clock with a more regular sleep-wake cycle , increasWhite-Traut, Wink, Minehart, and Holditch-Davis  comparedGabis’ intervention program  also incThe Family Nurture Intervention (FNI) program is also a multi-sensory care by parents for preterm newborns born between 26 and 34 weeks of gestational age ,23,24. FHowever, Aita’s meta-analysis  modulateMulti-sensory intervention programs emphasize the beneficial effects on the development of the preterm infant and on the establishment of early interactions and the first bonds of attachment. However, welcoming a preterm infant generates anxiety in the parents. Their state of stress can sometimes hinder the development of these interactions and communication skills with their preterm infant . ParentsThe parents’ initial involvement in the care process gave rise to contradictory emotions, such as joy but also stress and even anxiety . Most ofAlthough the parents are solicited for care in the incubator, such as toileting or diaper changes, this daily care requires mobilizations that can be disorganizing for the child, such as postural imbalances, disorganized motor skills with an increase in extensional movements, jerks and tremors, and expressions of discomfort, tonic contractions, grimaces, alert eyes, as well as vegetative reactions, such as desaturation or hiccups . The perParents of preterm infants have more difficulty recognizing and adjusting to their preterm child’s cues, with less synchronous interactions and an impoverished interactive style: less touching, vocalizing, and looking ,50. To tThere are multi-sensory interventions that focus on building early relationships between parents and preterm infants aimed at harmonious parenting ,105. ForThe involvement of parents during care is a major recommendation to be valued, and the quality of this parental involvement is one of the factors that supports the neurobehavioral development of the preterm baby ,107. PriTherefore, it seems useful to advocate for concrete recommendations both from the point of view of the NICU environment and the parent–preterm infant dyad while being aware that the major challenge in applying these recommendations is subtle: it lies in both qualitative and quantitative degrees, in the way they are applied, and in their duration. Caution should be exercised in the introduction and progressive provision of sensory stimulation in accordance with the perceptual developmental stages of the preterm infant and under continuous monitoring of the preterm infant’s reactionary state. These recommendations should be shared by everyone involved with preterm infants and their families in the NICU . People should be made aware of the needs of the preterm infant in order to take into account the temporal organization of technical gestures.Thus, recommendations for the NICU environment can be considered, such as minimizing excessive exposure to noise by speaking in a low voice, wearing soft shoes, not using the top of incubators as a table surface, closing porthole doors gently, responding quickly to alarms, and limiting the use of personal radios ,110,111.This is why a new study, with preterm infant aged between 26 and 32 weeks, financed by a Hospital Program for Nursing and Paramedical Research, having obtained the authorization n° 20-0271, has been designed in order to bring together tactile, olfactory, auditory, and vestibular (often missing) stimulations. The particularity of this multicenter comparative study lies in the fact that the parents will carry out the multi-sensorial solicitations themselves after having participated in a training and practice workshop on the sensory-motor needs of the preterm newborn. This care will be performed for 20 min, once a day, for 10 days, with a gradual presentation of the solicitations, under continuous monitoring of the baby’s reactionary state. The Coding Interaction Behavior (CIB)  and the


GABAergic cortical interneurons are important components of cortical microcircuits. Their alterations are associated with a number of neurological and psychiatric disorders, and are thought to be especially important in the pathogenesis of schizophrenia. Here, we reviewed neuroanatomical and histological studies that analyzed different populations of cortical interneurons in postmortem human tissue from patients with schizophrenia and adequately matched controls. The data strongly suggests that in schizophrenia only selective interneuron populations are affected, with alterations of somatostatin and parvalbumin neurons being the most convincing. The most prominent changes are found in the prefrontal cortex, which is consistent with the impairment of higher cognitive functions characteristic of schizophrenia. In contrast, calretinin neurons, the most numerous interneuron population in primates, seem to be largely unaffected. The selective alterations of cortical interneurons are in line with the neurodevelopmental model and the multiple-hit hypothesis of schizophrenia. Nevertheless, a large number of data on interneurons in schizophrenia is still inconclusive, with different studies yielding opposing findings. Furthermore, no studies found a clear link between interneuron alterations and clinical outcomes. Future research should focus on the causes of changes in the cortical microcircuitry in order to identify potential therapeutic targets. Schizophrenia is a psychiatric disorder that affects up to 