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This study aimed to validate the accuracy of identifying Japanese hardwood species from microscopic cross-sectional images using convolutional neural networks (CNN). The overarching goal is to create a versatile model that can handle microscopic cross-sectional images of wood. To gauge the practical accuracy, a comprehensive database of microscopic images of Japanese hardwood species was provided by the Forest Research and Management Organization. These images, captured from various positions on wood blocks, different trees, and diverse production areas, resulted in substantial intra-species image variation. To assess the effect of data distribution on accuracy, two datasets, D1 and D2, representing a segregated and a non-segregated dataset, respectively--from 1,000 images (20 images from each of the 50 species) were compiled. For D1, distinct images were allocated to the training, validation, and testing sets. However, in D2, the same images were used for both training and testing. Furthermore, the influence of the evaluation methodology on the identification accuracy was investigated by comparing two approaches: patch evaluation and E2 image evaluation. The accuracy of the model for uniformly sized images was approximately 90%, whereas that for variably sized images it was approximately 70%. [ABSTRACT FROM AUTHOR]

High-yield and superior-quality wheat varieties can greatly contribute to the escalating demand for food security. Artificial intelligence (AI) can be integrated into efficient breeding programs. AI-driven information exchange platforms can also facilitate the collection, analysis, and dissemination of breeding data in the genetic improvement of breeding. However, such platforms have hinged on the accurate identification and classification of core data. There are often some challenges, due to the complex and diverse nature of breeding information. In this study, a Naive Bayes-AdaBoost strategy was introduced to construct the wheat breeding interaction system. Naive Bayes, a probabilistic machine learning served as the foundation. The AdaBoost was then integrated to enhance the classification performance of the datasets. AdaBoost functions as a meta-learning iteratively boosted the performance of weak classifiers (in this case, Naive Bayes), where the misclassified data points were focused. As such, the strong classifier significantly improved the accuracy. Additionally, the vocabulary optimization was incorporated. The core vocabulary was then filtered and refined for the data classification. The noise and irrelevant information were reduced to further enhance the recognition accuracy. The efficacy of the Naive Bayes-AdaBoost strategy was evaluated to compare with traditional machine learning, including standalone Naive Bayes, decision tree, and support vector machine. The results demonstrated that the superior performance of the strategy was achieved in a remarkable 99.2% accuracy, in order to classify and recognize the wheat breeding information. There was a substantial 12.2 percentage point improvement over the conventional Naive Bayes. The highly accurate data classification and recognition were realized to construct efficient and reliable wheat breeding information platforms. The valuable tools were potential benefit to the data-driven decision-making in the development of superior wheat varieties for global food security. Meanwhile, a real-world case study was conducted to verify the practical applications of this strategy in a wheat breeding project. New wheat varieties were developed with enhanced disease resistance and yield potential. A dataset was also collected with the genotypic, phenotypic, environmental, and historical breeding records. The naive Bayes-AdaBoost strategy was efficiently classified to reveal the key genes and genomic regions with the targeted traits. The optimal parent selection and breeding strategies promoted the superior wheat varieties. Furthermore, user-friendly software was developed to integrate the strategy into an intuitive interface. The breeding data was easily uploaded, managed, and analyzed without specialized knowledge in machine learning. Data visualization and analysis features were facilitated to identify the potential breeding targets for the better optimization of breeding strategies. The strategy performed better than the immediate application in wheat breeding, indicating the potential applicability to the classification of complex data. For example, the accurate identification and classification of plant diseases were selected for effective disease management. The naive Bayes-AdaBoost strategy was employed to analyze the images of diseased plants in the automated diagnosis of diseases using visual symptoms. Similarly, the improved strategy can be utilized to classify the soil types in satellite imagery for crop health monitoring and resource allocation using real-time data analysis in precision agriculture. In conclusion, the Naive Bayes-AdaBoost strategy can provide a robust solution to the efficient classification and recognition of wheat breeding information. The high accuracy, adaptability, and user-friendliness can be expected to serve as the data-driven breeding programs. Moreover, the versatility can also be extended to diverse domains with complex data classification. [ABSTRACT FROM AUTHOR]

