This review examines recent advances and applications of three-dimensional (3D) printing technology in orthopedic fracture management, with a particular focus on its transformative role in personalized treatment strategies. The introduction of patient-specific 3D-printed implants and fracture plates has markedly improved surgical outcomes by reducing operative time, enhancing anatomical alignment, and promoting bone healing. By enabling the fabrication of customized implants, 3D printing provides an innovative approach for managing complex fractures and bone defects, particularly in cases where conventional methods are inadequate. Key benefits discussed include the development of tailored fracture plates, bone scaffolds, and bioactive materials that support bone regeneration. The review also explores the potential of emerging technologies such as four-dimensional printing and bioprinting, which allow for the creation of dynamic implants capable of adapting to biological changes and facilitating tissue regeneration. In addition, the integration of artificial intelligence into preoperative planning and implant design is highlighted for its contribution to improving surgical precision and individualized treatment. This review consolidates the latest advancements while also addressing challenges, including high production costs and regulatory barriers, that must be overcome for widespread clinical adoption. In conclusion, the future of orthopedic fracture management is expected to be significantly reshaped by the continuous evolution of 3D printing technologies, offering more personalized, effective, and efficient solutions for patients. As these innovations progress, 3D printing is anticipated to play a pivotal role in advancing orthopedic surgery and ultimately improving patient outcomes.

Radiation therapy (RT) is a cornerstone of cancer management, required in approximately half of all cancer cases, and is particularly relevant for older adults, who constitute the majority of oncology patients. Despite its localized nature and generally favorable toxicity profile, RT remains underutilized in this population, often due to age-related biases, comorbidities, or the limited integration of geriatric assessment into treatment planning. This review examines the evolving role of RT as an age-inclusive modality, highlighting innovations such as intensity-modulated and stereotactic techniques that enable more conformal, less toxic, and increasingly personalized regimens for older adults. Special attention is given to the challenges of frailty, cognitive impairment, and movement disorders, which may complicate treatment delivery and necessitate tailored adaptations. The role of comprehensive geriatric assessment and frailty screening tools is critically appraised, with emphasis on their predictive value in identifying treatment-limiting vulnerabilities and supporting shared decision-making. The review underscores the need to shift from age-based to function-based treatment paradigms, advocating for greater inclusion of older adults in clinical trials and for a multidisciplinary approach that aligns oncologic goals with patient priorities. When appropriately tailored, RT provides a safe, effective, and goal-concordant treatment option for older adults, and its optimized integration into geriatric oncology care is essential to meet the needs of an aging global population.

As the primary skin-contact interface in wearable electrocardiograph (ECG) devices, epidermal electrodes play a pivotal role in determining both signal quality and biocompatibility. With continuous advancements in materials science and structural engineering, next-generation flexible and stretchable bioelectrodes have emerged, enabling long-term ECG monitoring and offering superior signal-to-noise ratios compared to conventional clinical electrodes. Their performance in ensuring reliable signal acquisition and user comfort is primarily governed by key interfacial mechanical and electrical properties, including mechanical compliance (i.e., flexibility and stretchability), interfacial adhesion (i.e., conformability and adhesion strength), and electrical characteristics (i.e., contact impedance). In recent years, significant progress has been made in enhancing the signal acquisition capabilities of flexible and stretchable bioelectrodes by optimizing these critical interfacial attributes. This review highlights the latest advances in conformable epidermal electrodes, encompassing traditional wet electrodes, flexible dry electrodes, novel dry electrodes based on organic electrochemical transistors, and integrated wearable systems. We systematically examine strategies for improving skin-electrode interface performance in ECG monitoring. Finally, we discuss ongoing challenges and future directions to advance epidermal electrode technologies for next-generation wearable healthcare applications.

Advancing age substantially increases cancer risk, primarily due to progressive biological alterations over time. With the global population aging rapidly, the incidence of cancer is also rising. In cancer immunotherapy, patient age is emerging as a critical determinant for both identifying and predicting responses to immune checkpoint inhibitors. Aging is accompanied by significant changes in the immune system, notably a decline in T-cell function and a reduction in tumor-infiltrating lymphocytes, which collectively reshape the tumor microenvironment and weaken antitumor immunity. Immune senescence compromises the ability to recruit and maintain functional TIL activity, thereby limiting the benefits of immune checkpoint inhibitors therapy. Furthermore, senescent tumor cells influence their surroundings by releasing a broad spectrum of pro-inflammatory cytokines and chemokines, a phenomenon termed the senescence-associated secretory phenotype, while simultaneously upregulating immune inhibitory markers such as PD-L1. In addition, age-related immune dysregulation exacerbates cellular exhaustion, leading to abnormal expression of key biomarkers that govern immune checkpoint inhibitors efficacy and ultimately attenuating antitumor immune responses. This perspective discusses the mechanisms through which aging alters systemic immunity and the tumor microenvironment, thereby reducing immunotherapy effectiveness. By integrating current mechanistic insights into the interplay between aging and cancer immunobiology, we highlight potential aging-related biomarkers that may improve therapeutic strategies in geriatric oncology. A deeper understanding of these interactions is essential for developing personalized immunotherapeutic approaches tailored to the unique needs of elderly cancer patients.