1% of the population and is characterized by the dysregulation of cognitive, emotional, and behavioral functions . The cliSchizophrenia is diagnosed based on the Diagnostic and Statistical Manual of Mental Disorders (DSM)  or the IThe etiology and pathogenesis of schizophrenia are still unclear, though it is generally agreed that the clinical manifestation of schizophrenia necessitates a combination of environmental and genetic factors. There are several prevailing hypotheses on the origin of schizophrenia, yet none of these completely explain all the observed clinical manifestations. Nevertheless, most of them are not mutually exclusive, which leaves open the possibility of multiple hypotheses eventually explaining the mechanisms underlying the pathogenesis of schizophrenia ,7.The potential role of GABAergic cortical neurons in the pathophysiology of schizophrenia is of particular interest because schizophrenia is often associated with dysfunction of cortical microcircuits ,9. UnderHere, we give a comprehensive overview of the possible roles of GABAergic cortical neurons in schizophrenia. In particular, we focused on molecular studies done on human brain tissue and accentuated the strength and conclusiveness of the findings, which was not done in previous reviews on this topic. We also evaluated the different hypotheses on its etiology and pathogenesis in relation to the dysfunction of GABAergic neurons.in vivo studies is a relatively generalized reduction in brain volume, predominantly attributed to a reduction in gray matter of the cerebral cortex , temporal lobe, and basal nuclei.The most intriguing of these is the PFC. The PFC is generally divided into two distinct functional parts – the lateral PFC (LPFC) and the ventromedial (vmPFC) or orbitomedial PFC (omPFC). The term vmPFC is sometimes used partially or completely synonymously with the term orbitofrontal cortex (OFC). The LPFC is further divided into the dorsolateral (DLPFC) and ventrolateral (VLPFC) prefrontal cortex. The LPFC is crucial for the integration of higher cognitive functions (executive functions), such as decision making and working memory, while the vmPFC is involved in the control of emotions and motivation . The PFCBesides the mesocortical pathway, the mesolimbic pathway is also affected in schizophrenia. This pathway also originates in the VTA; however, its synaptic targets are located in the ventral striatum , which includes the nucleus accumbens and olfactory tubercle . In contThe medial portion of the temporal lobe contains the hippocampus, which is usually significantly reduced in size in schizophrenia. The hippocampus and its adjacent structures are involved in short-term memory consolidation, and their dysfunction in schizophrenia can explain poor memory retrieval and some of the positive symptoms. Another important part of the temporal lobe affected in schizophrenia is the superior temporal gyrus, which is involved in language comprehension, auditory processing, and self-monitoring. Its cortico-cortical projections form part of the temporal-frontal-parietal network involved in language production and interpretation. Cortical thinning in the superior temporal gyrus is associated with the severity of positive symptoms, particularly thought disturbances and auditory hallucinations . HoweverIn general, reduced blood flow in the PFC and striatum observed on functional MRI is particularly common in patients with prominent negative and cognitive symptoms. Such patients typically experience a prolonged prodromal period, characterized by inadequate social functioning, before the onset of positive (psychotic) symptoms .Overall, numerous studies investigated the neuroanatomical abnormalities in schizophrenia and their relation to the specific symptoms ,27-29,33The hypotheses explaining the etiology and pathogenesis of schizophrenia can be grouped into two categories – the hypotheses involving altered levels of different neurotransmitters  and the hypotheses involving reduced cortical synaptic connectivity.Among the hypotheses related to neurotransmitter dysfunction, the most prevalent is the dopamine hypothesis. This hypothesis suggests that the main cause of psychotic symptoms in schizophrenia is excessive dopamine D2 receptor activation. It is supported by the significant efficacy of D2-like-receptor antagonists in the treatment of psychotic symptoms. This hypothesis also has a strong neuroanatomical basis, since the PFC, cingulate cortex, and medial temporal cortex, which are all extensively affected in schizophrenia, receive particularly strong dopaminergic innervation (The next most prominent hypothesis is the one involving glutamate dysfunction. This hypothesis explains the etiology of both positive and negative symptoms of schizophrenia through the dysfunction of N-methyl-D-aspartate (NMDA) glutamate receptors. It is supported by ketamine (an NMDA receptor blocker) causing schizophrenia-like symptoms in pharmacological models of schizophrenia. Most models suggest a hypofunction of NMDA receptors, which could explain some of the negative and cognitive symptoms. Interestingly, the expression of the NR2D subtype of NMDA receptors in schizophrenia is increased. NR2D receptors in schizophrenia are characterized by hyperexcitability, probably as a compensatory response to reduced cortical activity. This particularly affects the stimulatory input of cortical GABAergic