We investigate postings on two unique online interactive platforms by investors around earnings announcements of publicly listed firms in China. We find posting volumes on the platforms increase around earnings announcement dates, suggesting that investors acquire firm-specific information via the interactive platforms. This relation is stronger for firms with higher reply rates or more timely replies, for firms with fewer shares held by institutional investors, for firms with larger information asymmetry, and when market uncertainty is higher. Furthermore, the positive association between posting volumes and earnings announcements is more pronounced for firms with smaller Baidu search volumes. This suggests a substitution relationship in collecting information between asking listed firms questions directly and searching online. Finally, our evidence suggests that questions posted around earnings announcements accelerate the price discovery of earnings and attenuate post-earnings announcement drift and stock price synchronicity. [ABSTRACT FROM AUTHOR]

Alzheimer’s disease (AD) is a complex degenerative disease of the central nervous system, and elucidating its pathogenesis remains challenging. In this study, we used the inverse-variance weighted (IVW) model as the major analysis method to perform hypothesis-free Mendelian randomization (MR) analysis on the data from MRC IEU OpenGWAS (18,097 exposure traits and 16 AD outcome traits), and conducted sensitivity analysis with six models, to assess the robustness of the IVW results, to identify various classes of risk or protective factors for AD, early-onset AD, and late-onset AD. We generated 400,274 data entries in total, among which the major analysis method of the IVW model consists of 73,129 records with 4840 exposure traits, which fall into 10 categories: Disease, Medical laboratory science, Imaging, Anthropometric, Treatment, Molecular trait, Gut microbiota, Past history, Family history, and Lifestyle trait. More importantly, a freely accessed online platform called MRAD (https://gwasmrad.com/mrad/) has been developed using the Shiny package with MR analysis results. Additionally, novel potential AD therapeutic targets (CD33, TBCA, VPS29, GNAI3, PSME1) are identified, among which CD33 was positively associated with the main outcome traits of AD, as well as with both EOAD and LOAD. TBCA and VPS29 were negatively associated with the main outcome traits of AD, as well as with both EOAD and LOAD. GNAI3 and PSME1 were negatively associated with the main outcome traits of AD, as well as with LOAD, but had no significant causal association with EOAD. The findings of our research advance our understanding of the etiology of AD.

In today’s era of rapid scientific and technological advancements, the deep integration of artificial intelligence technology and education is paving the way for intelligent development in English education. This paper designs the architecture of the interactive platform for English education in colleges and universities based on artificial intelligence technology, provides relevant tools for interaction assistance, and establishes an analysis model of students’ online learning interactions. To promote the diversity of English test papers, an improved genetic algorithm is utilised for the intelligent grouping of English test papers. A collaborative filtering algorithm is used to intelligently predict students’ English grades by considering the time sequence of English courses. Performance tests were designed to verify the effectiveness of the application of the interactive platform for English education. The intelligent paper grouping took an average of 1.247 seconds, and the discrepancy between the predicted and actual English course grades did not surpass one grade. The interactive platform for English education had a minimum response time of 1.69 ms, and the application server didn’t use more than 10% of its total CPU. The interactive platform, which combines artificial intelligence technology with English education, provides a new path for innovating teaching methods in English education.

