Additive manufacturing has gained significant popularity from a manufacturing perspective due to its potential for improving production efficiency. However, ensuring consistent product quality within predetermined equipment, cost, and time constraints remains a persistent challenge. Surface roughness, a crucial quality parameter, presents difficulties in meeting the required standards, posing significant challenges in industries such as automotive, aerospace, medical devices, energy, optics, and electronics manufacturing, where surface quality directly impacts performance and functionality. As a result, researchers have given great attention to improving the quality of manufactured parts, particularly by predicting surface roughness using different parameters related to the manufactured parts. Artificial intelligence (AI) is one of the methods used by researchers to predict the surface quality of additively fabricated parts. Numerous research studies have developed models utilizing AI methods, including recent deep learning and machine learning approaches, which are effective in cost reduction and saving time, and are emerging as a promising technique. This paper presents the recent advancements in machine learning and AI deep learning techniques employed by researchers. Additionally, the paper discusses the limitations, challenges, and future directions for applying AI in surface roughness prediction for additively manufactured components. Through this review paper, it becomes evident that integrating AI methodologies holds great potential to improve the productivity and competitiveness of the additive manufacturing process. This integration minimizes the need for re-processing machined components and ensures compliance with technical specifications. By leveraging AI, the industry can enhance efficiency and overcome the challenges associated with achieving consistent product quality in additive manufacturing. Over recent decades, the manufacturing sector has gone through significant changes, yet it continues to play a critical role in the growth and development of both developed and developing countries . It is cThe main advantage of additive manufacturing is that it can fabricate complex shapes and reduces material waste and production time (for some) ,12,13,14The surface quality of additively manufactured components is influenced by a multitude of factors that interact in intricate ways. These factors include the choice of AM technique, powder characteristics, layer thickness, scanning strategy, and energy parameters specific to each technique. For instance, in Vat Photopolymerization (VP), parameters such as layer thickness, exposure time, and resin properties contribute to surface quality ,41. SimiPowder Bed Fusion (PBF) ,55 and DFurthermore, post-build treatments significantly contribute to the final surface quality of additively manufactured components ,66. For Surface roughness has been identified as one of the most significant factors affecting product quality, as stated in previous research . For insThe impact of surface roughness on system performance is significant across multiple industries, such as aerospace ,76, autoDifferent methods were used to predict the surface roughness of additively manufactured components, i.e., Taguchi-based regression models ,88, statHowever, the emergence of Artificial Intelligence (AI) approaches, including both machine learning and deep learning, has opened avenues to surmount these limitations. These AI approaches offer the potential to yield more precise and adaptable predictions of surface roughness. By harnessing these advancements in AI, particularly in machine learning, we can transcend the confines of traditional methods and attain the capability to design and fabricate high-quality components with unparalleled precision and efficiency. The flexibility, accuracy, and capacity to discern intricate patterns inherent in machine learning techniques empower us to overcome the challenges posed by conventional methods.Machine learning methods have demonstrated remarkable capabilities in predicting complex patterns and relationships within diverse datasets ,94. TheiDeep learning, a subset of machine learning, adds a layer of sophistication to predictive modeling ,97. NeurRecently researchers gave great attention to AI for surface roughness prediction. Thus, this paper presents machine learning models or techniques used to predict the surface roughness of 3D printed parts. The remainder of the paper is organized as follows: To conduct this review, a rigorous and comprehensive methodology was employed. This section outlines the key steps undertaken to ensure a systematic and transparent approach to the review process. The systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines  to ensurThe search strategy was developed to identify relevant studies on the application of artificial intelligence for surface roughness prediction of additively manufactured components. The following databases were searched: PubMed, IEEE Xplore, ScienceDirect, MDPI, and Google Scholar. The search terms used included variations and combinations of the following keywords: “artificial intelligence”, “machine learning”, “deep learning”, “surface roughness”, “prediction”, “additive manufacturing”, and “3D printing”. To consider all published papers related to the surface roughness prediction of additively manufactured components, the database search strategy focused on articles, proceedings, and books published in the journals listed above for the last 10 years.Published materials in peer-reviewed journals or conference proceedings.Studies targeting the application of artificial intelligence techniques for surface roughness prediction in additively manufactured components.Studies that provided detailed methodology and results related to surface roughness prediction using artificial intelligence.Studies published in the English language.The inclusion criteria used to select studies were defined as follows:Studies that did not specifically address surface roughness prediction in additively manufactured components.Studies that focused solely on conventional manufacturing processes.Studies that were not available in full-text or were duplicates.Studies that were published in another language than English.The exclusion criteria for selecting studies were defined as follows:Two phases were used in the selection process: title/abstract screening and full-text assessment. The titles and abstracts of the identified studies are based on the inclusion and exclusion criteria. Any mismatch was resolved through discussion and consensus. In the full-text assessment phase, the reviewers thoroughly examined the selected articles to find out their eligibility for inclusion in the review.Objective of the study.Methodology employed .Key findings and conclusions related to surface roughness prediction.Limitations and future directions identified by the authors.The extracted data were then synthesized and analyzed to identify common themes, trends, and patterns across the reviewed studies.Data extraction was done by the reviewer using a predefined data extraction form. The following information was extracted from each selected study:In this section, an overview of artificial intelligence and its application in predicting surface roughness is presented.Artificial intelligence is defined as a subset of IT applications that can capture information from its environment, comprehend, learn, interpret, and derive actions based on their implemented objectives . In its Machine Learning, a prominent subset of AI, falls under the narrow AI category. It is characterized by its task-specific nature and focuses on a limited range of functionalities. Machine Learning algorithms are designed to learn from input data and enhance their performance in specific domains such as image recognition or natural language processing . MachineMachine learning plays a significant role in additive manufacturing, where it finds numerous applications. The integration of AI in additive manufacturing has the potential to revolutionize the field . In a reAdditively manufactured surfaces, as depicted in aS) and area root mean squared height (qS) are insensitive to measurement parameters [skS) effectively characterizes surfaces in SLM parts. Surface roughness impacts microstructures with peaks and valleys of varying heights, which is crucial as components miniaturize [Surface roughness, a crucial texture element, evaluates manufactured item quality by assessing topographical feature distribution. Diverse metrics are employed across industries, addressing uncertainty in 3D-printed product surface quality through multiple metrics for efficiency . For insrameters . Area heiaturize . As suchSurface topography evaluations based on 3D-scanned image data of sample surfaces are categorized as linear measurements denoted as “R” or aerial surface measurements denoted as “S”, as per ISO 21920-2  and ISO zR) outperforms qR and aR in correlating with the tactile and visual assessments of surface roughness. However, relying solely on zR may not fully encompass the impact of appearance factors such as texture and color on human perception and surface quality. Extracting roughness profiles lacks reproducibility due to instrument dependency and operational factors, as reported in studies [Various methods can be employed to measure surface roughness, depending on different definitions ,118. In  studies ,120. Whi studies ) are gaiThe prediction of surface roughness for additively manufactured components is a fundamental application of machine learning, where data-driven modeling is used to predict the surface roughness of additively manufactured components . There aProcess parameters: These include data related to the additive manufacturing process. In LPBF, for example, parameters such as laser power, scan speed, layer thickness, and hatch spacing  have beeMaterial properties: These include data related to the material used in the additive manufacturing process, such as powder size, particle shape, and chemical composition, especially in methods such as L-PBF for metal AM. Material properties can also have a significant impact on surface roughness .Geometrical features: These include data related to the geometry of the final product, such as the angle and direction of the surface, the size and shape of the features, and the number of layers. Geometrical features can influence the surface roughness by affecting the thermal and mechanical properties of the material. For example, in FDM, building orientation has been employed to predict surface quality . In the Environmental conditions: These include data related to the ambient conditions during the additive manufacturing process, such as temperature, humidity, and pressure. These conditions can affect the material properties and, in turn, the surface roughness of the final product .Surface texture data: These types of data involve measuring the surface roughness of an additively manufactured component using specialized instruments such as profilometers or surface roughness testers. The surface texture data can be used as a training dataset for machine learning models to predict surface roughness based on process parameters, material properties, geometrical features, and environmental conditions ,127.Vibration data: Vibration data play a crucial role in predicting surface roughness for additively manufactured components. By capturing and analyzing the vibrational characteristics during the additive manufacturing process, valuable insights can be gained regarding the quality and surface roughness of the manufactured components. Vibration data can help identify any anomalies, such as excessive vibrations or oscillations that may affect the surface roughness. In the FDM-based 3D printing process, for instance, vibration data were integrated to monitor the vibration of the extruder and the table . VibratiIn this section, we explore the literature of traditional and deep learning and machine learning algorithms. These incredible algorithms have been used to uncover the secrets of predicting surface roughness in components made through additive manufacturing.It should be emphasized that conventional machine learning techniques offer a strong basis for surface roughness prediction. However, they might face challenges in capturing intricate non-linear connections or managing data with high dimensions. The application of deep learning techniques has gained prominence in recent years due to their ability to handle these challenges more effectively. The common traditional machine learning algorithms that can be applied for the surface roughness prediction of additively manufactured components are Linear Regression, Support Vector Machine, Random Forests, Gradient Boosting Algorithms, and Artificial Neural Networks.A surface roughness prediction in the context of additive manufacturing can be facilitated through various supervised learning algorithms. One such algorithm is SVM, which aims to find an optimal hyperplane for data classification. SVM is widely used due to its ability to handle both linear and nonlinear data, as well as its high accuracy .Singh et al.  applied Joshi et al.  exploredMishra et al.  focused Random forests are a type of ensemble model that combines multiple decision trees. Each tree is trained on a different subset of data and features, and the final prediction is obtained through a voting or averaging process. The strength of random forests lies in their ability to handle complex relationships and interactions among input parameters, making them well-suited for surface roughness prediction .Li et al.  utilizedIn their article, Wu et al.  addresseWu et al.  utilizedMachine learning algorithms, including random forests, support vector regression, ridge regression (RR), and a least absolute shrinkage and selection operator (LASSO), were employed by Wu et al.  to accurK-nearest neighbors (KNN) is a non-parametric algorithm that utilizes proximity to the nearest neighbors to classify or predict data points. In the context of surface roughness prediction, KNN can estimate roughness by identifying the nearest neighbors with similar input parameter values .Kumar and Jain  focused In the paper , the focANNs are fascinating machine learning algorithms inspired by the intricate structure and functionality of the human brain. Composed of interconnected nodes called “neurons”, ANNs possess the remarkable ability to process and