interneurons, thus impacting feedback inhibition in cortical circuits. Such a dysfunction of NMDA receptors is particularly prevalent in the PFC  and projections neurons , with changes in microcircuits as well as in cortico-cortical and cortico-subcortical networks ,35. CortThe neurodevelopmental model of schizophrenia proposes that the first pathological events in the brain occur long before the onset of the symptoms ,36-39. NInitially, research on neuropathology in schizophrenia mainly focused on cortical pyramidal neurons. The changes to pyramidal neurons were subtle, and included reduced arborization and synaptic connectivity. There was also a reduced number of specific membrane receptors, with GABA receptors being among the most affected. The latter alterations suggested that the pathological changes in pyramidal neurons might be related to the dysfunction of GABAergic cortical neurons . Therefore, research interest has recently shifted to cortical interneurons, which regulate pyramidal neuron activity in cortical microcircuits.in situ hybridization, and real-time polymerase chain reaction. Research revealed that even the alterations of cortical interneurons were relatively subtle and affected only certain interneuron populations. However, there is a large number of contradicting studies, and certain changes in cortical microcircuitry are less supported than others. Many of these discrepancies could be attributed to differences in methodology, the use of animal models, prolonged use of pharmacotherapy, as well as differences in the postmortem delay of the analyzed brain. Furthermore, the predominance of different types of symptoms in schizophrenia may be related to alterations in different interneuron populations.There are numerous studies on cortical interneurons and their alterations in schizophrenia, in both animals and humans. We reviewed the data from neuroanatomical and histological studies that analyzed different populations of cortical interneurons in postmortem human tissue from patients with schizophrenia and adequately matched controls. We focused on the studies using the following methodologies: immunohistochemistry, RNA GAD1  mRNA expression, a decrease in SST (somatostatin) mRNA expression, and a decrease in parvalbumin (PV) expression and/or PV+ neuron density  is predominantly located in the soma and involved in metabolic production of GABA, while GAD65 (the product of GAD2) is located in the axon terminal and is involved in GABA synthesis for neurotransmission. This might suggest that in schizophrenia the neuroprotective role of GABA is more affected than GABA transmission. This is also in line with the selective loss of certain interneuron populations, such as SOM+ and PV+ cells, which seems to occur in this disorder.Since both the Besides the described alterations to GABAergic markers, the expression of certain subtypes of GABA receptors, located predominantly on pyramidal neurons, also appears to be altered in schizophrenia  8)..8).GAD1 expression, there is additional convincing evidence for the alterations of SST mRNA expression in schizophrenia. Multiple studies demonstrate a significant decrease in SST expression in the PFC and hippocampus in schizophrenia (+) cells  in schizophrenia yielded inconclusive results, with different authors finding increased, decreased, or unchanged levels of CB protein or mRNA -73. Sevepression ,73,74.+ interneurons co-express SOM and up to 50% of SOM+ interneurons co-express CB  perineuronal nets (PNNs) in schizophrenia  (RELN mRNA (reelin) (+) neurons (Significant findings regarding other interneuron markers include reduced expression of tokinin) ,73, decr(reelin) , and a r neurons . Studies neurons ,73,94.+ neurons compared with SOM+ and PV+ neurons , SOM+ and PV+ neurons originate from the medial ganglionic eminence (MGE) and preoptic area (POA) . Therefore, both an early and a late hit possibility could be in line with the neurodevelopmental model of schizophrenia and the multiple-hit hypothesis. Moreover, multiple hits to specific interneuron populations at different life stages are not mutually exclusive.The differences in alterations between different interneuron populations could also be explained by the distinct developmental origin of CR neurons . Whereasea (POA) . If inteIn conclusion, specific populations of GABAergic cortical interneurons are selectively affected in schizophrenia. Changes in the somatostatin interneuron population are the most substantiated in the literature, followed by alteration of parvalbumin neurons. Calretinin neurons seem to be largely unaltered, at least in the cerebral cortex. The changes in selective interneuron populations are most pronounced in specific cortical regions, particularly the PFC, where they likely have highly specific effects on the cortical microcircuitry. Nevertheless, the question remains whether the described changes in schizophrenia are part of the underlying pathophysiological mechanism that contributes to the clinical manifestation of the disorder, or whether they are a consequence of other underlying mechanisms. Future research should focus on determining the exact causes of these changes in the cortical microcircuitry in order to identify potential therapeutic targets. Such research could be especially beneficial if we want to better understand the pathophysiology and treatment of negative and cognitive symptoms.