Abstract Background Management of unscheduled urgent care is a complex concern for many healthcare providers. Facing the challenge of appropriately dispatching unscheduled care, primary and emergency physicians have collaboratively implemented innovative strategies such as telephone triage. Currently, new original solutions tend to emerge with the development of new technologies. We created an interactive patient self-triage platform, ODISSEE, and aimed to explore its accuracy and potential factors affecting its performance using clinical case scenarios. Methods The ODISSEE platform was developed based on previously validated triage protocols for out-of-hours primary care. ODISSEE is composed of 18 icons leading to algorithmic questions that finally provide an advised orientation (emergency or primary care services). To investigate ODISSEE performance, we used 100 clinical case scenarios, each associated with a preestablished orientation determined by a group of experts. Fifteen volunteers were asked to self-triage with 50 randomly selected scenarios using ODISSEE on a digital tablet. Their triage results were compared with the experts’ references. Results The 15 participants performed a total of 750 self-triages, which matched the experts references regarding the level of care in 85.6% of the cases. The orientation was incorrect in 14.4%, with an undertriage rate of 1.9% and an overtriage rate of 12.5%. The tool’s specificity and sensitivity to advise participants on the appropriate level of care were 69% (95% CI: 64—74) and 97% (95% CI: 95—98) respectively. When combined with advice on the level of urgency, the tool only found the correct orientation in 68.4% with 9.2% of undertriages and 22.4% of overtriages. Some participant characteristics and the types of medical conditions demonstrated a significant association with the tool performance. Conclusion Self-triage apps, such as the ODISSEE platform, could represent an innovative method to allow patients to self-triage to the most appropriate level of care. This study based on clinical vignettes highlights some positive arguments regarding ODISSEE safety, but further research is needed to assess the generalizability of such tools to the population without equity issues.

Background: Management of unscheduled urgent care is a complex concern for many healthcare providers. Facing the challenge of appropriately dispatching unscheduled care, primary and emergency physicians have collaboratively implemented innovative strategies such as telephone triage. Currently, new original solutions tend to emerge with the development of new technologies. We created an interactive patient self-triage platform, ODISSEE, and aimed to explore its accuracy and potential factors affecting its performance using clinical case scenarios.Methods: The ODISSEE platform was developed based on previously validated triage protocols for out-of-hours primary care. ODISSEE is composed of 18 icons leading to algorithmic questions that finally provide an advised orientation (emergency or primary care services). To investigate ODISSEE performance, we used 100 clinical case scenarios, each associated with a preestablished orientation determined by a group of experts. Fifteen volunteers were asked to self-triage with 50 randomly selected scenarios using ODISSEE on a digital tablet. Their triage results were compared with the experts' references.Results: The 15 participants performed a total of 750 self-triages, which matched the experts references regarding the level of care in 85.6% of the cases. The orientation was incorrect in 14.4%, with an undertriage rate of 1.9% and an overtriage rate of 12.5%. The tool's specificity and sensitivity to advise participants on the appropriate level of care were 69% (95% CI: 64-74) and 97% (95% CI: 95-98) respectively. When combined with advice on the level of urgency, the tool only found the correct orientation in 68.4% with 9.2% of undertriages and 22.4% of overtriages. Some participant characteristics and the types of medical conditions demonstrated a significant association with the tool performance.Conclusion: Self-triage apps, such as the ODISSEE platform, could represent an innovative method to allow patients to self-triage to the most appropriate level of care. This study based on clinical vignettes highlights some positive arguments regarding ODISSEE safety, but further research is needed to assess the generalizability of such tools to the population without equity issues. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a complex degenerative disease of the central nervous system, and elucidating its pathogenesis remains challenging. In this study, we used the inverse-variance weighted (IVW) model as the major analysis method to perform hypothesis-free Mendelian randomization (MR) analysis on the data from MRC IEU OpenGWAS (18,097 exposure traits and 16 AD outcome traits), and conducted sensitivity analysis with six models, to assess the robustness of the IVW results, to identify various classes of risk or protective factors for AD, early-onset AD, and late-onset AD. We generated 400,274 data entries in total, among which the major analysis method of the IVW model consists of 73,129 records with 4840 exposure traits, which fall into 10 categories: Disease, Medical laboratory science, Imaging, Anthropometric, Treatment, Molecular trait, Gut microbiota, Past history, Family history, and Lifestyle trait. More importantly, a freely accessed online platform called MRAD (https://gwasmrad.com/mrad/) has been developed using the Shiny package with MR analysis results. Additionally, novel potential AD therapeutic targets (CD33, TBCA, VPS29, GNAI3, PSME1) are identified, among which CD33 was positively associated with the main outcome traits of AD, as well as with both EOAD and LOAD. TBCA and VPS29 were negatively associated with the main outcome traits of AD, as well as with both EOAD and LOAD. GNAI3 and PSME1 were negatively associated with the main outcome traits of AD, as well as with LOAD, but had no significant causal association with EOAD. The findings of our research advance our understanding of the etiology of AD., Competing Interests: TZ, HL, MZ, YX, MZ, LC No competing interests declared, (© 2024, Zhao et al.)

