Cardiac substructure segmentation is a prerequisite for cardiac diagnosis and treatment, providing a basis for accurate calculation, modeling, and analysis of the entire cardiac structure. CT (computed tomography) imaging can be used for a noninvasive qualitative and quantitative evaluation of the cardiac anatomy and function. Cardiac substructures have diverse grayscales, fuzzy boundaries, irregular shapes, and variable locations. We designed a deep learning-based framework to improve the accuracy of the automatic segmentation of cardiac substructures. This framework integrates cardiac anatomical knowledge; it uses prior knowledge of the location, shape, and scale of cardiac substructures and separately processes the structures of different scales. Through two successive segmentation steps with a coarse-to-fine cascaded network, the more easily segmented substructures were coarsely segmented first; then, the more difficult substructures were finely segmented. The coarse segmentation result was used as prior information and combined with the original image as the input for the model. Anatomical knowledge of the large-scale substructures was embedded into the fine segmentation network to guide and train the small-scale substructures, achieving efficient and accurate segmentation of ten cardiac substructures. Sixty cardiac CT images and ten substructures manually delineated by experienced radiologists were retrospectively collected; the model was evaluated using the DSC (Dice similarity coefficient), Recall, Precision, and the Hausdorff distance. Compared with current mainstream segmentation models, our approach demonstrated significantly higher segmentation accuracy, with accurate segmentation of ten substructures of different shapes and sizes, indicating that the segmentation framework fused with prior anatomical knowledge has superior segmentation performance and can better segment small targets in multi-target segmentation tasks. According to a report from the World Health Organization (WHO), heart disease has been the leading cause of death worldwide for the past 20 years . To effeCurrently, segmentation of cardiac substructures in clinical practice relies heavily on manual labor. Radiologists manually draw contour lines of substructures, including the heart ventricles, atria, and vasculature, to obtain clinical parameters for analyzing heart function, such as ejection fraction and stroke volume. However, manual segmentation is often time-consuming and prone to subjective errors, resulting in a heavy workload for medical professionals and inconsistent segmentation results influenced by factors such as diagnostic experience. With a gradual increase in the incidence of cardiac-related diseases, manual segmentation cannot meet the great demand for medical treatment. A fast and accurate, fully automated cardiac segmentation algorithm is necessary to improve diagnostic efficiency, particularly in clinical applications of cardiovascular surgery planning and radiation therapy.The early studies on automatic cardiac substructure segmentation were based on traditional machine learning methods and mainly relied on visible image features such as image texture, image edges, and pixel intensity to achieve segmentation of the heart target ,9,10,11.In recent years, deep learning has been widely used in the field of medical image segmentation owing to its advantages in automatic feature extraction and nonlinear modeling. Its segmentation accuracy exceeds that of the traditional segmentation methods . By inteWith the emergence of the multimodality whole-heart segmentation (MM-WHS) challenge, an increasing number of methods have begun to focus on the automatic segmentation of cardiac substructures, benefiting from the availability of public datasets for whole-heart segmentation . Payer et al.  used a tIn addition, inherent problems in cardiac CT images pose significant challenges to segmentation. There are many substructures in the heart; the shape and structure are complex, with large differences in size among the substructures. The substructures are close to each other, and the grayscale images of the structures are very similar, with weak contrast between them. Additionally, some substructures have blood inflow between them. Owing to differences in imaging principles and tissue characteristics, image formation is easily affected by factors such as noise and tissue movement. Moreover, the shape and position of the heart vary from person to person; even in the same person at different times or in different medical image slices, the shape and position of the heart may differ. Addressing these challenges by incorporating complex cardiac anatomical knowledge into the model to improve the segmentation accuracy without decreasing the model efficiency is a critical issue. We observed how physicians annotated the cardiac substructures. First, physicians delineate larger or more easily segmented substructures on a normal scale. For smaller substructures or those that are difficult to segment, physicians first determine the location of the small organ and then focus on the surrounding area to achieve a more accurate delineation, solving the problem of size differences and class imbalances. Physicians introduce prior anatomical knowledge of the heart structures during delineation to prevent annotation errors due to insufficient image information or imaging quality.To address the limitations of the current deep learning segmentation models, our design, inspired by the approach of the doctors, incorporates prior anatomical knowledge of the position, shape, and scale of the cardiac substructure to separate different scales using a hierarchical segmentation approach to improve the segmentation performance of different scales, especially the sensitivity of small scales. We propose a novel framework for cardiac substructure segmentation that incorporates prior anatomical knowledge. The framework employs two consecutive segmentation steps using a cascade network from coarse to fine segmentation to separately address relatively easy and difficult-to-segment substructures. To improve the segmentation accuracy of small-scale substructures, we used the coarse segmentation results as prior information in combination with the original image to guide and train the fine segmentation network; we embedded anatomical knowledge of large-scale substructures into the fine segmentation network, which resulted in the efficient and accurate segmentation of ten cardiac substructures. The experimental results show that our proposed method has superior segmentation performance and can better segment small targets in multitarget segmentation tasks without requiring additional computation.The remainder of this paper is organized as follows. With the approval of the Institutional Review Board, we collected cardiac CT data (DICOM) from 60 patients at the Third Affiliated Hospital of Guangzhou Medical University for this clinical study. Each case was annotated with ten structural contours , includiSeveral characteristics were identified from the experimental data analysis. First, the dataset is not large; the data are 3D, with a class imbalance, particularly for the smaller substructures. Second, there are targets of various sizes, with large differences between the largest (LV) and smallest (SVC) substructures. The left and right ventricles and atria were larger; the pulmonary veins and superior and inferior vena cava were smaller. Third, the cardiac substructures have diverse grayscale values, indistinct borders, irregular shapes, and non-fixed positions. Moreover, the background occupies the largest area in the CT images, which leads to significant imbalances between the substructures and between the substructures and the background, posing substantial challenges for neural network training for segmentation tasks.The heart is located between the two lungs, near the middle of the chest, and slightly to the left. Its shape is similar to that of a peach and its size is roughly equivalent to that of a clenched right fist. A three-dimensional model and a sectional diagram of the entire cardiac anatomy are shown in From In this study, a cardiac substructure segmentation framework that integrates anatomical structure priors  was propU-Net is particularly suitable for the semantic segmentation of datasets at a small scale due to its simple and symmetric structure. It can combine information from low- and high-level layers, fuse features of multiple scales, and achieve excellent segmentation results. Thus, our model uses U-Net as the backbone and divides the ten segmentation targets of the cardiac substructures into two groups, the coarse segmentation group (CG) and the fine segmentation group (FG), according to the characteristics of the cardiac substructures. These two segmentation subtasks are cascaded; the output of the coarse segmentation module serves as an input for the fine segmentation module. This cascading strategy facilitates training of each level of the network model. Using the results from the previous level of network segmentation to constrain the areas segmented by the subsequent level of the network can effectively reduce the number of false positives outside the defined area.The coarse segmentation network LS-Net  To avoid gradient diffusion during network training, batch normalization was added to all convolution layers except the last layer. (2) A dropout layer was added before the output layer to prevent overfitting.The fine segmentation network SS-Net , with a ratio of 6:2:2 for random partitioning into training, validation, and testing sets.The cardiac CT images were stored in Hounsfield units (HU) per pixel, with different tissue organs typically corresponding to different HU ranges. To improve the local contrast of the images, specific HU windows were first used to perform gray-level truncation of the original images. Normalization was performed by setting pixel values greater than 2048 to 2048 and those less than 0 to 0, followed by division by 2048. To reduce the risk of overfitting, the CT images were augmented. To avoid alteration of clinical features such as shape, the data augmentation methods included random horizontal flip, random horizontal and vertical translations (5%), and random scale (95–105%), with the aspect ratio preserved.® Xeon® E5-2678 v3 CPU and an NVIDIA GTX-1080Ti GPU with 11 GB memory, running on the Ubuntu 18.04 operating system and programmed with Python 3.7. All the programs were implemented using the PyTorch 2.0.1 open-source framework. The network parameters in the experiment included an input image size of 512 × 512, a batch size of 12, and an initial learning rate set at 0.0001; the model optimizer was adaptive moment estimation (Adam: A Method for Stochastic Optimization) [The experimental hardware environment consisted of an Intelization) , and theThe Dice similarity coefficient (DSC)  and HausThe HD is the maximum distance between a point in the automatic segmentation group and the closest point in the corresponding manual segmentation group and is expressed in millimeters. It was used to calculate the distance between the boundary of the actual value and that of the predicted region. It can be expressed asRecall is also known as sensitivity; it refers to the ability of a segmentation model to accurately identify and include all the relevant regions or structures of interest in an image. It quantifies the proportion of true positive pixels or voxels that are correctly classified as belonging to the target region, out of all the pixels or voxels that actually belong to the target region. Its calculation formula is as follows, where the true segmentation result is Precision is expressed asA higher DSC, Recall, Precision, and a lower We divided the ten cardiac substructures into coarse and fine segmentation groups based on the grouping methods presented in To verify the impact of our method on the segmentation performance, we compared the baseline U-Net, two-stage U-Net, and our method; the experimental results are shown in To further validate the performance of the proposed method, it was compared with other deep learning methods, including 3D U-Net  and nnU-In this study, we developed a cardiac substructure segmentation framework that combines anatomical structure priors to accurately segment ten cardiac substructures including the LA, RA, LV, RV, SVC, IVC, PA, PV, AA, and DA. The framework comprises a two-step cascade network from coarse to fine segmentation, incorporating coarse segmentation results as prior information and combining them with the original image to form a multichannel input. Thus, the anatomical knowledge of large-scale structures is embedded into a fine segmentation