Self-cognition is unique to the human brain. The brain provides an advantage in prioritizing self-information, which has been found in ethology and brain imaging. Self-advantages are presented as fast response, high attention and strong memory. The specifics of self-cognition can be applied to brainprint recognition. Brainprints analyze the brain response of users when watching identity information sequences, which is considered to be a valuable exploration of intrinsic identity authentication.The important identity attribute of self-information presents unique cognitive processing advantages in psychological experiments and has become a research hotspot in psychology and brain science. The unique processing mode of own information has been widely verified in visual and auditory experiments, which is a unique neural processing method for own name, face, voice and other information. In the study of individual behavior, the behavioral uniqueness of self-information is reflected in the faster response of the human brain to self-information, the higher attention to self-information, and the stronger memory level of self-reference. Brain imaging studies have also presented the uniqueness of self-cognition in the brain. EEG studies have shown that self-information induces significant P300 components. fMRI and PET results show that the differences in self and non-self working patterns were located in the frontal and parietal lobes. In addition, this paper combines the self-uniqueness theory and brain-print recognition technology to explore the application of self-information in experimental design, channel combination strategy and identity feature selection of brainprints. The research on self-cognition  has alwaThe individual’s self-cognitive advantages are not only reflected in behavioral data, but also in unique in brain imaging results. EEG and functional magnetic resonance have become important technical means in self-cognition research. The temporal resolution advantage of EEG focuses on the components of neuronal discharge. The spatial resolution advantages of functional magnetic resonance and PET provide important brain regions for self-processing. Studies have shown that individuals induced specific early and late EEG components ,15 in thEEG and functional magnetic resonance imaging (fMRI) have become important technical means in self-cognition research. EEG has a temporal resolution advantage, which focuses on the components of neuronal discharge, while fMRI and PET have a spatial resolution advantage, which provides important brain localization for self-processing. Studies have shown that specific early and late EEG components have been induced in the process of self-recognition, and unique functional activation appeared in the cerebral cortex. With the continuous development of brain imaging technology, researchers have widely used observation methods, such as electroencephalogram (EEG), functional magnetic resonance imaging (fMRI), and positron emission tomography (PET) in experiments, providing research data on brain mechanisms such as nerve discharge and cortical activation for self-cognition. The self-cognition research based on EEG focuses on potential changes and oscillation components . The advThe uniqueness of the brain is the basis of identity authentication. A large number of identity authentication studies choose the unique processing of self-information as a breakthrough and awaken the special working mode of the brain by presenting the subjects with their own attribute information. This paper summarizes the research on the uniqueness of self-processing and summarizes and combs the application of self-cognition in the field of identity authentication. This paper summarizes the characteristics of self-information processing from multiple perspectives, combs the theoretical hypotheses of different self-cognition mechanisms and analyzes the brain mechanism of self-cognition advantages by combining various experimental methods. The above summary provides a research reference for further exploration of the uniqueness of self-information processing, and provides a theoretical basis and prior information for identity authentication based on ‘brainprint’, especially in paradigm design and identity feature extraction.A number of studies have shown that individuals pay more attention  to stimuThe earliest self-cognition experiment uncovered the behavior priority of own information processing; the experiment showed that subjects recognized their own facial images or names faster. The initial experimental records showed the behavior of pressing keys and eye movements, and further studies also found the priority recognition of the subjects’ own information in the neural response signal. This work summarizes the literature on self-awareness as shown in In behavioral studies, multiple experiments repeatedly verified the processing priority of subjects’ own faces and names. Self and non-self face recognition as the most intuitive stimuli has been widely studied. Tong  found thAs a highly abstract social identity, the rapid identification of names has become a research hotspot , and namResearchers have used eye tracking technology in the task of capturing self-visual information, recorded the eye movement data of subjects in task search and analyzed the temporal and spatial attribute information such as fixation time, fixation point position and eye hops. Wang  found thIndividuals are naturally sensitive to their own related information, which is mainly manifested in that their own information is more likely