This research is a case study that analyzes the problem of the Faith-Learning Integration management process for the Korean Sunday School “Y” in Indonesia during the COVID-19 pandemic. In this research, data collection was carried out with 5 informants, 58 questionnaires from respondents consisting of students, parents and teachers as well as recordings of student activity results. From the results of this study, it can be seen that there are several obstacles experienced by students, teachers and parents in the Faith-Learning Integration management process. First, students are not satisfied with centering on video learning platforms because there is no interaction between teachers, students and their friends so it needs to be changed from a one-way platform, such as video to an interactive platform between students and teachers such as Zoom or Google Classroom, etc. In addition, students from parents of other religions do not get support and attention from their parents to carry out Faith-Learning Integration with Work From Home (WFH) so that Faith-Learning Integration activities stop. Second, teachers' technical skills are very limited, so interactive online classroom training is required. Third, parents feel burdened and stressed when they have to give examples of faith, morals and good discipline to children in Faith-Learning Integration during WFH. The last obstacle, the 5 dimensions of the Faith-Learning Integration are not tightly integrated with each other so they do not run in harmony. The dimensions of Leitourgia and Didache were implemented well, while Kerygma, Koinonia, and Diakonia were not carried out well due to social distancing obstacles. For this reason, it is recommended that "Y" management use an interactive platform and develop innovative and creative ways that can motivate students so that they do not feel the distance between teachers and students and the surrounding environment is getting farther away even though they participate in social distancing. BAHASA INDONESIA ABSTRACT: Penelitian ini adalah studi kasus yang menganalisa masalah proses manajemen pengintegrasian pembelajaran iman Sekolah Minggu Korea “Y” di Indonesia pada masa pandemi COVID-19. Dalam penelitian ini dilaksanakan pengumpulan data dengan 5 orang narasumber, 58 hasil kuesioner dari responden yang terdiri dari siswa, orangtua dan guru serta rekaman hasil aktivitas siswa. Dari hasil penelitian ini terlihat adanya beberapa kendala yang dialami oleh siswa, guru dan orang tua dalam proses manajemen pengintegrasian pembelajaran iman. Pertama, siswa tidak puas dengan berpusat pada media pembelajaran video karena tidak terjadi interaksi antara guru, siswa dan teman-temannya sehingga perlu diubah dari media yang bersifat satu arah, seperti video ke berbagai media yang interaktif antara siswa dan guru seperti Zoom atau Google Classroom, dll. Selain itu, siswa dari orangtua yang beragama lain kurang mendapat dukungan dan perhatian dari orangtuanya untuk melakukan pengintegrasian pembelajaran iman dengan pembelajaran daring dari rumah sehingga aktivitas pengintegrasian pembelajaran iman jadi terhenti. Kedua, keterampilan teknis guru sangat terbatas sehingga diperlukan pelatihan kelas online secara interaktif. Ketiga, orangtua merasa terbebani dan stress ketika harus memberikan contoh iman, moral dan disiplin yang baik kepada anak-anak dalam pengintegrasian pembelajaran iman selama pembelajaran daring dari rumah. Kendala yang terakhir, 5 dimensi dari pengintegrasian pembelajaran iman tidak terintegrasi satu sama lain secara erat sehingga tidak berjalan selaras. Dimensi Leitourgia dan Didache dilaksanakan dengan baik, sedangkan Kerygma, Koinonia, dan Diakonia tidak terlaksana dengan baik karena adanya hambatan social distancing. Untuk itu disarankan manajemen “Y” menggunakan berbagai media yang interaktif dan mengembangkan cara yang inovatif dan kreatif yang dapat memotivasi siswa supaya tidak merasakan jarak antara guru dan siswa serta lingkungan sekitarnya semakin jauh walaupun mengikuti social distancing.