network to guide and train small-scale structures. Compared to existing segmentation models, our framework demonstrates superior segmentation performance, particularly in multi-target segmentation tasks involving small targets.We found that the segmentation targets had significant differences in size  was ten times larger than the smallest substructure (SVC)), unbalanced sample distribution, different grayscales, fuzzy boundaries, irregular shapes, and non-fixed substructure positions. It is difficult to accurately identify small substructures using current end-to-end segmentation neural network models. To address this issue, we proposed a framework that differentiates substructures with substantial size discrepancies and groups them for processing to mitigate the imbalance problem. If we treat large and small substructures equally and rely solely on high-performance hardware platforms and a large amount of labeled data to train the models, we may encounter lower segmentation accuracy for small substructures with low contrast. It is difficult to obtain large amounts of matched labeled data due to availability and the cost of medical imaging data and manual labeling. Thus, we divided the ten substructures into two groups: a coarse segmentation group for easy-to-segment substructures and a fine segmentation group for difficult-to-segment substructures. We explored the inherently stable relationships between heart substructures through experiments and selected the best grouping strategy for optimal model performance. As shown in Hierarchical segmentation enhances performance; however, it remains insufficient for tiny substructures like the superior vena cava, inferior vena cava, and pulmonary veins. Their distribution is concentrated, and coarse segmentation results encompass these small substructures within the targeted area. This inspired us to use the large substructures surrounding small substructures as prior information in small substructure segmentation to further improve segmentation accuracy. We used the first-stage segmentation results and original images as inputs to form a multichannel input for the fine segmentation network, embedding prior knowledge of the position, shape, and scale of the large substructures into the fine segmentation network. These high-resolution features were used to refine the segmentation results. Our prior knowledge included the masks of the four larger substructures, atria, and ventricles, which contained the shape and positional information of the larger structures that served as important prior knowledge for training the smaller substructures through geometric constraints. However, our prior knowledge was obtained through deep learning without additional modules or manually designed or preconceived prior information, resulting in no significant computational burden or increased computation time. By introducing the necessary prior information and increasing the number of input channels, the performance of the model was strengthened with limited data without reducing the operating efficiency of the segmentation network. The results in We combined anatomical structure priors in our cardiac substructure segmentation framework, which is compatible with base networks such as ResNet, DenseNet, and U-Net, which are commonly used for multi-target segmentation. We chose U-Net as the backbone owing to its simple and symmetrical structure, suitability for small-sample data, and excellent segmentation performance combining multiple scales of features. To further verify the effectiveness of our approach, we compared it with other deep learning methods, including U-Net, 3D U-Net, and nnU-Net, which are currently the most widely used and best-performing deep learning networks in the field of medical image segmentation.From This study has several limitations. With the high cost of medical image data labeling, the size of the dataset was limited. The generalization ability of the model was not tested because the experiment lacked external test datasets; there may be differences in modeling accuracy owing to different CT scanning parameters. In future studies, we will continue to improve our method in order to address these issues. More data will be collected to enhance the generalization ability of the method. The segmentation network was adaptively adjusted according to specific characteristics of the cardiac substructures to further improve the overall segmentation accuracy and meet clinical application requirements.This study proposes a cardiac substructure segmentation framework that integrates prior anatomical structure knowledge to address challenges such as varying grayscales, blurred boundaries, irregular shapes, and inconsistent positions of key substructures in cardiac CT images. The proposed method employs a cascading approach to automatically segment ten substructures in cardiac CT images, achieving accurate segmentation results for targets with different scales, from coarse to fine. The presented method leverages prior knowledge of the heart substructures’ location, shape, and size to separately process differently scaled structures. It employs a hierarchical segmentation approach to enhance segmentation performance, particularly for smaller structures. This segmentation model fuses the benefits of coarse-to-fine strategies and U-Net. By employing a cascading approach to progressively segregate larger to smaller substructures in cardiac CT images, the method effectively mitigates the issue of negligibly small substructures. Moreover, based on the coarse segmentation results, prior knowledge of the positions, shapes, and sizes of larger substructures was embedded into fine segmentation networks to refine the segmentation of smaller substructures. The proposed method eliminates the need for manual intervention and accurately segments substructures of varying sizes, shapes, and positions in cardiac CT images, even in cases of low contrast and blurred boundaries. The experiments demonstrated that this method can effectively identify small targets within substructures, outperforming existing methods in terms of segmentation accuracy.


This Research Topic wishes to promote the high quality and diverse work of internationally recognized and up-and-coming researchers in the earlier stages of their career in the field of ethnopharmacology. The Research Topic provides a dedicated platform for these researchers to present and discuss their findings, with the purpose of highlighting novel outcomes and opening up routes for future directions in the broad field of ethnopharmacology.in vitro and in vivo), as well as analytical studies  investigating extracts and thereof isolated compounds for the rationalization of the traditional use of herbal drugs. The rising stars in ethnopharmacology include authors from different countries/continents including Greece, Austria, Germany, Ghana, and Benin, thereby reflecting the longstanding popularity and tradition of the ethnopharmacological use of herbal drugs in the respective countries.The four original articles and the three reviews collected offer a potpourri of highlights on the stunning diversity of topics within ethnopharmacology. The original articles present pharmacological results (Momordica charantia L. (Cucurbitaceae), the bitter gourd or bitter melon, its constituents (in particular cucurbitane-type triterpenoids) and their pharmacological effects against diabetes-related conditions, as well as potential adverse effects, are the topic of the mini review by Çiçek. The author highlighted the need to make a distinction between different diabetes-related activities, including unspecified hypoglycemic effects, compounds enhancing glucose uptake, targeting glucose absorption or production, stimulating insulin secretion, and compounds targeting insulin resistance. In addition, aspects of quality control and issues of standardization of commercial products are discussed.The ethnopharmacological use of Tsioutsiou et al. reviewed the ethnopharmacological application of medicinal plants against skin diseases in the South Balkan and East Mediterranean region. In their work, the authors extracted relevant information out of more than 120 ethnobotanical studies, including a detailed statistical analysis regarding the taxonomic distribution of used plant species over more than 100 families. Concerning the application of the plant materials against skin disorders, the authors distinguished between internal and external use and also listed specific indications reported in the investigated studies, such as burns, hemorrhoids, or furuncles. The work concludes that in comparing the obtained results with Dioscorides’ “De Materia Medica,” a close connection and persistence in the use of plants against skin disorders can be found in the whole geographic area.Vaccinium myrtillus, ingredients for local foods and medicine, have long been associated with supporting vision and with claims of helping in the management of sugar levels and cardiovascular disorders. In their paper, Vaneková et al. review in detail the species’ known rich chemistry and the scientifically investigated uses, offering a short overview that helps debunking its unsupported claims while bringing to light its updated medicinal applicability. While there are insufficient studies for the application of bilberry fruits for patients suffering from diabetes and impaired night vision, there is sufficient evidence to prove the bilberries’ anti-inflammatory activity, backed up by clinical trials, at least for the application in cases of gingivitis and bowel inflammation. Interestingly, the use for circulatory disorders (approved by EMA) is supported, but the studies are outdated and not up to current clinical trial standards.Bilberries, the fruits of Codo Toafode et al. highlights the in vitro anti-inflammatory activity of the West African plant Entada africana Guill. & Perr. (Fabaceae). Using a bioactivity-guided fractionation approach, eleven phenolic constituents were isolated from three fractions of the hydroalcoholic leaf extract. To rationalize the traditional use of the species in the Republic of Benin, an in vitro model for skin inflammation using TNF-α stimulated human keratinocytes (HaCaT) was applied. The results show the release of proinflammatory cytokines IL-8 and IL-6, revealing strong to medium activities for some of the isolated constituents.The original article by Ativui et al. Interestingly, the compound is an active component in different traditional African herbal preparations. Beside its antimetastatic and antiproliferative effects, palmatine was found to inhibit metastatic colonization of triple negative breast cancer cells in the lungs by simultaneously preserving lung morphology.In another work, the anti-cancer properties of the protoberberine alkaloid palmatine were investigated by Arampatzis et al. investigated a set of hydroxynaphthoquinone compounds including alkannin, shikonin and their derivatives known as constituents of Boraginaceae plants for anti-obesity/insulin-mimetic properties as well as for anthelmintic effects. Two model systems were used for testing, i.e., 3T3-L1 pre-adipocytes and the nematode Caenorhabditis elegans. By analyzing the obtained data, valuable structure-activity-relationships could be determined showing that structural variations significantly impact biological activities in both model systems investigated in this study.Goels et al. tackles the analytical challenges of Norway spruce balm. The exudate, produced by Picea abies, is mainly composed of diterpene resin acids (DRAs) which are knowingly tricky to separate analytically. Norway spruce balm is referenced in the Austrian Pharmacopoeia (2021 ed.) but the monograph currently lacks both rigorous quality control methods for marker compounds and the minimum content for any compound. Considering DRAs constitute the main active component of the balm, their content determination would be pivotal for identifying the quality of any Norway spruce balm product. This is why the authors developed an Ultra-High-Performance Supercritical Fluid Chromatography method that optimizes the analyses of these compounds, overcoming the inefficiency, unsustainability and lengthy times of conventional liquid and gas chromatography protocols. The analysis of twenty-two Norway spruce balm samples and products showed the efficiency and selectivity of the developed protocol. Additionally, the method is more ecological, consuming significantly less solvents than other chromatographic methodologies.The work of In conclusion, the Research Topic contains a diverse selection of studies highlighting the relevance and applicability of ethnopharmacology not only for science but also for society.