to awaken the brain in unconscious or distracted states, and their own related stimuli are harder to mask by shortening the presentation time and adding a noise figure. Using subjects’ own characteristics as distractors has a stronger interference with the participants. This work summarizes the literature on self-sensitivity as shown in In the self-recognition of non-attentional states, the cocktail effect  is the mSelf-information is more difficult to mask under the threshold, and the brain needs less attention resources for self-information cognition. Shapiro  tested tThe interference effect of self-information as a distractor on main tasks is more obvious. Researchers  conducteAdditional studies have found that the audio of the subjects’ own name can cause individual brain responses under a minimally conscious state, such as sleep ,56, geneThe sensitivity of the subjects’ own information is also widely used in daily life, where medical care is used to detect the basic function of the brain . AccidenThe brain’s sensitivity to information can be applied to the experimental design of identity authentication, which has important reference significance for experimental material selection and stimulus presentation time. The original identity authentication experiment was stimulated by alphabetic spelling, daily necessities watching and other independent materials. The recent experiment targeted the user’ s personal information as the experimental content and compared it with the brain reactions of other’s information for identity authentication. The accuracy of identity recognition was significantly improved after the introduction of self-related attribute materials. In addition, based on the differences in self and non-self sensitivity, supra-liminal and subliminal experiments can be set. By adjusting the presentation time of stimuli and setting the masking map, self-related stimuli can be presented above the cognitive threshold, rather than self-information below the cognitive threshold. The supraliminal and subliminal cognitive differences expand the difference between self and non-self brain responses. Subliminal experimental stimuli can stimulate an individual’s subconscious and instinctive responses, and become a more hidden and safe means of identity authentication.Self-reference processing activates the memory advantage of the brain, and combining self-information with experimental materials helps memory. Researchers found that in the memory test experiment, subjects use self-reference strategies to remember the stimulus more firmly—this memory advantage phenomenon is named self-reference effect.Many studies have summarized the memory advantages of self-reference processing, among which some have proposed that self-reference strategies can obtain better memory scores than other processing and semantic processing . ResearcResearchers have tried to explain the self-reference effect from various perspectives. Dewhurst  studied Self-reference processing and survival processing have memory advantages, and the memory effects of the two types of processing are widely compared. In five experiments, Dewhurst  comparedIndividual self-cognitive advantage is not only reflected in behavioral data, but also is unique in brain imaging results. EEG and functional magnetic resonance imaging have become important technical means in self-cognition research. The temporal resolution advantage of EEG focuses on the components of neuronal discharge, and the spatial resolution advantage of fMRI and PET provides important brain localization for self-processing. Research has shown that individuals induced specific early and late EEG components in the process of self-recognition, and demonstrated unique functional activation in the cerebral cortex.The memory advantage of individual information provides a theoretical support for the security of identity authentication. Self-cognition is the only and long-term memory result. Intruders cannot forge the brain response of real users through short-term memory. Memory advantage is also reflected in functional magnetic resonance imaging, and memory function areas in the hippocampus are activated during self-recognition. Memory advantage is applied in the content of identity authentication experiments, which includes identity information stimuli of self and stranger, such as name words and facial images.In fMRI and PET studies, the important brain regions in the self-reference task can be located. Studies have generally found that the prefrontal lobe is significantly activated in self-related tasks, and the frontal lobe participates in the neural activities of self-awareness and self-reflection. The region is involved in the distinction between self and others  or the aThe temporal lobe has been widely studied in self-identification. A wide range of cortical areas, including parietal, occipital and temporal areas, are posterior hot areas , which pWith the deepening in self-cognitive model research, Northoff  summarizIt should be noted that the current brain region positioning conclusions of self-cognition are not consistent, and there are also differences in the degree of cortical activation during self-recognition in different studies. On the one hand, it is due to the different experimental paradigm design. On the other hand, the individual’s cultural and living background leads to differences in self-concept and social identity, and ultimately reshapes the differences in brain structure and function.With the advantage of the high temporal resolution of EEG, in the process of identity