[Display omitted] • Emerging contaminants (ECs) removal by biochar was modeled by machine learning (ML) • ML and classical adsorption models were coupled to build models. • Ash content of biochar had most critical influences on the adsorption speed of ECs. • Surface structure of biochar played a dominant role in maximum adsorption capacity. • An interactive Biochar-ECs platform was designed for users based on our ML models. Insights into key properties of biochar with a fast adsorption rate and high adsorption capacity are urgent to design biochar as an adsorbent in pollution emergency treatment. Machine learning (ML) incorporating classical theoretical adsorption models was applied to build prediction models for adsorption kinetics rate (i.e., K) and maximum adsorption capacity (i.e., Q m) of emerging contaminants (ECs) on biochar. Results demonstrated that the prediction performance of adaptive boosting algorithm significantly improved after data preprocessing (i.e., log-transformation) in the small unbalanced datasets with R 2 of 0.865 and 0.874 for K and Q m , respectively. The surface chemistry, primarily led by ash content of biochar significantly influenced the K, while surface porous structure of biochar showed a dominant role in predicting Q m. An interactive platform was deployed for relevant scientists to predict K and Q m of new biochar for ECs. The research provided practical references for future engineered biochar design for ECs removal. [ABSTRACT FROM AUTHOR]

We provide a narrative review of the crucial elements for online Problem Based Learning (PBL) and a reflective overview of factors to consider when temporarily moving to online tutorials, forming a practical guide for educators in the health professions and beyond. We give general set-up advice based on the literature and our own recent experience (tutor and learner observational feedback, departmental meeting notes, newly-developed written resources and performance reports) of transitioning between temporary online PBL and face-to-face PBL but note that the majority of this advice translates easily to many types of virtual, interactive tutorial. We also include contextual evidence and theories from existing literature, with a focus on online PBL facilitation, learning and quality assurance. Despite widespread implementation of online teaching, there remain unanswered questions about whether deep learning occurs. The focus of this reflective paper is to better align online PBL practice with the principles of contextual, active, collaborative and self-directed learning and learning issues to be pursued. [ABSTRACT FROM AUTHOR]

Personalised medicine is based on the principle that each body is unique and will respond to therapies differently. In cardiology, characterising patient specific cardiovascular properties would help in personalising care. One promising approach for characterising these properties relies on performing computational analysis of multimodal imaging data. An interactive cardiac imaging environment, which can seamlessly render, manipulate, derive calculations, and otherwise prototype research activities, is therefore sought-after.We developed the Cardiac Electro-Mechanics Research Group Application (CemrgApp) as a platform with custom image processing and computer vision toolkits for applying statistical, machine learning and simulation approaches to study physiology, pathology, diagnosis and treatment of the cardiovascular system. CemrgApp provides an integrated environment, where cardiac data visualisation and workflow prototyping are presented through a common graphical user interface.