transmit information. These networks find applications in a wide range of tasks, including pattern recognition, image and speech recognition, prediction, and classification .One captivating application of ANNs lies in the prediction of surface roughness for additively manufactured components . AdditivANN have been widely utilized by researchers to predict the surface roughness of additively manufactured components. In a remarkable study conducted by Wafa and Abdulshahed , they emIn another intriguing work, Lakshmi and Arumaikkannu  suggesteVahabli and Rahmait  made sigThe work of Mahapatra and Sood  focused Jiang et al.  investigPlaza et al.  conductePfleging  aimed toKandananond  utilizedLastly, Ulkir et al.  conducteThere are other machine learning algorithms used for surface roughness prediction. For instance, a machine learning method based on Gaussian Process Regression was proposed to establish a model relating the WAAM process parameters to the top surface roughness. To measure the top surface roughness of a manufactured part, a 3D laser measurement system was developed. The experimental datasets were collected and subsequently divided into training and testing datasets. Using the training datasets, a top surface roughness model was constructed and then verified using the testing datasets. The experimental results demonstrate that the proposed method achieves a surface roughness prediction accuracy of less than 50 µm .Li et al.  introducMishra and Jatti  conducteMachine learning techniques are considered a weak AI type, which means they are not entirely autonomous and require some level of guidance, such as adjusting hyperparameters. Deep learning (DL) methods were developed to push beyond the limitations of traditional machine learning, which are also aimed to emulate certain aspects of human cognition more closely. Consequently, they have proved to have a superior performance in terms of accuracy and speed compared to other machine learning algorithms, without the need for significant manual intervention from programmers. Deep learning is a specific subset of machine learning that operates by processing inputs through a biologically inspired ANN architecture. Over time, it has become evident that neural networks outperform many other algorithms in accuracy and speed due to their powerful ability to extract relevant information from vast amounts of data .As can be observed from Deep learning possesses remarkable capabilities for modeling and handling highly intricate non-linear relationships. Numerous variants of deep learning techniques are available, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), artificial neural networks (ANNs), and deep neural networks (DNNs). These neural networks consist of artificial neurons organized in multiple layers, where each layer communicates only with the immediately preceding and following layers  are widely used in image recognition and classification tasks, particularly in the analysis of surface topography scans and visual representations of additively manufactured components. These networks utilize convolutional layers to extract hierarchical features from input data, allowing them to capture intricate patterns and textures related to surface roughness. Researchers have recently employed CNNs for predicting the surface roughness of additively manufactured components .In a recent study , a deep In another work , researcIn a paper , an autoMachine learning can be leveraged to automate common or time-consuming engineering tasks using existing data. For example, design repositories can be employed to train deep learning algorithms to assess component manufacturability. However, methods for determining the suitability of design repositories for machine learning are lacking . A studyFurthermore, a machine learning integrated design for the AM framework was proposed, utilizing ML’s ability to learn complex relationships between design and performance spaces. A case study demonstrated ML’s effectiveness in designing a customized ankle brace with a tunable mechanical performance and tailored stiffness .A deep neural network (DNN) is an ANN having multiple layers between the input and output layers. A DNN consists of neurons, synapses, biases, weights, and functions. Deep neural networks have demonstrated discriminative and representative learning capabilities across various applications in recent years ,159.So et al.  developeIn the prediction of surface roughness for an AlSi10Mg aluminum alloy fabricated through laser powder bed fusion (LPBF), a deep learning framework was developed . The fraIn the study on selective laser melting, a deep neural network was applied to classify melt-pool images based on laser power labels . The neuPost-processing methods, such as shot peening (SP), severe vibratory peening (SVP), and laser shock peening (LSP), are commonly employed to address surface imperfections and bulk defects in additive manufactured materials. In a previous study, fracture surfaces of failed samples were analyzed, and experimental data were elaborated using a machine learning-based approach . The stuGenerative Adversarial Networks (GANs) have shown promise in predicting the surface roughness of additively manufactured components by generating synthetic profiles that closely resemble real-world data. GANs consist of a generator network and a discriminator network that compete against each other, enabling them to learn the underlying distribution of surface roughness and aid in prediction tasks .In a recent paper , a novelTo address the challenge of controlling porosity in metal additive manufacturing, a framework combining Generative Adversarial Networks and Mallat Scattering Transform-based autocorrelation methods was introduced . This frIn laser powder bed fusion (L-PBF), predicting the morphology of the melt pool is crucial for controlling the final build quality. Machine learning techniques were leveraged to predict both the quantitative attributes (size) and qualitative attributes (shape) of the melt pool . An LSTMA novel method utilizing artificial intelligence was proposed to generate the virtual surface morphology of Ti-6Al-4V parts based on given process parameters . ConditiIn the context of quality assurance in advanced manufacturing processes such as additive manufacturing, imbalanced training data can hinder the detection of abnormal states. To address this issue, a data augmentation method based on a Generative Adversarial Network was proposed . By joinAutoencoders are a type of unsupervised learning model used for dimensionality reduction and feature extraction. They encode and decode input data to learn compact representations of surface roughness profiles. These representations can be used in traditional machine learning models or for visualization purposes .In the context of additive manufacturing, post-processing methods are commonly employed to address surface imperfections and bulk defects. Previous studies analyzed the effects of different peening-based treatments on the fatigue performance of laser powder bed fusion AlSi1Mg samples. The fracture surfaces of failed samples were further analyzed, and machine learning-based approaches were utilized to identify correlations between the residual stress, hardness, surface roughness, depth of the crack initiation site, and fatigue life of the post-treated samples. A deep neural network (DNN) and stacked autoencoder (SAE) were used to develop a machine learning model, with the SAE providing accurate predicted results. Parametric analyses and sensitivity analyses were performed to assess the importance of each input factor. The results showed that enhancing surface hardening, inducing higher compressive residual stresses, and reducing surface roughness led to deeper crack initiation sites and improved fatigue life .Additive manufacturing is increasingly used in quality-critical applications, such as aerospace and healthcare, for fabricating complex geometries with novel materials. However, the layer-wise surface quality in additive manufacturing can be relatively poor, affecting the properties and functionality of the products. Surface morphology studies have demonstrated the significant impact of AM machine parameters on the resulting surface. Analyzing and correlating morphology features with machine parameters pose challenges due to the highly nonlinear nature of surface profiles and the presence of outliers and missing regions. To address these challenges, a convolutional autoencoder-based approach is applied in this paper to extract informative features from surface profiles. The autoencoder model, with regularization, can effectively discover low-dimensional representations from high-dimensional surface profile data, even in the presence of corruptions. Supervised machine learning is then employed to quantify the correlation between surface morphology and machine parameters. A case study in laser engineered net shaping (LENS) validates the effectiveness of the proposed method, achieving a classification accuracy of 70%, outperforming benchmark methods. Thus, the developed convolutional autoencoder-based approach shows promise for extracting surface morphology features in additive manufacturing .Deep Reinforcement Learning (DRL) is a combination of deep learning and reinforcement learning techniques. In the context of surface roughness prediction in additive manufacturing, DRL has emerged as a promising approach for optimizing the manufacturing process to achieve desired surface roughness outcomes. By employing DRL agents, which are intelligent systems, optimal control policies can be learned through interaction with the manufacturing environment and feedback on surface roughness performance .This study introduces a DRL framework for deriving a versatile control strategy aimed at minimizing the occurrence of defects. The control policy generated by the DRL framework modifies either the velocity or power of the laser during the melting process. This adjustment ensures the consistency of the melt pool and reduces overheating in the final product. To train and validate the control policy, efficient simulations of the temperature distribution within the powder bed layer are conducted under different laser trajectories .The presented studies demonstrate a diverse range of AI techniques employed for surface roughness prediction in additively manufactured components. Each study offers unique insights into the efficacy of different methodologies, shedding light on the advantages and limitations of AI-driven approaches in this domain. The variety of input data used across these studies highlights the adaptability of AI techniques to different additive manufacturing processes. Mishra et al.  utilizedThe performance metrics across the studies, such as R2, RMSE, and MAE, reflect the success of AI models in capturing complex patterns and providing accurate surface roughness predictions. The notable performance of the XGBoost algorithm in the study by Mishra et al.  highlighhttps://xgboost.ai/), consistently demonstrated high accuracy, they often sacrifice interpretability. In contrast, ensemble methods such as the Random Forest Regressor, as seen in Saxena et al. [While deep learning techniques, such as ANNs and XGBoost , is another promising future direction. GANs can be employed to generate synthetic surface roughness patterns that resemble real-world variations. These generative models can augment limited datasets, simulate different surface roughness scenarios, and enhance the robustness of AI models. By generating synthetic data, GANs can alleviate the data scarcity issue and improve the generalization capabilities of AI models.The development of explainable AI models is also critical for the future of surface roughness predictions in additive manufacturing. Explainability is essential for users and stakeholders to understand and trust the predictions made by AI models. Methods for explaining the decision-making process of AI models need to be explored and implemented. This can provide insights into the factors influencing surface roughness predictions and enable users to identify opportunities for process optimization and improvement. Explainable AI models can enhance transparency, trust, and acceptance of AI techniques in the additive manufacturing industry.Currently, the majority of studies in surface roughness prediction for additively manufactured components primarily concentrate on process parameters. Only a limited number of papers explore alternative data sources such as vibration data, material properties, environmental conditions, and geometric features for predictive modeling. Future research endeavors should explore and integrate these underutilized data types to enhance the accuracy and robustness of surface roughness predictions in additive manufacturing.Additionally, future research should focus on exploring advanced feature selection techniques specifically tailored for surface roughness prediction in additive manufacturing. The development of automated feature extraction methods that can effectively capture the underlying factors influencing surface roughness would enhance the predictive performance of AI models. Advanced feature selection techniques, such as deep feature learning or genetic algorithms, can help identify the most relevant and informative features from complex datasets.This review paper has explored the application of AI for surface roughness predictions of additively manufactured components. The literature review has provided an overview of the recent advancements in machine learning and AI deep learning techniques employed by researchers in this field. The findings demonstrate that AI-based methods have shown promising results in predicting surface roughness, offering benefits such as cost reduction and time savings. However, certain limitations and challenges, including the availability of quality data and model interpretability, need to be addressed. Future research should focus on addressing these challenges, developing more accurate and robust models, and integrating AI methodologies into the additive manufacturing process. Overall, leveraging AI for surface roughness predictions holds great potential for improving the productivity, competitiveness, and consistent product quality of additively manufactured components. Future research should focus on addressing the challenges and refining AI-based approaches to enhance surface quality control in additive manufacturing processes.