authentication, time-domain segmentation is carried out according to the EEG components, and corresponding weights are given to the characteristics of different stages according to the component differences of self and non-self signals. The attention of additional differences according to the characteristics of each component helps to improve the classification. The research of self-cognition based on EEG focuses on the self-induced oscillation component, and the experimental paradigm is mainly Rapid Serial Visual Presentation (RSVP). As shown in N170 is the earliest specific component of self-induced. Caharel  studied The pre-cognitive N250 component has also attracted the attention of researchers. Keyes  believesThe brain visual system showed the specificity of P300 in self-information, and the results were consistent with the visual and auditory experiments. Folmer’s  experimeResearchers carried out studies on self-processing of patients with specific cognitive impairment. The study of Webb  comparedIn addition to studying the event-related potential components of EEG components in the time domain, Mu  studied Studies have shown that individuals pay more attention to stimuli containing self-information, and the brain has a specific response to self-information. Individual advantages can be summarized as priority, sensitivity and long-term memory. Advantage characteristics provide reference for experimental material selection and data processing in identity authentication. The specific advantages of self-information processing, such as priority, sensitivity and long-term memory not only have theoretical value, but also have broad application prospects in the emerging biological recognition applications.With the advancement of sensors and biotechnology, the identity authentication of external biometrics has become mainstream in daily life. However, in the face of more complex and severe identity security attacks, we should not only confirm the external biological characteristics of an individual, but also protect the user’s subjective will in identity authentication. Traditional biometric authentication technology still has some security risks. In fingerprint identification, criminals may synthesize false fingerprints through materials such as gelatin, thus deceiving the authentication system. Some criminals may force legitimate users to complete authentication by force. These security risks put forward higher requirements for biometric-based authentication. How to achieve in vivo detection and stress resistance has become the focus of current research in this field. Therefore, exploring the inherent biometric authentication technology has become an important research topic in the field of identity security. The subjective initiative of the brain matches the cognitive needs of the intrinsic biological characteristics, protects the subjective certification intention of the coerced person, and enriches the reset replacement function of the intrinsic certification content.Brain signal is a recognition feature of organisms and has individual specificity that can be quickly identified. Extracting the ‘EEG fingerprint’ from EEG signals as a recognition feature has become a new way to authenticate identity. As an inherent individual difference phenomenon, self-uniqueness has become an important theoretical support for the current popular brainprint recognition technology. The effective verification of self-uniqueness in the field of psychology and brain science will support the individual uniqueness theory of brainprint recognition technology, similar to fingerprints. There are considerable individual differences in brain structure and advanced cognitive function, which lays a foundation for the study of brain biological indicators .Brain imaging technology obtains the activity of human brain neurons. At present, the commonly used brainprint recognition equipment is mainly portable EEG equipment, and the EEG equipment collects weak electrical signals on the scalp surface. Compared with other bioelectrical signals, brain imaging technology has unique advantages as a biological feature, mainly reflected in the following aspects: first, EEG signals have strong anti-counterfeiting ability, which is highly dependent on the individual’s brain structure and functional neural activity, and is difficult to replicate and forge; second, compared with traditional biological features such as face, fingerprint and voiceprint, EEG signals have higher concealment, will not be exposed to the outside world, and are not easy to obtain from the far end; third EEG signals have in vivo detection; and finally, EEG signals are stress-resistant, and external stress and mental stress will cause abnormal EEG signals to lead to authentication failure. EEG has more advantages than traditional biometric patterns in anti-spoofing attacks, privacy compliance and liveness detection .The basic principle of EEG-based identity authentication is to find information that can stably represent individuals from EEG signals and build models to achieve accurate classification on this basis. As shown in The premise of biometric-based identity authentication methods is to verify the individual uniqueness of the features. By measuring the neuronal activity of the individual brain, combined with the unique self-cognitive attributes of the brain, EEG can be used as an effective means of identity authentication. The experiment presents self-attribute stimulation to induce a specific response of the brain and detects that the brain stimulates a specific response