São Paulo is the most populous state in Brazil, home to around 22% of the country’s population. The total number of Covid-19-infected people in São Paulo has reached more than 1 million, while its total death toll stands at 25% of all the country’s fatalities. Joining the Brazilian academia efforts in the fight against Covid-19, in this paper we describe a unified framework for monitoring and forecasting the Covid-19 progress in the state of São Paulo. More specifically, a freely available, online platform to collect and exploit Covid-19 time-series data is presented, supporting decision-makers while still allowing the general public to interact with data from different regions of the state. Moreover, a novel forecasting data-driven method has also been proposed, by combining the so-called Susceptible-Infectious-Recovered-Deceased model with machine learning strategies to better fit the mathematical model’s coefficients for predicting Infections, Recoveries, Deaths, and Viral Reproduction Numbers. We show that the obtained predictor is capable of dealing with badly conditioned data samples while still delivering accurate 10-day predictions. Our integrated computational system can be used for guiding government actions mainly in two basic aspects: real-time data assessment and dynamic predictions of Covid-19 curves for different regions of the state. We extend our analysis and investigation to inspect the virus spreading in Brazil in its regions. Finally, experiments involving the Covid-19 advance in other countries are also given.

The process of moving from experimental data to modeling and characterizing the dynamics and interactions in natural processes is a challenging task. This paper proposes an interactive platform for fitting data derived from experiments to mathematical expressions and carrying out spatial visualization. The platform is designed using a component-based software architectural approach, implemented in R and the Java programming languages. It uses experimental data as input for model fitting, then applies the obtained model at the landscape level via a spatial temperature grid data to yield regional and continental maps. Different modules and functionalities of the tool are presented with a case study, in which the tool is used to establish a temperature-dependent virulence model and map the potential zone of efficacy of a fungal-based biopesticide. The decision support system (DSS) was developed in generic form, and it can be used by anyone interested in fitting mathematical equations to experimental data collected following the described protocol and, depending on the type of investigation, it offers the possibility of projecting the model at the landscape level.

Since current studies on clustering analysis mainly focus on exploring spatial or temporal patterns separately, a co-clustering algorithm is utilized in this study to enable the concurrent analysis of spatio-temporal patterns. To allow users to adopt and adapt the algorithm for their own analysis, it is integrated within the server side of an interactive web-based platform. The client side of the platform, running within any modern browser, is a graphical user interface (GUI) with multiple linked visualizations that facilitates the understanding, exploration and interpretation of the raw dataset and co-clustering results. Users can also upload their own datasets and adjust clustering parameters within the platform. To illustrate the use of this platform, an annual temperature dataset from 28 weather stations over 20 years in the Netherlands is used. After the dataset is loaded, it is visualized in a set of linked visualizations: a geographical map, a timeline and a heatmap. This aids the user in understanding the nature of their dataset and the appropriate selection of co-clustering parameters. Once the dataset is processed by the co-clustering algorithm, the results are visualized in the small multiples, a heatmap and a timeline to provide various views for better understanding and also further interpretation. Since the visualization and analysis are integrated in a seamless platform, the user can explore different sets of co-clustering parameters and instantly view the results in order to do iterative, exploratory data analysis. As such, this interactive web-based platform allows users to analyze spatio-temporal data using the co-clustering method and also helps the understanding of the results using multiple linked visualizations. [ABSTRACT FROM AUTHOR]