The treatment of early invasive breast cancer in older patients poses unique challenges due to the distinct biological, clinical, and psychosocial complexities associated with aging. As the population of breast cancer patients aged 70 years and older continues to grow, their persistent underrepresentation in clinical trials remains a major obstacle to evidence-based treatment decision-making. To support the development of a more effective, personalized, and patient-centered approach to systemic therapy, this review outlines the biological features of breast cancer in older women, synthesizes current evidence on neoadjuvant and adjuvant systemic therapies, and discusses strategies for individualized treatment decision-making. Key recommendations include the use of hormonal therapy as the standard of care for hormone receptor positive breast cancer, neoadjuvant therapy primarily when tumor downstaging is desired, and chemotherapy or anti-human epidermal growth factor receptor 2 therapy for relatively fit older patients with high-risk subtypes. Additionally, bisphosphonates may help preserve bone health and reduce recurrence risk. Novel targeted therapies such as cyclin-dependent kinase 4 and 6 inhibitors and immune checkpoint inhibitors show promise, though further studies are needed to confirm their safety and efficacy in older populations. Comprehensive geriatric assessments are essential for identifying patient frailty and vulnerabilities, while predictive tools such as the Cancer and Aging Research Group Breast Cancer model can help assess toxicity risk. In this population, competing risks of non-cancer-related mortality may reduce the absolute benefit of systemic treatment. For patients with elevated risks of other-cause mortality, the potential survival benefit of cancer therapy may be negligible. Predictive models that account for competing mortality, such as the PORTRET tool, can facilitate personalized and shared decision-making.

Aims: This study proposes a multimodal emotion recognition framework that combines a private-shared disentangled multimodal variational autoencoder (DMMVAE) with a long short-term memory (LSTM) network, herein referred to as DMMVAE-LSTM. The primary objective is to improve the robustness and generalizability of emotion recognition by effectively leveraging the complementary features of electroencephalogram (EEG) signals and facial expression data.

Methods: We first trained a variational autoencoder using a ResNet-101 architecture on a large-scale facial dataset to develop a robust and generalizable facial feature extractor. This pre-trained model was then integrated into the DMMVAE framework, together with a convolutional neural network-based encoder and decoder for EEG data. The DMMVAE model was trained to disentangle shared and modality-specific latent representations across both EEG and facial data. Following this, the outputs of the encoders were concatenated and fed into a LSTM classifier for emotion recognition.

Results: Two sets of experiments were conducted. First, we trained and evaluated our model on the full dataset, comparing its performance with state-of-the-art methods and a baseline LSTM model employing a late fusion strategy to combine EEG and facial features. Second, to assess robustness, we tested the DMMVAE-LSTM framework under data-limited and modality dropout conditions by training with partial data and simulating missing modalities. The results demonstrate that the DMMVAE-LSTM framework consistently outperforms the baseline, especially in scenarios with limited data, indicating its capacity to learn structured and resilient latent representations.

Conclusion: Our findings underscore the benefits of multimodal generative modeling for emotion recognition, particularly in enhancing classification performance when training data are scarce or partially missing. By effectively learning both shared and private representations, DMMVAE-LSTM framework facilitates more reliable emotion classification and presents a promising solution for real-world applications where acquiring large labeled datasets is challenging.

Diabetic foot ulcers (DFUs) are a serious complication of diabetes and often result in amputation. Traditional wound care methods have limitations in addressing the complex pathophysiology of DFUs. Hydrogel dressings, a type of biomaterial, have emerged as promising candidates for treating DFUs due to their biocompatibility, ability to retain moisture, and potential to incorporate therapeutic agents. Hydrogels create a moist environment, promote cell migration, and reduce inflammation, thereby supporting wound healing. Incorporating bioactive molecules, such as growth factors and anti-inflammatory agents, can further enhance the effectiveness of hydrogels. Additionally, stem cells can be loaded into hydrogels to improve tissue regeneration and help modulate the wound microenvironment. Recent advancements in hydrogel technology have also led to the development of smart hydrogels that can respond to changes in wound conditions, such as glucose levels and pH. These intelligent dressings offer personalized care by delivering targeted treatments based on real-time wound data. This review explores the mechanisms behind DFU development, the role of hydrogels in wound healing, and recent progress in hydrogel technologies for personalized DFU care.