Iatrogenic neurotoxicity – Mechanisms, prevention, and treatment” was to provide an integrated view of the state-of-the-art research on the basic mechanisms underlying iatrogenic neurotoxicity, as well as by providing a comprehensive insight of the topic through original research and review articles focusing on iatrogenic neurotoxicity. Although there is general awareness of the fact that the neurotoxicity is one of the most frequent adverse effects in various therapeutic protocols, the review of literature data confirmed that other manifestations of systemic toxicities, such as hepato- and nephro-toxicity, get more attention while the varieties of manifestations of neurotoxicity are often concealed, and consequently untreated. Furthermore, some manifestations of iatrogenic neurotoxicity are still misdeclared and attributed to social and environmental outcomes of surrounding artifacts accompaning specific therapeutic protocols.The aim of the Research Topic “Herewith, we provide a brief Editors' presentation of the articles published in this Research Topic.Wan et al. chose an optimal animal model (6-OHDA lesioned striatum of LID rat model) for the estimation of the signaling pathway underlying neuroinflammation, as the principle pathophysiological background of this iatrogenic adverse effect. Indeed, the applied methodology confirmed L-dopa-induced overexpression of the pro-inflammatory mediators and overactivation of the canonical NF-κB signaling pathway, but this was attenuated by the inhibition of BET protein function using JQ1, suggesting its protective role via suppression of the canonical NF-κB signaling pathway.Following the well-known and etiologically confirmed dyskinesia accompanied by the standard employment of levodopa in neurodegenerative diseases therapy, Zhang et al. confirmed that effect of isoflurane on neonatal mice in behavioral testing followed by biochemical and morphological analyses. However, they also reported that pretreatment with disodium cromoglycate attenuated the impact of anesthesia on cognitive function by reducing mast cells degranulation and mast cell tryptase expression. The presented protocol was sufficient to attenuate neuroinflammation, activation of microglia and astrocytes, and damage to oligodendrocytes and synapses underlying cognitive impairment.The neuroinflammation has also been considered as the key cause of cognitive impairment induced by anesthesia , which is generally considered to be the key cause of cognitive impairment that may result in long-lasting cognitive decline in vulnerable categories, such as neonatal subjects. Borgstedt et al. showed no significant impact of isoflurane on cognitive and/or behavioral performance in a mouse model of early-stage Alzheimer's disease performed on adult mice (10 weeks). Thus, typical cognitive impairment  was not worsened by general anesthesia, still remaining significantly above the values observed in wild type animals. Also, it is worth noting that according to the results of this study, neurodegeneration and clinical symptoms showed no gender differences in this animal model, in accordance with how it has been described in human population.In contrast to the studies on neonatal mice, the investigation of Chen et al. for the estimation of neurotoxic effects of another general anesthesia drug. An in vitro study performed on cell culture , showed that propofol induced numerous adverse effects, including decreased neuronal viability, mitochondrial membrane alterations, increased indicators of oxidative damage, apoptosis, and activation of ferroptosis. Although there was no ethological connection, the authors experimentally confirmed that hypoxic preconditioning was sufficient to reduce the neurotoxicity of propofol by inhibiting ferroptosis.A different methodological approach was employed by Zia et al.. Although with different origins, routes of administration, and a variety of indications, nanoparticles-induced neurotoxicity usually depends on their ability to bypass the blood-brain barrier. The most frequently described pathway for nanoparticles to enter the central nervous system is via sensory neurons and receptors, resulting in oxidative damage, disturbance in immune response, and genome dysfunction. In their review, the authors also discussed the cope-up strategies to overcome nanoparticles-induced neurotoxicity that include plant-based antioxidants, essential oils, and dietary supplements.In contrast to the previously discussed original research articles, this Research Topic was concluded by a very useful overview considering the nanoparticles-induced neurotoxicity by Taken altogether, iatrogenic neurotoxicity still remains insufficiently evaluated, but clinically extremely important outcome of numerous currently used medical procedures. Therefore, any effort in the estimation of its mechanisms and potential cope-up strategies to overcome its frequency and severity may significantly reduce the problems accompaning this spectrum of adverse effects.All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.


Advancements in neurosurgical visualization have been made possible by the introduction of the operating microscope (OM) and the emergence of exoscopic technology (EX). Both OMs and EXs provide enhanced magnification and illumination, but they come with their own set of advantages and disadvantages. OMs provide high-quality magnification and illumination and have been used successfully in a variety of surgical procedures. They can be customized to fit the specific needs of the surgeon and are a well-established technology. However, they can be bulky, expensive, and cause discomfort during extended procedures. EXs provide high-definition magnification and illumination, improved depth perception and ergonomics, and can be cost-effective. They can be customized to fit the specific needs of the surgeon and can be made using locally available materials, reducing the need for expensive imports. However, they may require adjustment and have a learning curve for surgeons who are used to operating with OMs. Additionally, they may have limited availability in some healthcare settings. The choice between OMs and EXs will depend on the specific needs of the surgeon and the healthcare setting. The integration of 3D EX systems has revolutionized neurosurgery, offering improved depth perception and ergonomics. EX's cost-effectiveness addresses accessibility concerns, making it an attractive alternative, particularly for low and middle-income healthcare settings. The exoscope seems to be a safe alternative compared to an operative microscope for the most common brain and spinal procedures. The exoscope may help expand access to neurosurgical care and training worldwide. In conclusion, both technologies have their own set of advantages and disadvantages, and the choice between them will depend on the specific needs of the surgeon and the healthcare setting. Advancements in visual technology have significantly impacted the field of neurosurgery, particularly with the introduction of the operating microscope (OM) in the late 1950s. This innovation gained momentum in microsurgery by 1962 and extended to spinal surgery in 1977, offering high-quality magnification, illumination, and depth perception [In response to these challenges and the quest for enhanced surgical visualization, a novel solution emerged in the form of exoscopic technology (EX). This revolutionary system involves positioning a high-definition scope externally, projecting an enlarged view of the surgical field onto a 2D  or 3D  high-resolution monitor. Recent advancements have led to the development of 3D EX systems, which significantly enhance depth perception for surgeons [As ongoing research delves into the capabilities and outcomes of 3D EX technology across various neurosurgical procedures , it becomes increasingly crucial to critically evaluate its effectiveness and safety compared to the traditional OM. This evaluation is paramount in paving the way for the potential integration of the 3D EX as a viable alternative in the field of spinal surgery. The utilization of EXs in the realm of neurosurgery introduces a range of models, each carrying distinct advantages. Particularly notable are models like ViTOM® and ORBEYE®, which offer features such as robotic arms and advanced optics. Diverse modes, including HD 3D, HD 2D, and 3D 4K, provide neurosurgeons with adaptable visualization options catering to specific needs. Comparative assessments of video quality between EXs and traditional OMs yield varying results, with certain studies favoring EXs for superior visual quality and enhanced surgeon comfort. One area where EXs excel is in ergonomic design, contributing to reduced surgeon fatigue and optimized efficiency . The avaFurthermore, EXs contribute significantly to the educational aspect of neurosurgery by offering detailed anatomical visualization . This isFuture research in the domain of EX neurosurgery holds the potential to significantly shape the trajectory of surgical innovation. Comparative studies are essential to comprehensively assess the extended outcomes of EX procedures, shedding light on factors such as long-term patient recovery and recurrence rates. Expanding the scope of investigation beyond spinal surgeries to encompass a diverse array of neurosurgical procedures is crucial to fully understand the versatility and efficacy of EX techniques. Additionally, an emphasis on patient-centric parameters, such as the duration of the surgical procedure, intraoperative blood loss, incidence of complications, management of postoperative pain, overall quality of life, and levels of patient satisfaction, will yield valuable insights into the comprehensive ramifications of EX interventions -5. InvesIn conclusion, the evolution of EX techniques represents a significant stride in the realm of spinal surgery, offering a potential solution to the limitations posed by traditional operating microscopes. The integration of 3D EX systems enhances surgical visualization and holds promise for various neurosurgical procedures, including spinal surgeries. Comparative assessments highlight the favorable outcomes of EX in terms of ergonomics, surgical efficiency, and educational benefits. Moreover, the cost-effectiveness and adaptability of EX address accessibility challenges, particularly in low- and middle-income settings. As ongoing research advances our understanding of EX technology, the feasibility of EX spinal surgery emerges as a necessity in resource-constrained environments. This transformation not only improves surgical techniques but also paves the way for a more inclusive and accessible future in spinal surgery.