Background: Machine learning and artificial intelligence have shown promising results in many areas and are driven by the increasing amount of available data. However, these data are often distributed across different institutions and cannot be easily shared owing to strict privacy regulations. Federated learning (FL) allows the training of distributed machine learning models without sharing sensitive data. In addition, the implementation is time-consuming and requires advanced programming skills and complex technical infrastructures., Objective: Various tools and frameworks have been developed to simplify the development of FL algorithms and provide the necessary technical infrastructure. Although there are many high-quality frameworks, most focus only on a single application case or method. To our knowledge, there are no generic frameworks, meaning that the existing solutions are restricted to a particular type of algorithm or application field. Furthermore, most of these frameworks provide an application programming interface that needs programming knowledge. There is no collection of ready-to-use FL algorithms that are extendable and allow users (eg, researchers) without programming knowledge to apply FL. A central FL platform for both FL algorithm developers and users does not exist. This study aimed to address this gap and make FL available to everyone by developing FeatureCloud, an all-in-one platform for FL in biomedicine and beyond., Methods: The FeatureCloud platform consists of 3 main components: a global frontend, a global backend, and a local controller. Our platform uses a Docker to separate the local acting components of the platform from the sensitive data systems. We evaluated our platform using 4 different algorithms on 5 data sets for both accuracy and runtime., Results: FeatureCloud removes the complexity of distributed systems for developers and end users by providing a comprehensive platform for executing multi-institutional FL analyses and implementing FL algorithms. Through its integrated artificial intelligence store, federated algorithms can easily be published and reused by the community. To secure sensitive raw data, FeatureCloud supports privacy-enhancing technologies to secure the shared local models and assures high standards in data privacy to comply with the strict General Data Protection Regulation. Our evaluation shows that applications developed in FeatureCloud can produce highly similar results compared with centralized approaches and scale well for an increasing number of participating sites., Conclusions: FeatureCloud provides a ready-to-use platform that integrates the development and execution of FL algorithms while reducing the complexity to a minimum and removing the hurdles of federated infrastructure. Thus, we believe that it has the potential to greatly increase the accessibility of privacy-preserving and distributed data analyses in biomedicine and beyond., (©Julian Matschinske, Julian Späth, Mohammad Bakhtiari, Niklas Probul, Mohammad Mahdi Kazemi Majdabadi, Reza Nasirigerdeh, Reihaneh Torkzadehmahani, Anne Hartebrodt, Balazs-Attila Orban, Sándor-József Fejér, Olga Zolotareva, Supratim Das, Linda Baumbach, Josch K Pauling, Olivera Tomašević, Béla Bihari, Marcus Bloice, Nina C Donner, Walid Fdhila, Tobias Frisch, Anne-Christin Hauschild, Dominik Heider, Andreas Holzinger, Walter Hötzendorfer, Jan Hospes, Tim Kacprowski, Markus Kastelitz, Markus List, Rudolf Mayer, Mónika Moga, Heimo Müller, Anastasia Pustozerova, Richard Röttger, Christina C Saak, Anna Saranti, Harald H H W Schmidt, Christof Tschohl, Nina K Wenke, Jan Baumbach. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 12.07.2023.)

Critical-sized maxillofacial bone defects have been a tough clinical challenge considering their requirements for functional and structural repair. In this study, an injectable in-situ forming double cross-linked hydrogel was prepared from gelatin (Gel), 20 mg/mL alginate dialdehyde (ADA), 4.5 mg/mL Ca2+ and borax. Improved properties of composite hydrogel might well fit and cover irregular geometric shape of facial bone defects, support facial structures and conduct masticatory force. We innovatively constructed a bioactive poly-porous structure by decoration with nano-sized hydroxyapatite (nHA). The highly ordered, homogeneous and size-confined porous surface served as an interactive osteogenic platform for communication and interplay between macrophages and bone marrow derived stem cells (BMSCs). Effective macrophage-BMSC crosstalk well explained the remarkable efficiency of nHA-loaded gelatin/alginate hydrogel (nHA@Gel/ADA) in the repair of critical-size skull bone defect. Collectively, the composite hydrogel constructed here might serve as a promising alternative in repair process of complex maxillofacial bone defects. Schematic diagram of a composite ADA-Gel hydrogel double cross-linked by Ca2+ and chemical bond and decorated with nHA. Dynamic dissociated PO 4 3− and Ca2+ from nHA interacted with biopolymer to form a tight and compact structure. Improved biomedical and mechanical properties were achieved for its application in maxillofacial bone defect. By interactive modulation between macrophages and BMSCs, the composite hydrogel significantly accelerated bone repair process. [Display omitted] [ABSTRACT FROM AUTHOR]

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