to identify the subject. Identity authentication is reflected by the uniqueness of self, and the unique processing of self-information by the human brain can be used as theoretical support.The specificity of self-cognition improves the paradigm materials and parameters of brainprint recognition. The sensitivity of the brain to information can be applied to the design of identity authentication experimental paradigms, which is of great significance for the selection of experimental materials and the presentation time of stimuli. The stimulus materials of brainprint recognition have experienced the development of general stimulation, self-stimulation and subliminal self-stimulation. The initial identity authentication experiments stimulated self-independent materials such as letter spelling and daily necessities viewing. The performance of identity authentication is limited by user capacity. When the user capacity is expanded, the performance will drop sharply. The current experiment takes the user’s personal information as the experimental content, and compares the brain response of others‘ information for identity authentication. The accuracy of identity recognition is significantly improved after introducing its own related attribute materials. The memory advantage is applied to the content of identity authentication experiments. The experimental content includes self and stranger identity information stimuli, such as name text, facial images, etc. In addition, based on the sensitivity difference between self and non-self, supraliminal and subliminal experiments can be set up. By adjusting the presentation time of stimuli and setting masking maps, self-related stimuli can be presented above the cognitive threshold, rather than self-related information below the cognitive threshold. The memory advantage of individual information provides theoretical support for the security of identity authentication. Self-cognition is the only result of long-term memory. It is difficult for intruders to forge the brain response of real users through short-term memory. The memory advantage is also reflected in functional magnetic resonance imaging. The memory function area located in the hippocampus is activated during self-recognition. The researchers considered whether visual stimuli could be presented to the participants in a subliminal manner. Since the stimulus is presented under the human perception threshold, the subjects will not be able to consciously change their processing of visual stimuli, thereby improving the security of the system.Self-cognition uniqueness inspires individual identity feature extraction methods. In the feature extraction of identity time domains, with the advantage of high temporal resolution of EEG, attention should be give to the self-specific components P300, N250 and N170. In the process of identity authentication, the time domain is segmented according to the EEG components, and the corresponding weights are given to the characteristics of different stages according to the component differences of self and non-self signals. The identity features of each component fusion contribute to the improvement in classification performance. In the feature extraction of identity frequency domain, studies have found that self-uniqueness is more obvious in low frequency, and the low-frequency energy difference of 3–5 hz can be used as an auxiliary identity feature. The conclusion of important brain regions in self-specific research is helpful for spatial feature extraction, and the lead weight can be configured according to the important brain regions in self-identification. At the same time, the attention mechanism in the application neural network can be combined to automatically learn the spatial characteristics of identity, and the spatial identity information can be integrated by data-driven methods. Researchers have explored the application of nonlinear features and entropy features, such as sample entropy, information entropy, and Lyapunov that quantify the randomness of brain waves. The entropy features enrich the feature content, and the individual difference features contained in them are even better than the conventional methods. With the advantages of EEG dense electrode arrays, the network topology of brain activity is more applied. The brain network features include important nodes and edge connection weights. Common brain network construction methods include correlation, coherence, Granger causality, directed ADTF, etc. Brain networks are used to measure the interaction and connectivity between electrodes, and the salient features of brain connectivity have been applied to biometrics as extensions and supplements . With thThis paper summarizes the uniqueness of self-information in behavioral, EEG components and fMRI imaging, comprehensively analyzes the multimodal brain imaging joint acquisition method, and discusses the neural mechanism of self-cognition from multiple perspectives. The uniqueness of self-information processing is manifested in faster responses, higher attention and stronger memory. The frontal lobe and parietal lobe are the most intense areas of specific responses. The amplitude and latency of P300 in EEG components are the most significant features. The above brain regions and signal components provide reference for data segmentation, lead selection and advanced feature construction. In addition, the wide application of self-information materials in the field of brainprint recognition reflects the important value of self-uniqueness in the field of biosafety detection.