Silencing genes using small interfering (si) RNA is a promising strategy for treating cancer. However, the curative effect of siRNA is severely constrained by low serum stability and cell membrane permeability. Therefore, improving the delivery efficiency of siRNA for cancer treatment is a research hotspot. Recently, mesoporous silica nanoparticles (MSNs) have emerged as bright delivery vehicles for nucleic acid drugs. A comprehensive understanding of the design of MSN-based vectors is crucial for the application of siRNA in cancer therapy. We discuss several surface-functionalized MSNs’ advancements as effective siRNA delivery vehicles in this paper. The advantages of using MSNs for siRNA loading regarding considerations of different shapes, various options for surface functionalization, and customizable pore sizes are highlighted. We discuss the recent investigations into strategies that efficiently improve cellular uptake, facilitate endosomal escape, and promote cargo dissociation from the MSNs for enhanced intracellular siRNA delivery. Also, particular attention was paid to the exciting progress made by combining RNAi with other therapies to improve cancer therapeutic outcomes. The Food and Drug Administration (FDA) approved the first RNA interference (RNAi) therapeutic in 2018, Patisiran . Due to siRNAs, with a length of 20 to 25 nucleotides, can bind to specific complementary mRNA pairs, thus triggering mRNA degradation through the RNA-induced silencing complex (RISC) to exert therapeutic effects. Currently, RNAi therapies can be classified based on the distinct targets of selected therapeutic genes, including reversing the drug resistance of tumor cells, promoting apoptosis, inhibiting angiogenesis, or acting synergistically with immunotherapy. Among them, reversing the drug resistance of tumor cells by altering the expression of resistance proteins through gene silencing is a frequent therapeutic strategy. The gene regulation of apoptosis-related proteins is an attainable antitumor approach to enhance drug-induced apoptosis and improve cytotoxicity. Moreover, overexpression of the Vascular Endothelial Growth Factor (VEGF) in tumor tissues drives angiogenesis and blocks the VEGF signaling pathway, which can be diminished by RNAi to prevent tumor growth ,8. siRNAThe delivery of siRNAs is commonly performed through viral and nonviral vectors . Due to Mesoporous silica nanoparticles (MSNs) are being considered as a promising delivery vehicle for a variety of biomedical applications, owing to their high biocompatibility ,26. MSNsWe searched for articles published in the last ten years that discuss the use of MSNs as delivery vehicles for cancer treatment. Our review includes an introduction to the characteristics of MSNs and how they are efficiently loaded with siRNA for precise delivery to tumor cells. We also provide an overview of cancer-treatment strategies that utilize siRNA-loaded MSNs. In conclusion, we discuss the potential uses of siRNA-loaded MSNs in tumor diagnosis and therapy.MSNs have a unique structure that includes a silica matrix with organized pores, providing a large surface area, enormous pore volume, and variably sized pores with a narrow distribution  that allSince MSNs are one of the prominent nanocarriers for cancer therapy, the synthesis strategies of MSNs have been comprehensively reviewed . To modi2+ and PEI. The MSNs were synthesized by a traditional hydrothermal method and modified with zinc ions [2+ and the cationic nature, PEI-Zn-MSN showed a higher zeta potential and consequently a better siRNA adsorption capacity than PEI-MSN alone. Additionally, grafting a layer of polyethylene glycol (PEG) onto the surface of PEI-Zn-MSN can enhance its release profile without causing cytotoxicity.Nucleic acids, bearing negative charges from phosphate groups, cannot directly bind to bare MSNs with silicon hydroxy groups. Typically, positively charged polymers like poly-l-lysine (PLL) or polyethyleneimine (PEI) are added to MSNs to facilitate the binding of negatively charged nucleic acids via electrostatic interactions ,46. Reseinc ions . Owing tThe cationic polymer coated on the surface of MSNs by electrostatic binding comes off after i.v. administration due to competing interactions with charged macromolecules in the bodily fluid . MSNs wiCompared to other nanomaterials, MSNs have a unique pore structure that enables the loading of various cargos inside the pores . The mesNa et al. compared the application of two types of MSNs, MSN2 (pore radius = 1.05 nm) and MSN23 (pore radius = 11.5 nm) as siRNA delivery systems . ElectroThe utilization of siRNA transfection technology based on MSN-based devices has provided a new approach for the development of multifunctional delivery vectors with relative hypotoxicity and excellent efficiency. Therapeutic siRNAs can be loaded onto the surface of MSNs or inside their pores. However, it is challenging to determine which strategy between these two can provide better results for widespread applications. On the one hand, a number of antitumor applications can considerably benefit from the use of MSNs’ wide pores. For instance, employing molecules to encase and cover other functional molecules inside the pores can result in tumor-specific “on-demand” release . AdditioMSN-mediated siRNA delivery offers distinct advantages. The unique modification on the surface of MSNs allows the complete delivery system to improve target cell recognition and enhance cellular uptake. MSNs with siRNA are endocytosed into the cytoplasm via endocytosis and escape from the endosome. Then, they precisely release siRNA under conditions of the internal and external activation of tumor cells, boosting the efficacy of siRNA-targeted therapy for cancers . We concMSNs have gained widespread interest as vectors and are promising candidates for packaging siRNAs. However, achieving the controllability and accuracy of delivery remains a challenge. In the field of targeted delivery, biomarkers that are highly or specifically expressed at the target site are usually used as targets, and targeted molecules, antibodies, and polypeptides are used to achieve specific binding to these targets. According to this idea of receptor-specific binding, siRNA-loaded MSNs can directly attach to the appropriate receptor, which is highly expressed on the surface of tumor cells, when modified by ligands including the targeting peptide, antibody, aptamer, small molecule, and polysaccharide ,66,67. SIn the MSNs’ delivery system, adding targeting peptides on their surface as ligands to selectively bind them to the corresponding receptors on the cell surface is a vital approach to achieving targeted transportation. Targeting peptides are a collection of linear or cyclic amino acids that bind to particular attaching sites on transmembrane receptors to specifically anchor vectors to target cells. These are generally composed of less than 50 amino acids. Compared to proteins, peptides with smaller molecular weights have several advantages, like more stability, resistance to environmental conditions, and ease of synthesis and conjugation. Currently, three main methods for screening tumor-targeting peptides are practiced: phage display technology, chemically synthesized peptide library screening, and computer simulation design based on tumor-specific targets.Peptide-conjugated MSNs are popular for siRNA-targeting delivery. Some representative peptides are utilized for targeting tumor areas with an emphasis on cancers that are now of great interest and those that might benefit from more-effective siRNA delivery . At present, the Arg-Gly-Asp (RGD) family of peptides is widely used, and the binding site is integrin αvβ3, which is overexpressed in tumor cells and tumor neovascular endothelial cells ,69. ThusThe antibody is a glycoprotein with a high affinity to receptors and a unique specificity to recognize receptors, which has been widely evaluated as a targeting ligand. Modified antibodies can accomplish the targeted delivery of drugs by antibody–ligand or antibody–receptor binding. The basic principle of actively targeted drugs constructed based on the antibody–receptor binding strategy is that certain receptors are highly expressed in tumor tissues. After binding to the surface membrane receptors of tumor cells, antibodies cause receptor-mediated endocytosis ,73. TherA transmembrane glycoprotein with a coding molecular weight of 185 kDa, human epidermal growth factor receptor 2 (HER2), includes an intracellular tyrosine kinase catalytic region, a transmembrane region, and an extracellular ligand-binding region . When thAptamers are single-stranded DNA or RNA sequences that bind specifically to corresponding targets by forming a specific conformation . AptamerSmall molecules, i.e., molecules with a low molecular weight , have attracted research attention for cancer targeting owing to their straightforward