ChatGPT is a cutting-edge series of machine learning models designed by OpenAI. Built on the Transformer architecture, these models make use of deep learning techniques to comprehend and create text that resembles human-like language. They are trained on extensive collections of text data and use a multi-layered mechanism that recognizes the sequential character of language. This understanding facilitates the generation of text that is contextually pertinent and coherent. With applications that span various natural language processing tasks, including text completion, translation, and summarization, ChatGPT helps to narrow the divide between human and machine communication.. Through the utilization of the analytical capabilities of ChatGPT, medical professionals can obtain unprecedented insights into complex reproductive issues, thereby accelerating the development of individualized fertility treatments. Furthermore, the skilful abilities of ChatGPT to process and analyze vast amounts of medical data enables the extraction of pertinent information. This enhances effective communication between patient and practitioner, ultimately contributing to the advancement of reproductive healthcare.The expansion of ChatGPT applications within healthcare signals a significant transformation, especially in the field of reproductive medicine. This emerging technology embodies the intersection between artificial intelligence (AI) and medical expertise, leading to a paradigm shift that reshapes both patient interaction and the delivery of care This correspondence is intended to stimulate discussion and offer insights into the growing application of ChatGPT, a complex linguistic model built on the GPT-4 architecture, within the sphere of reproductive medicine. Specifically, our objective is to explore the potential advantages and disadvantages associated with the implementation of such an AI system, as well as to discuss the ethical dilemmas it may present.milieu, ChatGPT materializes as an ostensibly propitious instrument for reproductive specialists, attributable to its voluminous repository of scholarly dissertations and the faculty to engender contextually germane ripostes . Withinin vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI), this sophisticated language model empowers reproductive experts to more proficiently assess the success rates of various treatments, consequently honing their clinical acumen and decision-making prowess. Moreover, through the adept application of natural language processing algorithms, ChatGPT exhibits a remarkable ability to demystify abstruse scientific principles and nurture patient comprehension. In doing so, it engenders a conducive environment for securing informed consent and reinforcing patient autonomy, thus fostering a more collaborative and transparent rapport between medical practitioners and their clientele .In the contemporary realm of reproductive medicine, the implementation of sophisticated AI system of ChatGPT plays an instrumental role in augmenting clinical acumen and facilitating efficacious therapeutic interventions. By leveraging the GPT-4 architecture, ChatGPT demonstrates an unprecedented capacity to assimilate, analyze, and interpret voluminous quantities of data, thereby contributing substantially to the advancement of these interrelated disciplines. ChatGPT functions as a powerful adjunct to clinical decision-making processes. By employing advanced machine learning algorithms, this AI-driven platform facilitates the rapid and accurate diagnosis of multifarious reproductive disorders, thereby expediting the implementation of targeted, evidence-based therapies. Additionally, the ability of ChatGPT to systematically parse through the ever-expanding corpus of medical literature serves to keep healthcare practitioners apprised of emerging therapeutic modalities and novel diagnostic techniques. In infertility management, proficiency of ChatGPT in discerning subtle patterns in complex datasets enables it to aid clinicians in the identification of underlying etiological factors contributing to suboptimal reproductive outcomes. Moreover, the capacity of AI system for sophisticated statistical analyses bolsters the development and refinement of personalized treatment regimens, thereby enhancing the likelihood of successful conception and pregnancy maintenance. In obstetrics and gynecology practice, aptitude of ChatGPT for processing a plethora of patient-specific variables, such as demographic data, medical histories, and imaging studies, allows for the provision of tailored care that addresses the unique needs of individual patients. Furthermore, this cutting-edge AI platform assists in the prevention, early detection, and management of obstetric and gynecologic complications, fostering the delivery of safe, effective, and patient-centered care. Moreover, employment of ChatGPT in reproductive biology research has engendered a renaissance in the field, as its capacity to process vast quantities of data expedites the discovery of novel molecular mechanisms underpinning reproductive function and dysfunction. In so doing, ChatGPT catalyzes the development of innovative therapeutic strategies and pharmacological agents, ultimately bolstering the armamentarium of clinicians specializing in reproductive medicine, infertility management, obstetrics, and gynecology practice. Thus, the integration of ChatGPT into the domains of reproductive medicine serves to propel these disciplines into a new era of data-driven, personalized, and patient-centered care. By harnessing the power of advanced AI systems such as ChatGPT, healthcare practitioners and researchers alike stand poised to effectuate significant improvements in reproductive health outcomes, thereby promoting the well-being of countless individuals worldwide.Contrarily, the inherent limitations present within the artificial intelligence framework of ChatGPT make it susceptible to the spread of biased, incorrect, or outdated information. The unique learning approach, which involves the combination of text corpora from various sources, enhances the possibility of incorporating false materials or perpetuating mistaken beliefs . MoreovThe multifarious ethical consequences emerging from the deployment of ChatGPT in the realm of reproductive medicine necessitate scrupulous examination. The unintentional spread of incorrect information by this artificial intelligence system may potentially violate fundamental bioethical principles, including those of beneficence and non-maleficence. This could occur because the distribution of inaccurate advice may lead to less-than-optimal treatment results or inadvertently cause harm through medically induced errors . In addIn view of the aforementioned discussions, it is of utmost importance to exercise practical discretion in assimilating ChatGPT within the domain of reproductive medicine practice. Meticulous validation investigations ought to be executed to determine the precision, dependability, and pragmatic applicability of the artificial intelligence system in question (Chavez et al., 2023[In summary, the incorporation of ChatGPT in the realm of reproductive medicine unveils both alluring prospects and imposing obstacles. As we endeavor to exploit the capabilities of AI to enhance patient treatment and outcomes, it is crucial to remain alert to the prospective hazards of this incipient technology, while upholding the inviolable tenets of medical practice. Only through such vigilance can we be assured that ChatGPT will adopt the disposition of a beneficent Dr. Jekyll, rather than an iniquitous Mr. Hyde.Not applicable.The authors declare no conflicts of interest.Not applicable.


Over recent decades, different types of animal models, from arthropods to primates, have been applied to neuroscience research, making irreplaceable contributions to reveal the neural basis of various behaviors. Aves are one of the most prosperous amniotes  globally, which has evolved in parallel with mammals. Although avian and mammalian brains are constructed in very different ways, the sensory information processing, cognition, behavior, and related neural control pathways of some bird species are highly similar to those of mammals, including primates and even humans. For example, the brains of birds and mammals have conserved visual and auditory pathways  Species that communicate primarily or only through auditory signals over long distances (separating the sender and receiver). (2) Species that need to converge or diverge from other group members by modifying their vocal signals, such as dolphins, seals, bats, elephants. (3) Species that would benefit from increasing the complexity of vocal signals, such as songbirds and whales. The perspective of this article provides a new integrated framework for understanding the evolution of vocal production learning.Vocal production learning is a kind of information communication ability that only few animal species possess. It refers to the behavior of animal individuals forming new vocal signals or modifying existing vocal signals based on auditory feedback from other individuals' vocalizations and their own vocalizations. Besides humans, the most striking examples of vocal production learning are songbirds, parrots, and hummingbirds in birds, and cetaceans and bats in mammals Beecher, . In two Zhang, Zhou et al. is also about animal vocal learning, but focuses more on the comparison of similarities between songbird singing and human speech. The article begins with an overview of the current understanding of mammalian and avian vocal learners, including newly discovered potential vocal learners. Based on existing research data, the following part of the paper focuses on the rare similarities in vocal learning between humans and songbirds, which are known to have different evolutionary origins. The song learning process of young songbirds is similar to the speech learning process of human infants, including the sensory stage and the sensorimotor stage, which require the participation of auditory feedback. Additionally, humans and some songbirds, such as zebra finches, have similar vocal learning critical periods and have greater learning abilities in infancy. Another important feature of human language is its complex grammatical structure. Some species of songbirds, such as Bengalese finches, can flexibly and purposefully change the order of syllables in their songs, just like humans control the order of words in language. Although there are insurmountable differences in the structure and cell types of the cerebral cortex between songbirds and humans, the coordination and interaction between the forebrain vocal motor pathway and the brainstem innate vocal pathway are indispensable for the production of autonomous vocalization, and the vocal learning pathway and the vocal motor pathway have similar neural connections, and both receive auditory information from the auditory system. This article further compares the neural basis of human speech and songbird singing, and in detail compared the similarities of projection connections, functions, neural activities, cell types, genome and transcriptome between each human speech-related brain region and songbird song-related neural nucleus in the brainstem innate vocal control pathway, forebrain vocal motor pathway and vocal learning pathway, including more recent findings. The article concludes with outlining, prospective topics of relevance for the future research.Next, the review presented by Zhang, Sun et al. to this Research Topic is the first evidence to prove that E2 can regulate the excitability of projection neurons in the song premotor nucleus robust nucleus of the arcopallium (RA) in adult songbirds. In this study, whole-cell patch-clamp recordings of adult male zebra finch RA projection neurons were performed and combined with pharmacological detection. The findings indicated that E2 has the ability to lower the spontaneous and evoked action potential firing rate of RA projection neurons, induce hyperpolarization of the resting membrane potential, and decrease the membrane input resistance. At the same time, it was found that GPER agonist G1 can mimic the action of E2 on RA projection neurons, while GPER antagonistist G15 can block the effect of E2, indicating that E2 reduces the excitability of RA projection neurons via GPER. These results contribute to understanding the regulation of E2 on the intrinsic membrane properties of songbird RA projection neurons and its receptor mechanisms.Estrogen in the animal brain, mainly estradiol 17β-Estradiol (E2), is converted from androgens under the action of aromatase, and can affect brain function and behavior  is an evolutionarily conserved neurotransmitter present in the central nervous system of all vertebrates. It is now known that 5-HT is widely involved in the regulation of mammalian brain functions, including cognition, behavior, and emotion (Bacque-Cazenave et al., WM: Funding acquisition, Project administration, Resources, Writing – original draft, Writing – review & editing.