Accordingly, the characteristics of the chosen biomaterials have a significant impact on the final mechanical performance at different size scales, ranging from cellular up to tissue/organ level.Soft tissue repair and regeneration is a field of bioengineering focused on the development of bioactive substitutes capable of restoring the physiological function of damaged and diseased tissues. Biomaterials play a crucial role in this field, serving as either bio-inert materials that integrate well into living tissues with minimal biological response, or as bioactive materials that promote new tissue growth while slowly degrading in the body. In most tissue repair and regeneration applications, several mechanical aspects come into play, such as Hydrogels exhibit great potential as biomaterials for soft tissue replacement and regeneration due to their unique physicochemical and mechanical properties. They possess the ability to absorb large amounts of water, show low elastic modulus and viscoelastic behavior. Because of their structural similarity with the extracellular matrix (ECM), engineered hydrogels can resemble native ECM, providing an ideal environment for cell survival. Moreover, they can also be used as a bioink, which is seeded with living cells for the 3D bioprinting of artificial tissues and organs, or as injectable substances able to adapt to the shape of tissue defects.Zhu et al.), as they can conform to irregular cartilage surfaces, and offer excellent biocompatibility and tunable physicochemical properties. Both natural and synthetic polymers can be adopted for this purpose, and different cross-linking methods allow for the customization of hydrogel structure, resulting in high water content, good cell interaction and improved mechanical properties. The ability to withstand high compressive loads and to reduce friction are fundamental requirements for cartilage tissue substitutes. However, hydrogels currently exhibit compressive stiffness one to two orders of magnitude lower than that of articular cartilage. Additionally, the rapid degradation of the hydrogel matrix prior to the replacement by autologous tissue can affect mechanical stability and therapeutic outcomes. Therefore, while a low friction coefficient is generally ensured, achieving higher compressive stiffness and adequate stability for cartilage repair remains a challenge. A promising approach to overcome these limitations is the combination of advanced hydrogels  to develop composites with superior properties, including self-healing capabilities.Injectable hydrogels find frequent use in cartilage defect repair (Meng et al. discuss its potential applications in the field of soft tissue engineering, along with the functional behavior and preparation methods of different hydrogel structures. The mechanical properties of albumin-based hydrogels are tunable, with tensile strength up to hundreds kPa and compressive strength ranging from about 0.1 to 10 MPa. Their self-healing properties can be quantitatively evaluated by comparing the tensile stress-strain behavior of original and healed samples, revealing an increase of tensile strength with the healing time. These smart features make them suitable for the development of both injectable hydrogels, if softer, and scaffolds, if stiffer.Self-healing hydrogels have the unique ability to autonomously repair themselves upon damage, making them valuable as tissue adhesives and wound-healing patches. Albumin-based hydrogels are an example of self-healing hydrogels, offering versatility, ease of gelation, pH and temperature responsiveness. While albumin has primarily been researched for its use as a drug delivery carrier, Kozlowski et al. demonstrate the efficacy of two novel protein-based hydrogels as 3D medium for supporting the survival and growth of primary endocrine cells and endocrine progenitor cells. In this context, the control of the mechanical properties of the 3D culture system is fundamental. The chemical and mechanical properties of protein-based hydrogels can be tuned to better control the microenvironment surrounding the cells, providing new insights on mechanotransduction phenomena in cell differentiation and maturation.Protein-based hydrogels can also be used as 3D culture systems, in order to mimic a complex extracellular environment with a highly porous topography at the nanoscale. In this regard, Yousefi-Mashouf et al. compare the mechanical behavior of neat gelatin hydrogels and gelatin covalently cross-linked with glutaraldehyde at different concentrations, in several loading conditions, i.e., tension, compression, shear, under finite strains, cyclic loading and different strain rates. This extended test protocol allows to identify the effect of the cross-linker concentration on the mechanical strength and stiffness of gelatin, and can be considered as a basis for the selection of specific cross-linking conditions to obtain desired mechanical properties in view of target tissue repair applications.Another biopolymer used to develop hydrogels with customizable mechanical properties is gelatin, being one of the main ECM components in soft tissues. In order to optimize the preparation method of different formulations, Kainz et al. adopt this approach to characterize the poro-viscoelastic behavior of a polyvinyl alcohol-based hydrogel, tailored to mimic the compressive behavior of brain tissue. In general, hydrogels are modeled as composed of an elastic solid matrix and a fluid phase: the time-dependent mechanical behavior is controlled by the flow of the fluid phase through the polymer network under compressive loading. Moreover, viscoelastic phenomena occur in physically cross-linked hydrogels. The distinction between the viscoelastic relaxation in the polymer network and the fluid time-dependent response is challenging from an experimental point of view, as it is necessary to clarify the role of absorbed and bounded water in the hydrogel matrix and the effect of compressive loads on the amount and flow of the liquid phase.In order to better understand the mechanical and structural properties of hydrogels for soft tissue replacement, experimental mechanical testing can be associated to constitutive and computational modeling. in vivo, in the short and long-term performances.While addressing several different themes related to the mechanical behavior of hydrogels for soft tissue replacement and regeneration, this Research Topics aims to highlight the importance of investigating and understanding mechanical aspects, both with experimental and computational methods, which could improve the functionality of hydrogels