structure and inexpensive cost. As a targeting ligand, small molecules bind specifically to the receptors overexpressed in tumors and thus have been widely studied for their application in tumor-targeted therapy. Among these, folic acid (FA) stands out as the most up-and-coming targeting molecule due to its excellent affinity to the folate receptor.Folate receptors (FRs), which are transmembrane glycoproteins linked by glycosylphospholipid acyl alcohols with a molecular weight 38–40 kDa, exist on most tumor cell surfaces and are rarely expressed in normal body cells . FRs are7 U, hyaluronic acid (HA) is a natural macromolecule acid mucopolysaccharide comprising N-acetylglucosamine and D-glucuronic acid joined by glycosidic bonds [With a molecular weight of 5000–2 × 10ic bonds . Four HAic bonds . Accordiic bonds . Through2@siRNA@DOX@HA@ peptide NPs, HACT NPs) [2 was coated with HA, creating a protective shell. The PEGA pVEC peptide, another target ligand for tumor vascular system recognition, is bound to the HA shell by an amino oxime reaction. The assembly of negatively charged peptides was shown by the significant drop in the zeta potential from −5.7 mV to −17.8 mV following the coating of the outermost peptide layer. After an intravenous injection of DDS, NP first accumulates in tumor blood vessels through the peptide’s active targeting recognition of vascular markers and is selectively ingested by CD44-rich cancer cells. Shi and coworkers successfully loaded and delivered an MTH1 inhibitor, TH287, and multidrug resistance protein 1 (MDR1) siRNA with HA-modified MSNs (HA siTMSN) [Ding et al. combined HA and the breast-tumor-cell-penetrating peptide (PEGA-pVEC) as the targeting media in the cascade vector (rmSiOACT NPs) . To targ siTMSN) . A cell  siTMSN) .When siRNA-containing MSNs are internalized by cells through endocytosis, they are transported to late endosomes followed by lysosomes, where they are degraded . Hence, According to the proton sponge hypothesis, endosomes inflate due to osmotic pressure and rupture as a result of the buffering power of polycations, releasing their contents into the cytoplasm . Some pePhotochemical internalization (PCI) is triggered by light to promote drug transport from endosomes to the cytoplasm. The photosensitizer bound to the cytoplasmic membrane enters the cell through the endocytosis pathway and then locates the membrane of the endocytosis vesicles. Under light, the photosensitizer is activated, oxygen-free radicals (ROS) are induced to generate oxygen, the membrane structure of the endosome is destroyed, and drugs are released into the cytoplasm . Zhang aWhen delivering siRNA, the main challenges are dissociating siRNA from the carrier to facilitate inclusion into RISC following cellular binding and exiting the endosomes via receptor-mediated endocytosis. Despite continuing efforts to suppress off-target effects and improve the gene-silencing efficiency, understanding the release of siRNA from vectors still possesses vital importance .Through sensitive groups  by cleavage under different conditions, the carrier and the drug are separated, and the release and uptake of the drug are accelerated. The disulfide bond possesses distinct chemical properties . It can Through the change in solubility of the dissociable groups (such as the amino and carboxyl groups) on the polymer, the stability of the carrier system reduces while drug release is promoted. A surface carboxylated MSN formed an in situ bond with a zeolite imidazole framework-8 (ZIF-8) membrane with a thickness of several nanometers and was used to block the pores of the MSN to efficiently load siRNAs for a pH-responsive drug-delivery system ,108. TheResearch on siRNA in the field of antitumor gene therapy has been increasing, making it a major focus in cancer treatment. By targeting specific genes and effectively inhibiting the expression of disease-causing genes (such as oncogenes) and/or regulating the expression of tumor-suppressor genes, it is possible to achieve the prevention and treatment of tumors. siRNA provides an efficient means to silence specific genes, and its recognition of target sequences is highly specific. By chemically synthesizing siRNA targeting different genes and transfecting them into tumor cells, tumor signal transduction can be disrupted and the relevant target genes can be modulated, thereby achieving the goal of blocking tumor occurrence and development ,110,111.Cancers can frequently develop treatment resistance because of their highly heterogeneous character . ChemothResistance to antineoplastic drugs mostly comprises pump and nonpump drug resistance. Pump resistance is caused by the ATP-binding cassette (ABC) transporters, such as P-gp, MDR-related protein, and ABC subfamily G member 2 (ABCG2), which carry drugs from cancer cells to the plasma membrane . To overIn the development of tumors, certain genes such as oncogenes and tumor-suppressor genes play a crucial role in controlling programmed cell death, also known as cellular apoptosis. When these genes are not properly regulated, they can lead to uncontrolled cell growth and prevent the normal apoptotic process, ultimately resulting in cellular malignancy and unlimited proliferation ,127,128.Bcl-2, an antiapoptotic gene closely associated with malignant tumors, can impede the apoptosis of tumor cells induced by chemotherapy drugs and is closely linked to cancer development and drug resistance . By utilThe growth of malignant solid tumors is dependent on angiogenesis, which occurs after the tumor breaches the basement membrane. This process enables the tumor to acquire a continuous supply of nutrients and oxygen, facilitating its growth and expansion. Neovascularization is therefore an important factor to consider in the treatment of tumors. The formation of new blood vessels is closely linked to tumor growth, proliferation, and metastasis. By preventing the development of tumor neovasculature and cutting off the tumor’s nutrient supply, we can more effectively inhibit tumor invasion, recurrence, and metastasis . The ampImmunotherapy approaches have gained attention in the field of cancer treatment as they have the potential to eradicate cancer cells and metastatic tumors by recruiting the host immune system. Unfortunately, not all therapeutic targets can be effectively treated with antibodies or small molecule inhibitors due to the complex mechanisms of immune evasion by tumor cells. RNAi technology, which can target different molecules, has been used to silence specific targets in tumor cells and noncancerous host cells, enhancing the immune response against tumors . Some caFerroptosis is a new form of programmed cell death that involves iron-dependent lipid peroxidation and is linked to various illnesses, such as cancer . EncouraPhotothermal therapy (PTT), an emerging cancer-treatment method, uses photothermal agents to convert absorbed NIR light into thermal energy to achieve the photothermal ablation of cells . By injeRNAi therapies work by directly introducing genes—either through regulation or replacement—into cancer cells or the tissues around a tumor. However, the vulnerability of degradation by serum nucleases, the difficulty of crossing physiological barriers, the potential off-target effect, and the possibility of activating the innate immune system has limited clinical applicability. As relatively well-studied and widely used nanomaterials, MSNs possess characteristics such as different pore sizes, good biocompatibility, and various targeting strategies, making them a suitable carrier for siRNA. The incorporation of siRNAs onto nanometer-sized MSNs has clearly advanced their biological behavior. siRNA-loaded MSNs have greatly overcome the aforementioned limitations and provided a new strategy to tackle cancer. To realize clinical functionalization, a therapeutic combination was promoted recently to extend synergistic antitumor effects by using RNAi and other adjuvants, which can induce ROS or heat species to achieve the ablation of cells. Additionally, through various forms of surface functionalization and the creation of core/shell nanomaterials, therapeutic techniques using MSN-based RNAi are being successfully integrated with various imaging modalities. We anticipate that these functional MSN systems have an excellent application potential for RNAi applications, such as achieving precise treatment and the integration of cancer diagnosis and treatment. In conclusion, the development of multifunctionally modified MSNs or their combination with other functional biomolecules has led to the pursuit of smart nanomedicine based on MSNs, and the testing of these systems in depth in vitro and in vivo has grown to be one of the most significant study fields in recent years. Future developments will see siRNA-loaded MSNs used in a wider range of drug-delivery procedures, therapy, and therapeutic evaluation.