Recent advances in roller coasters accelerate the creation of complex tracks to provide stimulation and excitement for humans. As the main load-bearing component, tracks are prone to damage such as loose connecting bolts, paint peeling, corroded sleeper welds, corroded butt welds, reduced track wall thickness and surface cracks under complex environments and long-term alternating loads. However, inspection of the roller coaster tracks, especially the high-altitude rolling tracks, is a crucial problem that traditional manual detection methods have difficulty solving. In addition, traditional inspection is labor-intensive, time-consuming, and provides only discrete information. Here, a concept of the multifunctional detection robot with a mechanical structure, electrical control system, camera, electromagnetic ultrasonic probes and an array of eddy current probes for detecting large roller coaster tracks is reported. By optimizing the design layout, integrating multiple systems and completing machine testing, the multifunctional roller coaster track detection robot exhibits outstanding performance in track appearance, thickness and crack detection. This study provides great potential for intelligent detection in amusement equipment, railcar, train and so on. Amusement facilities including the roller coaster, Ferris wheel, pendulum ride, etc., are all manned devices, which bring excitement and stimulation to tourists. However, once an accident occurs, it can cause significant casualties and extremely adverse social impacts. According to accident events from 38 countries, roller coasters account for nearly half of accidents, and the most common event type was malfunction . As the To resolve this problem, detection robots have attracted great attention in many fields. In the welding field, multiple sensors integrated with a wall-climbing robot have been developed for magnetic particle testing, which achieves collaborative control of the highly integrated robot and efficient detection . Robots Current research on track detection mainly focused on a single system, such as detecting track faults and monitoring . AdditioSo far, there is no multi-functional robot that can integrate visual inspection and non-destructive testing functions for detecting rolling tracks. Here, a multifunctional climbing detection robot with a mechanical structure, electrical control system, wireless control and transmission module, camera, electromagnetic ultrasonic probes and array eddy current probes was designed. Eventually, visual inspection of the appearance, wall thickness inspection of the track tube and surface crack detection were carried out on a certain vertical roller coaster track; the detection robot can stably move along the rolling track and detection results were basically consistent with manual retesting results, which verified the effectiveness and accuracy of the multi-functional detection robot developed in this paper. This study also provides a reference for future multi-functional intelligent detection of the large roller coaster track. In In recent years, detection robots with various sensors  have becRoller coaster tracks are located outdoors and subjected to complex long-term alternating load conditions. Based on the characteristics and analysis of failure modes of these tracks, the multifunctional track detection robot developed in this paper mainly includes mechanical, electrical control, video detection, electromagnetic ultrasonic thickness detection and array eddy current crack detection subsystems. Therefore, some key detection items of the track can be measured, such as loose connection bolts, paint peeling, corroded sleeper welds, corroded track butt welds, reduced track wall thickness and surface cracks.The multi-functional inspection robot adopts a roller coaster wheel system structure, an additional drive system, a clamping system, an inspection system, a control system, etc., as illustrated in The multi-functional detection robot developed for roller coaster tracks is applied to a vertical roller coaster to detect the appearance, wall thickness, and surface cracks of the tracks. The vertical roller coaster track has various types such as spiral rings, vertical rings, and horizontal rings, as shown in The vehicle structure consists of seven main parts: frame, front axle assembly, rear axle assembly, front wheel set, rear wheelset, clamping mechanism and driving system, as shown in The main contribution that is not offered by equivalent state-of-the-art systems is the dual-servo motor-controlled walking system. The roller coaster track is usually two sets in parallel, where the left and right driving wheels of the vehicle walk along the corresponding side tracks. However, the traditional motor-driven control method applied to this type of rail vehicle has the following problems: whether the left and right drive wheels are driven by the same motor through a differential transmission mechanism, or are independently driven by two motors, there is a problem of two wheels interacting with each other. Therefore, the relative motion state between the left and right wheels and the track is inconsistent, ultimately leading to unstable or even abnormal vehicle walking, which affects the progress of inspecting work.1−I2, where I1 and I2 are the current feedback values of servo motor 1 and 2. When e > 0, the proportion coefficient Kp in the PID algorithm is adjusted according to the size of e. For example, the smaller the value of e, the smaller is Kp to reduce the static error of the system and weaken fluctuations; when e < 0, the static error of the system is reduced through the integration in the PID algorithm.To solve the problem of asynchronous walking, servo motors 1 and 2 are used to control the rotation of drive wheels 1 and 2, respectively. Meanwhile, the rotation of servo motor 1 is controlled and the current feedback value of servo motor 1 is obtained, then the torque of servo motor 2 is adjusted based on the current feedback value of servo motor 1. Additionally, the current feedback value of servo motor 2 needs to be obtained in real time, and the difference between the current feedback values of servo motors 1 and 2 should be calculated, and then the output torque of servo motor 2 should be controlled based on this difference. The control objective is to ensure that the current feedback values of servo motor 1 and servo motor 2 are equal. Control the output torque of servo motor 2 using PID algorithm. Set the difference e between the current feedback values of servo motor 1 and 2 to be IThe vehicle body adopts a typical full-degree-of-freedom roller coaster chassis structure with the front axle connected to the frame through joint bearings, and the rear axle fixed to the frame through a vertical axis, ensuring that the detection device has sufficient degrees of freedom. The driving system adopts a DC servo motor, which is connected to the reducer through a synchronous belt to drive the main walking wheel to move. The clamping mechanism adopts a cylinder and connecting rod mechanism, which can provide sufficient and reliable compression to the driving wheel. It ensures that the driving wheel is always effectively pressed on the track surface and maintains stable compression force under different sporty postures. In order to verify whether the mechanical structure can adapt to the track characteristics, the main test items including the structure operation interference test and the track-climbing achievement test were carried out, which can successfully achieve various operational functions.The electrical control system, including the onboard control system a and theThe macroscopic information on the track surface, such as loose connecting bolts, paint peeling, corroded sleeper welds, and corroded track butt welds, can be detected by a video visual inspection system. The robot designed in this paper mainly includes the front-end Gopro camera, local camera, pan tilt servo motor, video-processing module, etc., as shown in The GoPro camera at the front of the vehicle b collectThrough visual inspection of the entire video, it can be seen that the macroscopic state of the roller coaster track is in good condition, with no obvious damage or failure, as shown in The electromagnetic ultrasonic thickness measurement method used in this paper is the pulse reflection method. The probe generates ultrasonic waves that propagate perpendicular to the surface. Ultrasonic waves will reflect when they reach the lower surface of the test track wall, which continue to propagate upwards along the thickness direction and are then received by the receiving probe on the upper surface.The electromagnetic ultrasonic thickness-detection system, including electromagnetic ultrasonic thickness-measurement probes, data acquisition cards, thickness-measurement processing units and so on, is used for detecting the wall thickness of track pipes, as shown in t between n echo signals can be used to calculate the thickness d, which is given byThe electromagnetic ultrasonic thickness detection system adopts a dual-channel set, mainly consisting of two electromagnetic ultrasonic probes, probe-clamping mechanisms, and data-processing modules. The clamping mechanism fixes the electromagnetic ultrasonic probe between the two active wheels of the front wheel group of the detection device. In order to adapt to various transformation types of the track, the clamping mechanism has two degrees of freedom in both directions to prevent interference between the probe and the track, where probes are stably pressed onto the surface of the track to ensure a certain lift off the gap, thereby maintaining the stability of the probe operation. The fixed type and detection data display of the electromagnetic ultrasound probe are shown in The thickness detection results of the track are shown in The principle of eddy current detection in this paper is that when alternating current is applied to the eddy current detection coil, an alternating magnetic field can be generated. It can induce eddy currents on the surface of the roller coaster track, which generates a magnetic field that interacts with the original coil magnetic field. When there are defects on or near the track surface, the change in induced eddy current will lead to the change in impedance or voltage of the detection coil, which can achieve defect detection on the track surface and near surface. Therefore, the array eddy current crack detection system includes the array eddy current detection module, data-acquisition card, crack-processing unit, etc., as shown in Array eddy current detection modules 1 (on the left rail), 2 (on the left rail side), 3 (on the right rail side) and 4 (on the right rail side) transmit the eddy current detection signal on the track surface to the data-acquisition cards, which convert the eddy current detection signal into a voltage signal and transmit it to the crack-processing module for calculating, analyzing and processing. Then, the information on the crack of the track is generated with track position information from the position module and transmitted to the onboard controller, where crack