Astrocytes are a vital cellular component of the central nervous system that impact neuronal function in both healthy and pathological states. This includes intercellular signals to neurons and non-neuronal cells during development, maturation, and aging that can modulate neural network formation, plasticity, and maintenance. Recently, human pluripotent stem cell-derived neural aggregate cultures, known as neurospheres or organoids, have emerged as improved experimental platforms for basic and pre-clinical neuroscience compared to traditional approaches. Here, we summarize the potential capability of using organoids to further understand the mechanistic role of astrocytes upon neural networks, including the production of extracellular matrix components and reactive signaling cues. Additionally, we discuss the application of organoid models to investigate the astrocyte-dependent aspects of neuropathological diseases and to test astrocyte-inspired technologies. We examine the shortcomings of organoid-based experimental platforms and plausible improvements made possible by cutting-edge neuroengineering technologies. These advancements are expected to enable the development of improved diagnostic strategies and high-throughput translational applications regarding neuroregeneration. Astrocytes are abundant cells in the central nervous system (CNS) that serve myriad functions in maintaining the general homeostasis of neurons during healthy development, maturation, and aging. For example, astrocytes directly manipulate synaptic network formation and function through various signaling molecules , remodelFor over a decade, astrocytes derived from human pluripotent stem cells (hPSCs) have been used as a cellular source to further understand basic neurobiology at the molecular and cellular levels, as well as for disease modeling, as we  and otheAstrocytes are an integral part of the CNS throughout the course of the life span. One of their important functions is producing and modulating ECM components that affect, among other things, neural activity. The ECM in the CNS can be generally subdivided into (a) basement membrane of the vasculature that plays a part in the blood brain barrier and is mainly composed of collagens, laminins, fibronectins, and heparin sulfate proteoglycans (HSPGs) among others ; (b) theAs ECM and other extracellular components derived from astrocytes play an integral role in synaptogenesis and/or function , an inteThe addition of astrocyte-derived ECM into organoids via direct inclusion of astrocytes or exogenous application is an opportunity to better understand their functional contribution. One approach is to extract native ECM through a decellularization process, producing an ECM in a hydrogel scaffold. Then, researchers can subsequently use this scaffold to determine the effect upon organoid maturation and function. Decellularization typically consists of the chemical and enzymatic removal of cellular components while retaining the ECM. Decellularized human brain ECM, containing GAGs, collagens, proteoglycans, and various glycoproteins, has been discovered to enhance neurogenesis and physiological maturation of neurons within organoids as compared to Matrigel, which is a commonly used rodent tumor-derived basement membrane composed of collagens, laminins, and proteoglycans . Similarin vitro drug analyses and high throughput studies.Astrocyte-derived, or astrocyte-inspired, ECM compositions can also lead to distinct autonomous and/or non-autonomous impacts. For example, protoplasmic astrocytes derived from mouse embryonic stem cells in monolayers produce elevated levels of permissive ECM components, such as laminin α5 and γ1, in contrast to fibrous astrocytes that produce inhibitory ECM components, such as spondin-1, that limit axonal growth capacity . PrimaryDysregulation of astrocyte ECM is also expected to be a phenotypic consequence of disease states that can subsequently impact neuronal function, similar to what we previously observed with a model of Costello syndrome that displayed a dysregulated ECM and increase in synapse density using monolayer cultures  and AsteNeural networks in the CNS are interconnective systems of neurons that integrate and process information for signal transmission. These networks are complex and diverse; their make-up consists of various neuron types with regards to function and morphology. For example, networks can be composed of different ratios of excitatory and inhibitory subtypes of neurons that secrete different types of neurotransmitters, and they can be distinct depending on the brain region . Neural in vivo conditions (Sox9) and nuclear factor 1 A (NFIA) for astrocytes. Thus, direct differentiation helped to rapidly produce postmitotic neurons and astrocytes, which were used to form all-inducible organoids  in specific cellular ratios that do not change over time . In one study, although synchronous calcium activity could not be detected in organoids after 104 days in culture, dissociation and reformation of clusters over time led to synchronized network calcium activity that was increased by applying glutamate and blocked by GABA and CNQX . HoweverBecause fluorescent calcium sensors and rigid MEAs have drawbacks , engineers have developed arrays that envelop and encase organoids throughout the entire surface of the structure. Various designs have been developed, including shell-like designs that encase organoids in a transparent sphere allowing for simultaneous microscopy  and rollIn vivo studies have discovered that prolonged electrical stimulation can promote neuronal axon growth and integration within the spinal cord to restore motor function in monkeys and rats  channels that result in hyperactivity and rescued with the BK antagonist paxillin  deletions are linked to increased risk in schizophrenia. Along with cell-specific transcriptional changes, organoids with NRXN1 deletions displayed decreased synchronized calcium burst activity at 4–5 months of culture compared to control organoids . Networkpaxillin . In cancpaxillin . AltogetCan astrocytes enhance the therapeutic ability of transplanted organoids to rescue diseased or structurally impaired neural networks? hPSC-derived dissociated astrocytes have been engrafted into animal models by our lab  and otheBesides direct astrocyte-neuron interactions, there are multiple other cell types that may directly or indirectly modify this relationship. Here, we will focus on the role of microglia as they have been the subject of numerous neural organoid studies. Astrocytes and microglia remain in constant communication and coordinate their functions with crosstalk and feedback signaling cycles . In brieOne of the main roadblocks to systematic inclusion of microglia, and multiple additional cell types, within organoids is the lack of standardization in neural organoid production. Various adjustments to protocols have been implicated to increase reproducibility and complexity of the resultant cellular population . To avoiin vivo also occurs between hPSC-derived microglia and astrocytes within organoids remains to be rigorously explored. In monolayer cultures, tri-cultures of neurons, astrocytes, and microglia have been used to model Alzheimer’s disease (in vitro that activate the CSF-1 receptor, vascularization of organoids, and appearance of mature astrocytes over time (Whether the same intercellular inflammatory signaling observed  disease . Thus, iver time . MicroglHere, we have summarized major astrocyte-related components and technological innovations that we expect would either enhance or disrupt organoid function as defined by various experimental readouts . We furtSL: Writing – original draft, Writing – review & editing. MP: Writing – original draft, Writing – review & editing. MM-P: Writing – original draft, Writing – review & editing. RK: Writing – original draft, Writing – review & editing.


The COVID-19 pandemic has disproportionately affected different social and demographic groups, deepening the negative health implications of social and economic inequalities and highlighting the importance of social determinants of health. Despite a deep literature on pandemic-related disparities, specifically regarding social determinants and health outcomes, the influence of the accessibility of financial services on health outcomes during COVID-19 remains largely unexplored. Modeling (pre-omicron) COVID-19 mortality across 142 nations, we assess the impact of national-level usage and access to formal financial services. Two financial access indexes constructed through principal component analysis capture (1) usage of and access to formal financial tools and (2) reliance on alternative and informal financial tools. On average, nations with higher pre-pandemic use of and access to formal financial services had substantially lower population mortality risk from COVID-19, controlling for key population health, demographic, and socioeconomic covariates. The scale of effect is similar in magnitude—but opposite in direction—to major risk factors identified in previous literature, such as lung cancer, hypertension, and income inequality. Findings suggest that financial services deserve greater attention both in the public health literature related to COVID-19 and more broadly in policy discussions about fostering better public health overall. The COVID-19 pandemic has both highlighted the importance of social determinants of health and exacerbated the negative health implications of social and economic inequalities. There is broad literature and evidence that inequalities in income, access to resources, and other measures of household financial security affect household members’ health . SpecifiBefore COVID-19 hit, many developing economies had seen expansions in financial access such as banking deposit and credit services, digital payment systems, and other inclusive fintech innovations. For decades, advocates for global-scale investments in inclusive financial services have championed the role that improved access to financial services plays in helping families in developing economies, particularly those reliant on income from informal work, buffer the risks to their livelihoods and health they face from highly variable and unstable income flows –11. Job Yet it remains an open question whether investments in inclusive financial services and inclusive fintech have mattered for global health. Because health is such a complex socio-economic phenomenon, and financial access is only one (perhaps minor) element among many confounding factors, teasing out the health effects of expanding financial services has proven remarkably challenging. The unfortunately stark and broad natural experiment of COVID-19 might have strengthened the signal-to-noise ratio enough to present a unique opportunity to deepen understanding of the role of access to financial services in health outcomes.Most of the literature to date linking COVID-19 and financial services has focused on how the pandemic has challenged the financial security of financial sector institutions e.g., –14), of , of 14])But causality should run the other direction as well if financial security is beneficial to health. Advocates for inclusive finance might have hoped that financial access would have some impact on risk mitigation during the pandemic. Did it? If advocates have been correct, families, communities, and nations that had better financial access pre-COVID-19 should have been better able to weather the health storm in measurable ways during the pandemic. Somewhat surprisingly, there so far has been no global study exploring how the pandemic’s effects correlate with financial access metrics and whether there is any evidence of risk mitigation.The analysis that follows models COVID-19 mortality rates across 142 nations and finds that nations where residents had greater (pre-pandemic) access to an array of formal financial services had substantially lower population mortality risk during the pandemic. Indeed, the risk reduction is surprisingly large, similar in magnitude to but opposite in direction from the increased COVID-19 mortality risks associated with higher rates of lung cancer, hypertension, and greater income inequality.Population-level COVID-19 mortality risks are, by now, well understood in the medical literature to be related to a