information based on the position coordinates from the crack-processing unit is transmitted to the handheld operating terminal controller through a wireless control module. Finally, it is displayed in real time on the screen of the human–computer interaction interface and stored in the memory of the handheld operating terminal, which is beneficial for offline data processing and analysis.B and B0 are the surface current density and the eddy current density. z is the distance from the surface of the conductor. f is the frequency of the eddy current excitation signal. μ and σ are the magnetic permeability and conductivity of the tested conductor, respectively. The eddy current penetration depth δ can be obtained byThe array eddy current crack detection system detects cracks on the main walking surface and side wheel walking surface of the track, using a 16-channel array eddy current probe. According to the principle of eddy current, the induced eddy current density in the tested conductor isTherefore, the parameters of the probe are determined based on the required crack depth to be detected, where a stable fixed height is crucial. Each probe integrates four channels and is fixed to the front wheel group of the detection device through a probe clamping mechanism. Four sets of pressure springs and pressure wheels are used to ensure stable contact and lifting height between the probe and the track surface. The array eddy current probe and its fixing type are shown in The surface crack detection results of the track have a large amount of data, and this paper only displays the location signal of the discovered defect, as shown in In summary, a multifunctional inspection robot suitable for roller coaster tracks is reported. Firstly, a vehicle structure was developed that can actively drive and controllably walk on roller coaster tracks. On this basis, a vehicle electrical control system with remote wireless drive control, detection control, data wireless transmission, display, storage and other functions was developed and tested. In terms of testing functions, a video visual inspection system with macro and local video detection of the track has been integrated and tested. Additionally, an electromagnetic ultrasonic thickness-detection system and array eddy current crack-detection system have been equipped, which achieve the wall thickness of the track and surface-crack-detection functions. Finally, the intelligent robot with multiple detection functions visualized the appearance, wall thickness detection, and surface crack detection of a vertical roller coaster track, which were basically consistent with manual retesting results, verifying the effectiveness and accuracy of the multi-functional detection robot for large roller coaster tracks. It should be noted that due to the lack of automatic fault identification, the current version of the detection robot relies on manual assistance. As a result, the potentiality of the multifunctional detection robot as a new detection method targeting tracks with complex trajectories has been exhibited.


Free radicals are produced during metabolic processes in the human body and can lead to oxidative stress, cellular aging, and the development of various diseases. Antioxidants act as protective agents by neutralizing radicals ,2. WhileThis Special Issue, “Health Effects of Natural Antioxidants”, was established to highlight the latest discoveries regarding the molecular mechanisms underlying natural antioxidants, their bioavailability, and their potential therapeutic uses. It aimed to present the most recent advancements in the field, showcasing the significant impacts of natural antioxidants on health and their potential for therapeutic applications.Mimura et al. conducted a study comparing the therapeutic potential of vitamin D3 (VitD) and paricalcitol (Pari) in controlling the development of experimental autoimmune encephalomyelitis (EAE), an animal model for multiple sclerosis (MS). EAE and MS are characterized by immune cell activation, the release of proinflammatory mediators, and demyelination in the central nervous system (CNS). While VitD has demonstrated immunomodulatory and antioxidant properties in controlling EAE, it can also trigger hypercalcemia. To address this issue, the authors sought to evaluate the efficacy of Pari, a non-hypercalcemic vitamin D analogue, in comparison to VitD in controlling EAE. Mice were injected with either VitD or Pari every other day after EAE induction. The researchers assessed various parameters including the recruitment of inflammatory cells, the mRNA expression of inflammatory markers, demyelination in the CNS, the production of proinflammatory cytokines, and inflammation in the gut. The results showed that VitD, but not Pari, exhibited down-modulatory effects by reducing the recruitment of inflammatory cells, suppressing the mRNA expression of inflammatory parameters, and attenuating demyelination in the CNS. Additionally, the EAE/VitD group showed lower production of proinflammatory cytokines by lymph-node-derived cells, reduced IL-17 production by gut explants, and decreased intestinal inflammation compared to the EAE untreated and Pari groups. Furthermore, the study investigated the differentiation of dendritic cells (DCs) in the presence of VitD or Pari. DCs differentiated in the presence of VitD displayed a more tolerogenic phenotype compared to those differentiated with Pari. Overall, the findings suggest that VitD, but not Pari, has the potential to be used as a preventive therapy to control the severity of MS. VitD demonstrated immunomodulatory effects, attenuated inflammatory responses, and protected against demyelination in the CNS. These results highlight the therapeutic potential of VitD in managing MS and provide insights into the immunological mechanisms underlying its beneficial effects [In a study conducted by Tratnjek et al., the aim was to provide a detailed overview of the roles of vitamin A and retinoids in the chemoprevention and treatment of bladder cancer (BC). Bladder cancer (BC) is a prevalent malignancy associated with high recurrence rates and significant morbidity and mortality. The development of effective strategies for chemoprevention and treatment of BC is crucial. Epidemiological studies have suggested a potential link between adequate vitamin A intake and reduced BC risk. Retinoids, which are natural and synthetic derivatives of vitamin A, have gained considerable attention in cancer research due to their antioxidant properties and their ability to regulate cell growth, differentiation, and programmed cell death. Both in vivo and in vitro studies focusing on BC have demonstrated promising results regarding the potential use of retinoids in the prevention and treatment of this disease. However, the translation of these findings into clinical practice has been limited. This narrative review comprehensively discusses several key aspects related to vitamin A and retinoid metabolism, retinoic acid signaling, the pathobiology of BC, the epidemiological evidence supporting the role of dietary vitamin A in BC, mechanistic insights obtained from preclinical models, clinical trials involving retinoids, the limitations associated with retinoid use, innovative approaches for retinoid delivery, and the identification of components within retinoid signaling pathways that may serve as novel therapeutic targets. By providing an in-depth analysis of these topics, this review highlights the potential benefits and challenges associated with retinoid-based interventions in BC. An understanding of vitamin A metabolism and retinoid signaling pathways, as well as their complex involvement in the pathogenesis of BC, may offer valuable insights for the development of effective chemopreventive and therapeutic strategies. However, further research is needed to address the limitations and optimize the utilization of retinoids in clinical settings. The identification of novel delivery systems and the exploration of specific components within retinoid signaling pathways hold promise for enhancing the efficacy of retinoid-based therapies for BC .Cotoraci et al. conducted a study with the objective of exploring the therapeutic potential of biologically active compounds and plant extracts in the treatment of various types of anemia. Anemia, characterized by low hemoglobin levels and reduced oxygen-carrying capacity, is a significant public health concern affecting individuals of all ages. While blood transfusion and oral iron supplements are commonly used treatments, they come with side effects and limitations that hinder their long-term use. This review aims to provide up-to-date information on the use of these compounds and plant extracts as alternative or adjunctive therapies for anemia. By examining the existing literature, the goal is to highlight their potential benefits, considering their natural origins and potential effectiveness in improving treatment outcomes. Ultimately, this review aims to contribute to our understanding of alternative therapeutic approaches for various types of anemia, expanding the available options beyond conventional treatments .In a comprehensive review conducted by Jiménez-Cabrera et al., the primary objective was to examine the potential role of the Erythrina genus in managing inflammatory pain and its antioxidant properties. Oxidative stress and the generation of reactive oxygen species (ROS) have been implicated in the development and progression of various degenerative disorders accompanied by inflammation and pain. Despite the significant impact of oxidative stress on health, effective treatments for pain and inflammation remain a challenge. Multidisciplinary approaches are commonly employed in pain management, and traditional remedies, such as those derived from the Erythrina genus, have been utilized to alleviate pain and inflammation. The Erythrina genus is known to produce a diverse array of secondary metabolites, including flavanones, isoflavones, isoflavones, and pterocarpans, which possess antioxidant activity. Phenolic compounds, found in abundance in Erythrina plants, have demonstrated the ability to counteract pro-oxidants and inhibit key inflammatory signaling pathways, such as MAPK, AP1, and NFκB. While there is preclinical evidence supporting the use of Erythrina compounds for pain management, the precise pharmacological mechanisms underlying their effects are not yet fully understood. This review acknowledges the rapid advancements in drug-discovery-related disciplines but emphasizes that much of nature’s medicinal potential remains untapped. The antioxidant properties of Erythrina compounds may play crucial roles in mitigating oxidative stress and reducing inflammation-associated pain. Understanding the mechanisms of action and the pharmacological effects of these compounds could potentially lead to the development of novel therapeutic approaches for pain management. By deepening our understanding of the compounds derived from Erythrina plants and their mechanisms of action, we can potentially uncover new avenues for pain management and contribute to the advancement of drug discovery in this field .