variety of population-level risk factors. Though our main interest here is not these previously studied population health and socioeconomic variables, we include some key national-level public health risk characteristics as control variables. These control variables were selected based on previous evidence in the literature as covariates with COVID-19 mortality, including population health and respiratory issues ; healthcare system availability and effectiveness; and socio-economic characteristics, including age distribution, urbanization, population density, and socioeconomic inequalities e.g., –43). To . To 43])In addition, we also screened for data availability. Much of the statistical modeling in the national-level literature suffers from limited data availability across nations, restricting sample size. Aiming for the broadest and most representative set of countries, we selected publicly available variables at the national level that had no more than five nations missing from the 142 nations for which financial inclusion variables and COVID-19 mortality rates were also available. To assess the extent to which access to financial services has contributed to mitigating COVID-19 risks, we develop financial inclusion indices at the national level using metrics from the World Bank’s Global Findex Database . The GloWe construct our financial access indexes from principal component analysis (PCA) on 20 metrics drawn from the Global Findex Database 2017 panel, the most recent available pre-pandemic. The 2017 panel includes indicators on the access and use of both formal and informal financial services as well as the use of various financial technologies. As described in To reduce the dimensionality of our exploration of whether countries with greater access to financial services have better success mitigating COVID-19 risks while retaining most of the variability of the underlying metrics, we apply PCA. Our PCA incorporates all 20 variables in By contrast, the second index associates most closely with metrics related to alternative financial tools, such as mobile phone transactions and informal finance of borrowing from family and friends, and distress financing tools, such as selling assets for emergency uses and borrowing for health and medical needs. It is also negatively associated with the ability to raise emergency funds and access to family funding for emergencies, suggesting that high values of the second index associate, in part, with financial resilience challenges. We therefore interpret the second component as an index capturing a nation’s tendency toward “Reliance on alternative, informal, and distress financial tools.” Descriptive statistics on two financial tools indexes created from these two principal components are shown in The correlation matrix in All data used in the study is publicly available secondary data, not collected by the authors. No permissions for data use were required. Protocols for data collection, ethics, and participants’ rights protection vary by source organization, as listed in As the slight differences among variable observation counts in The first financial access index, capturing access to formal financial services, is statistically significantly related to COVID-19 mortality rates (p < .001), controlling for demographic, socioeconomic, population health and health infrastructure variables, and region. In As additional validity checks on the stability of estimates of our main financial access index variables of interest, To test the internal validity of model specification, a Ramsey RESET test and a LINK test are conducted after each model (except the FIML models) to test functional form and omitted variable problems. Wald tests on variables added beyond the two variables in Model 5  are also shown. The Ramsey, Link, and Wald test results appear in the bottom rows of By contrast, the results for the second financial services index, reliance on alternative, informal, and distress financing, are weaker in scale and statistically less robust. In While the control variables—drawn from the medical and public health literatures—are not our central interest, we now turn to those population health, demographic, and socioeconomic variables. Youthful populations are associated with statistically significantly lower mortality, as are more-equal income distributions. Consistent with existing literature e.g., –56), nat, nat56])Given that COVID-19 attacks the respiratory-system , 36, 43,We also find that nations with higher per capita incomes had statistically significantly higher mortality rates, even after controlling for characteristics associated with wealth, like aging and obesity, that might be problematic for richer nations. Though curious, this finding is consistent with most of the COVID-19 literature. For example, Goldberg and Reed  found, aThough not tested or of central interest here, numerous speculations—with often conflicting empirical results—exist in that literature about why wealthier nations, which also tend to be those with deeper financial services, have had substantially higher average levels of COVID-19 infection and mortality. Possible explanations include, for example: measurement issues such as less-robust public health data reporting resulting in undercounting by lower-income nations, or political and cultural differences in reporting transparency; political economic differences in policy responses; socio-economic differences like greater population mobility in wealthier nations either before or after lockdowns, very high population densities in the very largest urban centers in wealthier nations, previous experience in lower-income nations with handling and/or accumulated immunities from similar viral  outbreaks. or more-concentrated systems of elder care; and even seasonal differences in northern and southern hemispheres; or a host of other potential reasons. Clarifying what drives the counterintuitive wealth-COVID-19 correlation has so far been frustratingly difficult and remains a work in process and beyond the scope of this article.Similarly counterintuitive, nations with more effective healthcare systems, as measured by the universal healthcare effective coverage index, have significantly higher COVID-19 mortality. The size of the relationship in our models is similar to that of lung cancer relation: a one standard deviation increase in the UHC index goes with an approximate two-thirds increase in mortality. Greater population shares of nurses and midwifery personnel are also positively related to mortality, however not statistically significantly in most models. The coefficients on diabetes hint at another puzzle. In models with regional dummies, diabetes prevalence associates negatively with COVID-19 mortality—not the direction we would expect based on the medical literature , 42. HowAn attempt to explore the effective healthcare system puzzle a bit, specifically whether the interaction between deeper financial systems and stronger healthcare systems might help untangle it, is shown in Finally, although the independent variables included in Models 1–10 reflect national conditions in 2019 or earlier, prior to the arrival of COVID-19, in In summary, we find that greater pre-pandemic national levels of use and access to formal financial services are related to substantially lower death rates from (pre-omicron) COVID-19. The result suggests that financial services deserve substantially greater attention both in the public health literature related to COVID-19 and more broadly in policy discussions about fostering better public health overall. Robust financial services like savings, insurance, credit, payment systems, and the like are clearly potentially useful tools for households paying for food, housing, medicines, and health services and dealing with medical emergencies. Yet despite the extensive literature linking household financial security and health and the obvious potential link to COVID-19 outcomes, we are unaware of any other study exploring how COVID-19 mortality relates to financial systems.To help assess the relative importance of the financial access variable, Though the GDP-COVID-19 puzzle is not our central interest, our finding that deeper formal financial systems appear associated with better risk mitigation if anything deepens the perplexity. It is puzzling too that not only the income variable but also both health infrastructure variables relate positively to COVID-19 mortality. All three behave oppositely to our formal financial access metric, despite the clear relationship between national wealth and the depths of both financial systems and health systems. Some analysts have speculated the positive association of national income and COVID-19 might be an artifact of nations with deeper healthcare systems better tracking health statistics . Yet conUntangling the complex socio-economic and socio-structural determinants of health inequalities remains a major challenge. The role of financial tools in health should feature centrally in that inquiry.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S1 Fig(TIFF)Click here for additional data file.S2 Fig(TIFF)Click here for additional data file.


Neural stimulation presents a promising avenue for treating neurological disorders, offering an alternative or complement to the traditional pharmaceutical approaches. By delivering targeted stimulation specifically to the brain region that is causing the pathology, the potential for a wide range of adverse effects from drug treatment may be reduced. However, some conventional brain stimulation methods, such as deep brain stimulation (DBS), involve high-cost, high-risk surgeries to implant electrodes into the brain and a battery to the chest, with a wire connecting them to deliver electrical stimulation.In this Research Topic, we asked the question: can we utilize the existing “wires” -peripheral nerves- in the body for stimulating the brain instead? Specifically, the overlapping structures between peripheral pathways  and the central circuits of the brain  may enable such utilization. The articles in this collection aim to illuminate some of the possibilities of peripheral stimulation as a non-invasive neuromodulation method for the central nervous system (CNS).Li et al. discuss how acupuncture, a traditional form of peripheral nerve stimulation used for thousands of years, can regulate the autonomic nervous system (ANS) to treat disorders such as migraine, pain, and inflammation. This review article highlights that acupuncture activates sensory nerves and spinal pathways to modulate a complex network of autonomic nuclei within the brain, such as the hypothalamus, brainstem nuclei, and cortical regions. This, in turn, regulates sympathetic, parasympathetic, and enteric efferents to restore homeostasis and alleviate symptoms. Further study on the specific neural pathways and mechanisms involved for different acupoints, stimulation parameters, and conditions could help optimize the clinical outcomes. Overall, the modulation of ANS represents an important link between acupuncture and its therapeutic effects.Advancements in various techniques, such as electrical, vibrotactile, and audio/visual stimulation, have piqued interest in their potential applications for treating neurological and autonomic disease indications. However, sometimes to go forward, one must look back. Garcia et al. use electrical stimulation to do so. This article investigates the effects of different respiratory-gated auricular vagus afferent nerve stimulation (RAVANS) frequencies on blood pressure and heart rate variability in hypertensive patients. The RAVANS system stimulates the auricular branch of the vagus nerve in the ear only during the exhalation phase of the breathing cycle. RAVANS shows frequency-dependent effects, with 100 Hz stimulation significantly lowering heart rate and blood pressure compared to sham, especially in some subgroups. This non-invasive approach likely acts on the medullary vagal complex to increase parasympathetic activity and/or reduce sympathetic drive. The findings demonstrate that peripheral stimulation can modulate central circuits to treat disorders like hypertension and that the stimulation parameters need to be optimized to achieve the greatest effects on the modulation of physiological and clinical outcomes.Acupuncture is not