In recent years, the increased population has led to an increase in the demand for various industrially processed edibles and other consumable products. These industries regularly alter the proteins found in raw materials to generate more commercially viable end-products in order to keep up with consumer demand. These modifications result in a substance that may cause allergic reactions in consumers, thereby creating a protein allergen. The detection of such proteins in various substances is essential for the prevention, diagnosis and treatment of allergic conditions. Bioinformatics and computational methods can be used to analyze the information contained in amino-acid sequences to detect possible allergens. The article presents a deep learning based ensemble approach to identify protein allergens using Extra Tree, Deep Belief Network (DBN), and CatBoost models. The proposed ensemble model achieves higher detection accuracy by combining the prediction results of the three models using majority voting. The evaluation of the proposed model was carried out on the benchmark protein allergen dataset, and the performance analysis revealed that the proposed model outperforms the other state-of-the-art literature techniques with a protein allergen detection accuracy of 89.16%. Its founding in 1948 was motivated by a desire to help people everywhere have happier and healthier lives in the years to come. By providing leadership and organizing efforts to prevent, diagnose, and cure illnesses, the WHO helps governments and other partners improve health outcomes. The WHO plays a pivotal role in the area of allergies by encouraging research and evidence-based policies to prevent and treat allergies and increasing awareness of the global effects of allergies .via research, teaching, and advocacy, and to facilitate communication and collaboration among immunologists. IUIS has formed a subcommittee to standardize the nomenclature and categorization of allergens, known as the Allergen Nomenclature Subcommittee. The IUIS also keeps track of sequences, structures, and cross-reactivity data for allergens in their AllergenOnline database. When it comes to combating the problems caused by allergies, both the WHO and the IUIS play crucial responsibilities . The mission of IUIS is to develop the discipline of immunology bilities . WHO hasbilities .WHO and IUIS have been working together to combat the rising prevalence of allergic diseases. Increased measures to prevent and cure allergies have been advocated for by the WHO. Through its Global Initiative for Asthma, WHO has also created recommendations for the treatment of allergic reactions and asthma . Most alAllergens are compounds that, in those who are sensitive, can cause an allergic reaction. They come from many different places, such as pollens, moulds, foods, and animal dander. From minor itching and sneezing to severe anaphylaxis, allergens can induce a variety of symptoms that can be life-threatening . As discA growing number of protein allergen sequences are being examined to find possible allergenic areas using bioinformatics methods. These technologies analyse the sequences using computer techniques to find potential immune response-inducing areas. Studying protein allergen sequences is crucial for understanding the mechanisms behind allergy reactions and creating effective diagnostic and treatment strategies. Using bioinformatics tools, standardising allergen naming and categorization, and applying machine learning and deep learning algorithms can help us learn more about protein allergens and their role in allergies.Machine learning systems can predict and detect allergenic proteins based on their sequence or structure. Training models using allergen data can predict allergenicity in newly found proteins and accelerate allergy diagnosis and treatment.Machine learning can extract amino acid composition, physicochemical features, and secondary or tertiary structural components from protein sequences and structures. These properties help to categorize allergic and non-allergenic proteins.When an allergic person reacts to structurally or functionally identical allergenic proteins, machine learning can predict cross-reactivity. Machine learning can assist in the design of hypoallergenic foods and allergy treatments by discovering cross-reactive protein families or motifs.Based on sequence or structural data, machine learning may identify allergenic protein sources . This can help discover allergies in new food or environmental samples.Machine learning can help personalize allergy diagnosis, treatment, and management by combining patient data like genetics and allergen exposure history.In protein allergen classification, machine learning is a powerful tool. Machine learning can help diagnose and manage allergies by using computational methods. The key role of machine learning in allergy control includes :MachineMachine learning helps characterize and categorize protein allergens. Allergen identification, categorization, cross-reactivity prediction, and personalized medication improve allergy diagnosis, treatment, and management.Data about allergens can be retrieved from a wide number of resources, such as the Allergen Nomenclature database maintained by the IUIS, the Structural Database of Allergenic Proteins (SDAP), and the AllergenOnline database. These databases include well-curated information on known allergens, including the sequences and structures of proteins .The Protein Data Bank (PDB), the Swiss-Prot database, and the Structural Classification of Proteins (SCOP) database are some of the places where non-allergen data may be found . Other pData that does not pertain to allergens was included since it helps create a more well-rounded dataset for the purpose of training machine learning and deep learning algorithms. The inclusion of proteins that do not cause allergic reactions enables the algorithms to acquire the knowledge necessary to distinguish between allergenic and non-allergenic proteins based on their sequence features. Due to the fact that many proteins that are not allergenic may have sequence similarities to allergens, this is an extremely important factor for accurate allergenicity prediction.Physicochemical properties: The term ‘physicochemical properties of protein sequences’ refers to the recognized physical and chemical properties of the amino acids that are used to form a protein. These characteristics have the potential to influence the structure, stability, and function of proteins, and they are essential for gaining a knowledge of the biological functions of proteins as well as creating medications and therapies that specifically target certain proteins .Molecular properties: The functions and behaviours of proteins at the molecular level are referred to as the molecular properties of protein sequences. Understanding protein function and interactions, as well as creating medications and therapies that target specific proteins, relies heavily on these characteristics .Structural descriptors: Protein structural features encompass the primary, secondary, tertiary, and Quaternary structure, as well as solvent accessibility, of the amino acid sequence. These characteristics are critical for understanding protein function and interaction with other molecules and for designing therapies and medicines with particular protein targets .Various properties are extracted from these allergen and non-allergen sequences as shown in in silico model that predicts the allergenic potential of a protein based on its physicochemical and biological features. The ProtParam tools and the PSIPred Protein Sequence Analysis programme were used to figure out 29 factors from the protein sequence.in-silico analysis and bioinformatics methods. The study attained an accuracy of 88.24% by analysing the attributes of the allergens and applying the PseAAC concept together with deep learning algorithms for categorization. In terms of their extinction coefficient and secondary structure, plant allergen proteins showed a more randomly coiled shape than animal allergen proteins, according to the investigation. The research shows the promise of bioinformatics-based methods for comparing allergens and elucidating their characteristics.The physiochemical and structural characteristics of allergen proteins originating from plants and animals were examined and compared in in silico tools, improved in vitro testing integration, and clearer guidance on the overall weight-of-evidence strategy for protein safety are among the major modernization areas. Future goods, such as those made using novel genomic methods and synthetic biology, will require the allergenicity risk assessment to change in order to handle their complexity. A road map that addresses important issues for risk assessors and management is required to clarify allergenicity safety objectives and risk assessment requirements.In the context of sustainable food systems and goods produced from biotechnology, the scientific opinion in The study in in vitro experiments and the QSAR model.The study in The study in In comparison to previous computational techniques, AllerCatPro 2.0  is a cometc., using ensemble learning.In this section we describe the details of proposed model for the detection of protein allergens. The literature suggests that by combining the outcome of different machine learning models together using ensemble learning techniques can enhance the overall accuracy of prediction. This work uses this concept and hence proposes an ensemble learning model to detect protein allergens in a given dataset. The system model for the proposed mechanism is shown in In the initial phase, we processed each protein sequence collected from different sources. The allergen protein sequences were accessed from AllergenOnline , and nonThe performance of any machine learning algorithm depends on the values contained in the dataset, and the prediction results are effective if all the feature values are numeric and lie in a particular range. In order to achieve this, we have replaced any missing value with the mean of the corresponding feature vector and encoded non-numeric values to numeric values using the labeled encoding technique.The updated dataset was then passed to the feature scaling phase, where we used a standard scalar to transform all the values of feature vectors. If The completion of feature extraction, pre-processing and feature scaling results into a pre-processed dataset containing 128 features and 4,854 entries , which can then be used for training and testing the machine learning models.We have selected three models for constructing the proposed ensemble learning model for detecting protein allergens. The selection was based on a thorough performance analysis of different machine learning and deep learning models on the dataset.Extra Tree: Decision tree algorithms are the basis of the Extra Trees (Extremely Randomised Trees) classifier, another ensemble learning approach. Training numerous decision trees and aggregating their predictions results in a more accurate and robust categorization. In comparison to the standard Random Forest algorithm, the Extra Trees classifier introduces additional randomness, leading to more diverse trees and improved generalisation performance . They are taught greedily, layer after layer, using unsupervised pre-training to set their starting weights and then supervised learning to fine-tune them. DBNs have been successful in a number of contexts, including those involving image and speech recognition, NLP, and dimensionality reduction  and CatBoost models, the details