the only way peripheral stimulation can modulate the ANS though, as Koseki et al. investigate the combined effects of neuromuscular electrical stimulation (NMES) and transcutaneous spinal direct current stimulation (tsDCS) on the neural plasticity of the CNS in healthy participants. NMES activates peripheral sensory nerves while tsDCS modulates spinal cord excitability, and their combination induces longer-lasting effects compared to either stimulus alone. Results showed an increased corticospinal excitability for at least 60 min, reduced intracortical inhibition, and decreased spinal reflex excitability immediately after stimulation. The findings suggest that the enhanced motor cortex plasticity by combined stimulation could help develop new neurorehabilitation techniques. However, further research is needed in patients with neurological conditions such as those with CNS lesions.Peripheral nerve stimulation is not always a replacement to direct brain stimulation but can also be complementary. Fuksa et al. use audio/visual stimuli with various complexities to examine functional changes in the brain related to age, hearing loss (presbycusis), and tinnitus. The authors utilized functional magnetic resonance imaging (fMRI) to investigate brain activity during different types of acoustic and visual stimulations, and their combinations. The results demonstrate that the aforementioned pathologies as well as the stimulus complexity influence lateralization and activation patterns in the limbic system and the auditory cortex. The authors conclude by recommending types of audiovisual stimulation better suited to identifying between presbycusis and tinnitus, alluding to the diagnostic use of peripheral stimulation.Aside from its potential for novel therapeutic applications, peripheral stimulation can be utilized for diagnostic purposes as well. Suk et al. take peripheral stimulation to another level by suggesting a whole-body stimulation paradigm. The authors have previously reported the benefits of non-invasive visual stimulation using 40 Hz light-flicker and were curious if their findings were modality dependent. In the current article, similar to the visual stimuli, they observe that non-invasive whole-body vibrotactile stimulation at 40 Hz improves motor performance, induces neural activity, and reduces pathology in the somatosensory and motor cortices of mouse models of neurodegeneration. The findings demonstrate that non-invasive sensory stimulation at 40 Hz, engaging multiple modalities like vision and touch, represents a promising therapeutic strategy for mitigating pathology and improving performance in neurodegenerative diseases. While the study focuses on animal models, it implies the potential for similar strategies in human patients, although additional research is needed to establish the clinical applicability and efficacy of the approach.Wang et al. explore the use of a regenerative peripheral nerve interface (RPNI) as a neuroprotective tool for injured nerves. RPNI involves implanting the proximal nerve stump into a free muscle graft, promoting synaptogenesis and sensory receptor reinnervation. The article demonstrates that RPNI rescues sensory neuron loss and apoptosis in dorsal root ganglia (DRG) neurons after sciatic axotomy in adult rats. They conclude that RPNI helps prevent neuronal loss after nerve injury, likely by re-establishing retrograde transport of neurotrophins from the reinnervated muscle back to the DRG. This provides a potential therapy to improve outcomes of delayed peripheral nerve repair by protecting injured sensory neurons from retrograde degeneration.No matter what modality is used for peripheral stimulation, it will be more effective if the peripheral nerves are healthy. To conclude, we believe there is a significant untapped potential in neuromodulation through peripheral nerves. As the compendium of work presented in our Research Topic demonstrates, such therapies can derive inspiration from both novel technologies, such as regenerative nerve interfaces, and ancient traditional practices, such as acupuncture. We are particularly pleased that the articles in this Research Topic cover different sensory modalities, such as electrical, somatosensory, and audiovisual. Especially with the developments in non-invasive approaches such as transcranial magnetic stimulation (TMS), electro- and magneto-encephalography (EEG/EMG), the underlying mechanisms and pathways behind various peripheral stimulation approaches can be explored without surgical experiments. We look forward to ongoing interest in developing peripheral stimulation further, both with existing and new modalities, and providing a less-invasive alternative for the drug-resistant patient populations suffering from neurological disorders.MO: Writing—original draft, Writing—review and editing. JH-P: Writing—review and editing. AK: Writing—review and editing. JP: Writing—review and editing. IT: Writing—review and editing. AT: Writing—review and editing.


Cancer diagnosis and treatment constitute profoundly stressful experiences involving unique and common challenges that generate uncertainty, fear, and emotional distress. Individuals with cancer must cope with multiple stressors, from the point of diagnosis through surgical and adjuvant treatments and into survivorship, that require substantial psychological and physiological adaptation. This can take a toll on quality of life and well-being and may also promote cellular and molecular changes that can exacerbate physical symptoms and facilitate tumor growth and metastasis, thereby contributing to negative long-term health outcomes. Since modifying responses to stressors might improve psychological and physiological adaptation, quality of life, and clinical health outcomes, several randomized controlled trials have tested interventions that aim to facilitate stress management. We review evidence for the effects of stress management interventions on psychological and physiological adaptation and health outcomes in cancer patients and survivors and summarize emerging research in the field to address unanswered questions. Receiving a cancer diagnosis is a profoundly stressful experience involving unique and common challenges that often introduces uncertainty, fear, and emotional distress . Patient1 Primary treatment refers to the first or main treatment used to eliminate or reduce traceable cancer. Adjuvant therapy is additional treatment given after primary treatment to eliminate any remaining cancer cells using either systemic or nonsystemic therapies. Neoadjuvant therapy is treatment that occurs prior to initial treatment  to facilitate the primary treatment or make it more effective. Palliative treatment or treatment with palliative intent is the use of any therapy with the goal of improving quality of life  and reducing the physical burden of cancer by relieving treatment side effects or symptoms related to the cancer itself. Palliative treatment can be applied to patients with any stage of cancer experiencing symptom burden; however, it is often the focal treatment for those with metastatic cancer that has spread to other parts of the body from where it originated.Cancer treatments can be classified into different categories based on the goal or intention of the treatment.An individual’s response to treatment with respect to both its effectiveness and its side effects is highly variable. Symptomatic side effects or toxicities, such as pain, nausea, fatigue, and neuropathy, can significantly reduce the tolerability of cancer treatments (a patient’s capacity to adhere to therapy) and therefore have a negative impact on QoL and psychosocial adjustment . Cancer Advances in cancer prevention, screening, and treatment have led to a significant increase in the number of individuals who live beyond a cancer diagnosis and the completion of curative-intent treatment . AlthougIn contrast to treatment with curative intent, treatment for incurable advanced or metastatic cancer is often not circumscribed to a definitive period and does not include a post–treatment completion phase as with curative-intent treatment . FurtherGiven the stressors and challenges associated with diagnosis and treatment, it is not surprising that cancer can take a significant toll on emotional well-being and require sustained psychological adaptation. Anxiety and depressed mood are two of the most common emotional reactions among individuals who are undergoing cancer treatment  or have Despite the considerable impact of cancer on psychological adaptation, individuals often demonstrate resilience. Facing cancer can lead to opportunities for positive change as individuals engage in efforts to find meaning in their experience . PositivThe multiple stressors that cancer patients must manage, from the point of diagnosis and treatment decisions, through surgical and adjuvant treatments, and into survivorship, require a substantial amount of emotional processing and adaptation, both psychologically and physiologically. The physiological basis of adapting to stressors was initially articulated by Hans Seyle in his notion of general adaptation syndrome . We knowOne physiological system often implicated in responses to stressors is the sympathetic nervous system (SNS), which mediates neural signaling directly from brain regions such as the locus coereleus (LC) to peripheral tissue via the sympathetic chain of neural fibers. Sympathetic neurons release norepinephrine (NE) at junctures with many vital organs, including the heart, as well as with immune-related lymphoid organs such as the lymph nodes, bone marrow, and gut-associated lymphoid tissue (GALT), which produce or house different types of immune cells that bear beta-adrenergic receptors (β-ARs) on their cell membranes . We now Another physiological stress response system, the conservation-withdrawal response, is often associated with persisting, repeated, uncontrollable, and unpredictable stressors as well as distress states such as depression and threat/anxiety and is characterized by activation of and dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis. Following the perception of uncertainty from cortical regions of the central nervous system, the hypothalamus produces corticotrophin releasing hormone (CRH), which in turn signals the pituitary to release adrenocorticotrophic hormone (ACTH) into the circulation, which signals the adrenal cortex to produce the glucocorticoid cortisol . This syMany reviews focus on the influence of neuroendocrines on the immune system as a plausible explanation for stress effects on negative health outcomes in cancer patients . These iOther reviews of the field refer to a neurobiology of cancer articulating the links among psychosocial factors, the nervous system, and tumor tissue . The proThe nervous system is believed to influence cancer initiation and progression via DNA mutations and oncogene signaling . The nerRecent work suggests that stress-related neuroendocrines may even contribute to reactivating dormant cancer cells. For instance, stress hormones activating β-ARs can stimulate release of s100A8/A9 ligands from neutrophils, which promotes activation of the receptor for advanced glycation end (RAGE) products, causing release of oxidized lipids that activate dormant cancer cells via a fibroblast growth factor pathway . InteresPreclinical animal models have shown that a variety of experimental stressors   can incrIL1A, IL1B, IL6, and TNFA gene expression as well as increased expression of several chemokine, COX2 , and prometastatic  genes (Much work in the past 30 years has related stress processes to changes in immune system activity and regulation in cancer patients . Much of9) genes .With growing interest in the effects of stress factors on transcriptional (gene expression) changes in cancer and immune cells, molecular work has related stress-related variables to a comprehensive gene expression profile termed the conserved transcriptional response to adversity (CTRA) . The CTRThere is growing evidence that adverse psychosocial factors  are associated with shorter survival time for a wide number of different cancers . For insSince modifying responses to stressors might improve adaptation to cancer, QoL, and health outcomes, several randomized controlled trials (RCTs) have tested interventions that can be