of which are given as:As discussed earlier in this section, we have selected three models based on the performance analysis of various machine learning and deep learning models, to create an ensemble model. The other machine learning models used in the study include: Logistic Regression (with maximum iterations of 300 on L2 regularization and Limited-memory BFGS optimizer), k-Nearest Neighbour . The third hidden layer has 13 neurons that are responsible for pattern recognition for major feature descriptors which include hydrophobicity, kideraFactors, amino acid property scales, VHSE scales, Z scales, Cruciani molecular descriptors, WHIM molecular descriptors, BLOSUM matrix scores, Boman index, charge, molecular weight, peptide length and protein fingerprint.The deep learning models include: Multi-layer perceptron and DBN, the architecture of which is shown in Each of the models in the proposed ensemble learning approach receives feature values concurrently and predicts the class label . The prediction results of the base models are then combined together using majority voting, the output of which gives the final predicted class label for the input values. In majority voting, the class label that receives the most votes becomes the ensemble’s prediction for that instance. We have used majority voting because it lowers the likelihood of overfitting, boosts model stability, and increases prediction accuracy .The performance analysis of the proposed model and other machine learning and deep learning models on the dataset was implemented using Python 3.10 with scikit-learn, PyTorch and other related packages on 1.6 GHz Intel Core i5 with 12 GB of RAM. The dataset was divided into training and testing set by random selection of feature values. The training and testing datasets contained 3,397 and 1,457 respectively, randomly selected entries.In order to evaluate the performance of each learning model, we have trained each model on the training dataset and tested it on the testing dataset. The test results were calculated using confusion matrix  curve and Precision-Recall (PR) curve, and calculated Area Under the Curves (AUC). The training and testing time was also noted during the analysis.In the study, we have also used deep learning models (MLP and DBN) for allergen classification. The loss occurred during training of these models with varying epoch is shown in The test results of various machine learning, deep learning and proposed model are presented in The lower values of False Positives and False Negatives for DBN, Extra Tree and CatBoost models, as shown in The The F1-score is a statistic that integrates recall and precision into one performance parameter for the model. It offers a fair assessment of the model’s capacity to recognize both positive and negative instances. When the cost of false positives and false negatives is not equal, this is especially crucial. The F1-score of various studied models is shown in i.e., it tells us how well the model is able to detect undesirable circumstances. Specificity quantifies the percentage of true negatives that the model properly detects, The significance of Matthews correlation coefficient (MCC) is that it does not depend on unbalanced datasets and gives a fair assessment of the model’s performance, accounting for both true positives and true negatives. The MCC runs from −1 to +1, with +1 denoting perfect agreement, 0 denoting predictions that are no better than random, and −1 denoting utter disagreement between the predicted and the actual values. The MCC value of each model on the test dataset is shown in Apart from the already described performance parameters, we have also plotted ROC and PR curves for various models on the test dataset. Since the number of models are more, that is why we have presented the ROC and PR curves in two parts. The ROC curves are shown in The combined analysis of all the presented results clearly reveals that the proposed model effectively detects allergen and non-allergen protein sequences. The proposed model has maximum value of detection accuracy, F1-score, MCC and area under the ROC and PR curves.To further specify the significance of the proposed model, we have compared its performance with AllerTOP , AlgPredThis article presented the analysis of various machine learning and deep learning techniques to propose an ensemble learning based approach for the identification of protein allergens. The article also highlighted various entities and concepts related to allergy control. In this article, we also described various protein sequence properties like physicochemical, molecular, antimicrobial and other characteristics to extract the feature vectors, giving rise to the dataset used for training and testing of intelligent techniques. The performance evaluation of various machine learning, deep learning techniques and proposed mechanism revealed that the proposed mechanism outperforms the other discussed techniques and achieved an allergen detection accuracy of 89.16%. The performance of proposed mechanism was also compared with the state-of-the-art literature techniques, which used the similar dataset as used in this study, and the results prove that the proposed mechanism outperforms the other techniques.etc., can be used which will further enhance the performance of the proposed mechanism. Furthermore, the diversity of the dataset can be increased in the future by adding data from different geographical regions for increasing the precision of the proposed model.Even though the proposed model outperforms the other related techniques, the overall accuracy can be further enhanced. So, in the future, various approaches like increasing the size of dataset, use of different optimization and regularization techniques, 10.7717/peerj-cs.1622/supp-1Supplemental Information 1First column represents protein sequences followed by features associated to that sequence. The last column 'allergen' represents the target. 1 represent allergen, 0 represent non-allergen.Click here for additional data file.10.7717/peerj-cs.1622/supp-2Supplemental Information 2Click here for additional data file.


Atherosclerosis is a multifactorial chronic disease triggered and sustained by different risk factors such as dyslipidemia, hypertension, diabetes mellitus (DM), smoke, elevated homocysteine, and hormones . AtherosThis Special Issue entitled “Atherosclerosis: From Molecular Biology to Therapeutic Perspective 3.0” contains a combination of original articles and reviews written by an international panel of researchers and reviewed by several other experts in the field of cardiovascular sciences, focused on the molecular mechanisms related to the pathogenesis of the disease, the methods that can detect and characterize the lesions, and the potential novel therapeutic strategies for targeting atherosclerotic plaques.SMCs, as the major cell component of the vessel wall, play an integral role in vasoconstriction and blood pressure regulation and maintain vascular structural integrity. SMCs can also participate in vascular remodeling in both physiological or pathological states by virtue of their capacity to change their phenotype in response to changes in local environmental cues. Under healthy conditions, they exist in a contractile, well-differentiated phenotype. In cases of disease or injury, SMCs undergo a profound transformation to a less differentiated state, acquiring a proliferative potential and migratory properties. A wide range of regulatory factors and signaling pathways can promote this phenotypic transition ,6. MiyabAs briefly mentioned earlier, ox-LDLs are thought to play a central role in the pathogenesis of atherosclerosis. OxLDL has been demonstrated to promote monocyte migration into the vessel wall, endothelial cell (ECs) activation and dysfunction, foam cell formation, SMC migration and proliferation, and to exert powerful pro-inflammatory and pro-oxidative stress effects . AdditioThe interaction of LDL particles with the arterial glycosaminoglycan (GAG) chain(s) present in the extracellular matrix and on cell membranes significantly contributes to the focal retention of lipids, increases the oxidation levels of LDL in the intima, and induces the formation of foam cells during atherogenesis . RecentlVascular inflammation plays critical roles in all phases of the atherosclerotic process, from its beginning to the onset of clinical complications . PerticoAccording to the assumption that a wide range of cells of both innate and adaptive immunity are relevant to atherosclerosis initiation and progression , SaigusaGrowing evidence suggests that vascular inflammation is associated with adventitial and perivascular vascularization. Vasa vasorum (VV) provide nourishment to the media and facilitate the transport of inflammatory cells from the perivascular adipose tissue to the adventitia ,17. WithThe contribution of inflammation to atherosclerosis has been also analyzed in the review by Richter and colleagues, who discussed novel pro-inflammatory and pro-atherosclerotic risk factors that are closely related to systemic lupus erythematosus (SLE) inflammation, and which determine an increased risk for the occurrence of early cardiovascular events.Dabravolski et al. summarized recent data demonstrating the biological role of pericytes in various aspects of atherosclerosis pathogenesis and discussed how novel molecular and cellular insights into pericyte biology may help to find new candidate targets for more specific and effective therapeutic modalities.An interesting correlation between plasma fortilin levels and the severity of coronary artery disease (CAD) has been proposed by Aoyama and co-workers, while the review by Gardin et al. has summarized the various methods currently employed for exosome isolation, with a focus on platelet-derived exosomes, and the authors have discussed the role these extracellular vesicles play in the pathogenesis of atherosclerosis and their therapeutic possibilities.Recently, obstructive sleep apnea (OSA) has emerged as an independent risk factor for atherosclerosis. OSA may contribute to atherosclerosis via direct and indirect mechanisms. Endothelial dysfunction, sympathetic stimulation, and the generation of pro-inflammatory cytokines induced by OSA may play a crucial part in the pathogenesis of atherosclerosis ,19. ClinIn recent years, numerous studies have highlighted the role of the miR-17/92 cluster in cardiovascular physiology, and dysregulation in the expression levels of miR17-92 cluster members has been broadly implicated in cardiomyopathy, aberrant cardiomyocyte differentiation, myocardial infarction, and cardiac aging ,22. miR1Kamato and colleagues perform an extensive review of the various non-mouse animal models of atherosclerotic plaque formation and rupture , with the aim to provide a basis to select the most relevant model to investigate the molecular mechanisms underlying the disease process and its clinical consequences.Finally, Romero-Cabrera et al. reviewed the available evidence on the ability of novel lipoprotein biomarkers to accurately predict CVD risk among middle-aged adults without history, signs, and symptoms of known CVD, while Vellasamy et al. has explained how the aging of the immune system (immunosenescence) contributes to atherosclerosis.I would like to express my special appreciation and thanks to all editorial board members, reviewers, and authors; without their cooperation, the present Special Issue would not have come into being.I sincerely hope that you will enjoy reading this Special Issue and that it will stimulate new ideas for improvement. Happy reading!