considered SMIs in the past 50 years . Some SMIt is noteworthy that other psychosocial/behavioral interventions that are not necessarily SMIs have shown efficacy in cancer populations. These include supportive expressive therapy (SET)  targetinCBT refers to a class of interventions that share three fundamental principles: Cognitions affect behavior, cognitive patterns can be monitored and changed, and desired change in behavior can be achieved through change in cognitions . SpecifiResearch examining the effects of CBT on individuals affected by cancer has flourished. Meta-analytic evidence demonstrates that CBT interventions support psychological adaptation by reducing anxiety, depressive symptoms, and both general and cancer-specific emotional distress as well as by improving emotional well-being and overall QoL in both individuals diagnosed with cancer and their caregivers . CBT intNevertheless, research on the effects of CBT includes mixed findings. Observed effect sizes range from small to large , and theStudies of CBT interventions in the cancer context have largely focused on women with early-stage breast cancer . ImportaCognitive behavioral stress management (CBSM) is a 10-week CBT-based SMI that incorporates cognitive, behavioral, and interpersonal skills training and relaxation training through in-session didactic and role-playing activities as well as homework and daily practice to help improve QoL and reduce symptoms . This pra) modulate psychological adaptation indicators  in cancer patients in tandem with (b) changes in neuroendocrine indicators , (c) immune measures , and (d) long-term effects on QoL and physical health status   . Such stThe RCTs showing neuroendocrine and/or immune system effects of SMIs on cancer patients have been limited to trials enrolling nonmetastatic cancer patients, including patients with breast cancer and malignant melanoma, at the early stages of diseases. Generally, studies showing intervention effects on physiological adaptation in cancer patients also showed parallel effects on psychological adaptation . An earlA 12-month group-based SMI that included relaxation and CBT-based stress reduction exercises, coping skills training, and health education in 14 weekly sessions and 8 monthly maintenance sessions decreased distress and negative health behaviors (eating high-fat foods and tobacco smoking) over the initial 4 months, and it increased cellular immunity [lymphocyte proliferative response (LPR)] compared to treatment as usual in stage 2–3 breast cancer patients recruited in the period after surgery .Similarly, a 10-week group CBSM intervention  was testIL1B, IL6, TNF), of inflammatory chemokines and their receptors (COX2/PGS2), and of mediators of tissue remodeling and EMT , together with upregulation of 29 genes related to cellular immune responding   . Over 50duction) . Those iduction) . This prduction) , which cduction) . A reanaduction) . This paThe literature documenting the effects of cognitive-based SMIs on physical health outcomes ranges from studies showing relatively short-term effects on physical symptoms during and after cancer treatment to those showing longer-term effects on clinical disease endpoints, such as disease-free interval (time till recurrence) and cancer-specific and overall survival. Among studies of physical symptoms during cancer treatment, some CBT-based SMIs have been associated with reductions in sleep disruption , fatigueMen treated for prostate cancer report significant physical side effects, including sexual and urinary dysfunction, fatigue, and pain, along with anxiety and depressive symptoms, decreased QoL, and threats to masculine identity . Among mThe question of whether psychosocial interventions can improve long-term clinical outcomes in cancer patients has been of longstanding interest and a source of controversy in the field since the report by In one of the first trials to report the effects of a CBT-based SMI on psychological, biological, and health outcome parameters, Fawzy and colleagues observed among 66 patients with malignant melanoma that those assigned to a 6-week group intervention showed improved coping and mood , increasLooking across the entire psycho-oncology literature, several reviews reported primary or secondary analyses of RCTs for which follow-up data on clinical endpoints were available for periods ranging from 1 to 15 years. Efforts to summarize this literature have appeared in multiple qualitative and quantitative reviews  in the past 10 years . A meta-One recent review focused on 22 studies reporting long-term effects of what were referred to as stress-reducing interventions among patients with nonmetastatic breast cancer (N = 5), metastatic breast cancer (N = 7), malignant melanoma (N = 2), and several other cancer types (N = 8) including lymphoma; esophageal, lung, and gastrointestinal cancer; and samples of mixed cancer types . These tp < 0.002] (p < 0.005) and in older (≥60 years) versus younger (<50 years) patients (p < 0.01) (p = 0.01 for difference). In fact, the two studies showing the largest effects on survival were both group CBT-based SMIs in nonmetastatic breast cancer patients followed over an 11-year median. A more selective meta-analysis included 12 trials  drawn from the Cochrane Central Registry of Controlled Trials, Ovid MEDLINE, PubMed, PsycINFO, and prior meta-analyses for the period 1970–2017. Included trials met several a-priori selection criteria  . The met< 0.002] . Among a < 0.01) . Of inteWe now focus on the two trials showing CBT-based SMI effects on psychological adaptation, physiological adaptation, and long-term clinical health outcomes within the same trial, and we examine the evidence showing that changes in physiological adaptation predict or mediate the magnitude of the interventions’ effects on clinical outcomes measured at up to 15 years into survivorship. This affords the opportunity to study the interventions’ effects not only on overall and cancer-specific survival but also on disease-free interval—that is, the period of time patients remain free of a recurrence of primary disease or a secondary cancer.Another RCT reported CBT-based SMI effects on psychological adaptation , physiolIt is plausible that adjuvant therapy receipt increases CTRA signaling and that this is compounded by chronic stress during initial treatment. If so, CBSM may mitigate the contribution of chronic stress to CTRA, and maintaining lower CTRA over this cancer treatment period may decrease the odds of pro-metastatic signaling and thereby improve long-term DFS . This exWhich aspects of these two comprehensive SMI trials of breast cancer patients can provide guideposts for future mechanistic work in the field? Although conducted by different laboratories, the two CBT-based SMI RCTs in nonmetastatic breast cancer patients reviewed here are similar in terms of sample size (N = 227 and N = 240), timing (>2 weeks post-surgery and prior to starting adjuvant therapy), intervention format (group), theoretical orientation (CBT-based SMI), frequency , and follow-up intervals for main outcomes . A deeper look at each trial reveals that reported distress and/or cortisol decreases were related to either increased frequency of relaxation practice or increased confidence in using SMI skills such as relaxation and cognitive restructuring . AlthougDifferences in the two trials include a longer period of continuous intervention . CAA is defined as the combined effects of cancer diagnosis, treatment, and their sequelae on the physiological aging process . Two areConditions associated with metabolic syndrome —which represent major CVD risk factors—are often elevated in cancer survivors . The AmeA fourth area involves extending the reach of SMIs to the underserved through the use of culturally adapted interventions  and remoAttending in-person SMIs may present challenges to specific cancer populations who navigate health care appointments while maintaining employment and child care responsibilities, to those hesitant to attend structured groups in institutional settings due to medical mistrust, or to patients isolated for infection control following procedures such as hematological stem cell transplant. All of these challenges may be exacerbated in patients with low-income jobs and who lack the financial resources for child care and time off work. Technological innovations make it now possible to offer interventions remotely over digital platforms and embedded within health systems and patient portals . RecentlCancer patients and survivors have been disproportionately and significantly impacted by the COVID-19 pandemic. Emerging studies have documented that the COVID-19 pandemic has further exacerbated common challenges to psychosocial adaptation following a cancer diagnosis and treatment. In the absence of major contextual stressors, survivors experience relatively high rates of psychosocial  and physical  concerns due to cancer and its treatment. Furthermore, COVID-19 mitigation strategies leading to social isolation from friends and family, limited leisure activities, financial strain, and fears and concerns over the physical health status of an already compromised population synergistically contribute to greater levels of psychosocial distress in cancer survivors . COVID-1Multiple challenges remain in SMI research in cancer populations. Importantly, progress needs to be made in developing systems, methods, and incentives to identify and engage underrepresented populations, many of whom face extensive barriers to accessing health care, let alone research trials.These include individuals who are medically vulnerable, such as those who are older,are obese, or have significant comorbidities as well as individuals who are minoritized because of race, ethnicity, sexual orientation, gender identity, and so on. As described above, technology and cultural adaptation are two possible approaches to improving the reach of SMIs to diverse populations. Relatedly, more research is needed to optimize not only the timing of intervention  but also the length, frequency, and delivery of intervention contact. Best practices for recruitment and retention of cancer patients and survivors in SMI research are also largely understudied. Importantly, innovative trial designs, such as just-in-time adaptive intervention (JITAI), multiphase optimization strategy (MOST), and sequential multiple assignment randomized trial (SMART) , as wellWith growing evidence for the efficacy of SMIs in cancer patients, future research questions will need to ask which, when, where, and for whom these interventions might be used optimally in clinical oncology settings . The “whThe “when” question asks at what point in the post-diagnosis cancer continuum should one intervene with SMIs. While there is growing evidence that these interventions can create changes in stress-related biobehavioral processes for periods up to 12 months in patients with early-stage nonmetastatic disease, it remains to be determined whether they are able to modulate these biobehavioral processes in patients with advanced cancers. Similarly, there are two SMI trials that have shown effects on long-term recurrence and survival in early-stage patients receiving intervention in the postsurgical period . Given tThe “where” question concerns the setting for delivery of SMIs and it requires us to consider a variety of implementation issues. As we have noted, extended interventions requiring weekly group attendance over several months may not be practical within the context of primary oncology treatment, and ongoing trials are testing briefer forms and remote delivery platforms (using tablets and broadband connection) to determine if they show comparable effects to their longer and in-person versions. More work should also be conducted testing the effects of embedding SMIs (in-person or remotely delivered) into adjuvant therapy settings such as preparation for radiation treatment (simulation visits) and the chemotherapy infusion suite, where patients are attending multiple treatment sessions and are often isolated from their family and social support system .In terms of the “for whom” question, we need more information on which subgroups of patients are likely to benefit the most from SMIs. There is good evidence that patients with greater cancer-specific distress , pessimi