This study integrates ChatGPT and complex network (CN) techniques into an accident analysis framework designed to reduce manual effort in accident causation analysis. The proposed framework supports construction stakeholders in extracting causal factors (CFs) from accident reports and identifying both critical CFs and key causal paths. A multistep research design was adopted to develop and validate this novel framework for analyzing crane-related construction accident reports using ChatGPT and CN techniques. First, ChatGPT was prompted to extract CFs from a database of crane-related accident reports. Second, evaluation metrics and an expert questionnaire survey were developed to assess ChatGPT’s performance in CF extraction. Finally, CN analysis was conducted to explore the relationships among CFs and to identify critical causal paths. A total of 95 crane-related accidents from Hong Kong (2011-2020) were analyzed using the proposed framework. The critical CFs identified included: “carelessness”, “operation error”, “crane unbalanced”, “machine failure”, “parts of a crane fall”, “object strike”, “worker fall”, “trapping”, “collapse of crane”, and “load drop”. The critical path identified was: “broken/failed rope” → “load drop” → “object strike”. The primary contribution of this study lies in developing an AI-driven framework that combines the contextual understanding of ChatGPT with the structural analysis capabilities of CN methods—offering a novel and scalable approach to accident causation analysis in the construction industry. Safety managers and practitioners can leverage this framework to improve the automation, consistency, and interpretability of construction accident reporting.

This study explores the adoption of Virtual Reality (VR) in the Nigerian construction industry, with a focus on its potential benefits and associated challenges. Purposive and snowball sampling techniques were employed to select 52 construction professionals from Benin, Edo State,an emerging urban center with extensive construction activity. Adopting a quantitative approach, the research utilized a five-point Likert scale survey to assess perceptions of the benefits and barriers to VR adoption. The survey was pretested for clarity and reliability, and data were collected via the Qualtrics platform. The findings indicate that the key benefits of VR include improved task-technology alignment, enhanced workplace safety through virtual training, and more effective remote collaboration. VR was also found to enrich user experience and learning engagement by simulating high-risk scenarios to aid hazard prevention. Nevertheless, the study identifies several critical barriers to adoption, such as uncertainty regarding learning outcomes, technical disruptions, and high implementation costs. Despite these limitations, VR holds considerable promise for transforming training and professional development in the construction sector. To maximize its impact, the study recommends the development of customized training modules, technological improvements to enhance system reliability, and government support to mitigate implementation costs. Overall, VR has the potential to significantly improve training effectiveness, safety standards, and operational efficiency in the Nigerian construction industry, provided that the identified barriers are adequately addressed.

Amid growing global attention to occupational health and safety, the construction industry faces critical challenges associated with engineered stone, which emits high concentrations of respirable crystalline silica during processing and has been linked to severe lung diseases. In response, Australia enacted a comprehensive nationwide ban on engineered stone in July 2024. Drawing on media framing theory, this study analyzes public discourse and sentiment surrounding the ban by examining 7,198 comments collected from Reddit and YouTube. Through Latent Dirichlet Allocation, three dominant themes emerged: health risks and safety concerns, economic impacts and industry transition, and regulatory implementation. Sentiment analysis revealed that 55.5% of the comments expressed negative sentiment, mainly centered on economic concerns, while 21.3% were positive, emphasizing health benefits, and 23.1% were neutral. Economic impact frames predominated among negative comments, whereas health risk frames were more common in positive ones. These findings suggest that future policy communications should more effectively integrate narratives around both health protection and economic transition. This study contributes to the methodological development of sentiment analysis and offers practical insights for policy formulation and implementation.

Aims: Storytelling has evolved alongside human culture, giving rise to new media such as social robots. While these robots employ modalities similar to those used by humans, they can also utilize non-biomimetic modalities, such as color, which are commonly associated with emotions. As research on the use of colored light in robotic storytelling remains limited, this study investigates its integration through three empirical studies.

Methods: We conducted three studies to explore the impact of colored light in robotic storytelling. The first study examined the effect of emotion-inducing colored lighting in romantic storytelling. The second study employed an online survey to determine appropriate light colors for specific emotions, based on images of the robot’s emotional expressions. The third study compared four lighting conditions in storytelling: emotion-driven colored lights, context-based colored lights, constant white light, and no additional lighting.

Results: The first study found that while colored lighting did not significantly influence storytelling experience or perception of the robot, it made recipients felt more serene. The second study showed improved recognition of amazement, rage, and neutral emotional states when colored light accompanied body language. The third study revealed no significant differences across lighting conditions in terms of storytelling experience, emotions, or robot perception; however, participants generally appreciated the use of colored lights. Emotion-driven lighting received slightly more favorable subjective evaluations.

Conclusion: Colored lighting can enhance the emotional expressiveness of robots. Both emotion- driven and context-based lighting strategies are appropriate for robotic storytelling. Through this series of studies, we contribute to the understanding of how colored lights are perceived in robotic communication, particularly within storytelling contexts.

Bone defects represent a significant orthopedic challenge, with associated disorders continue to pose clinical difficulties. In the biomedical field, advancements in three-dimensional (3D) printing technology have established bone tissue engineering (BTE) scaffolds as a promising approach for effective treatment. These scaffolds not only provide structural support for cells but also serve as templates to guide bone tissue regeneration. In recent years, owing to their exceptional physicochemical properties, two-dimensional nanomaterials (2D NMs) have garnered increasing attention and have been widely explored as additives in the fabrication of BTE scaffolds. This review centers on the most recent developments in the combination of 2D NMs and 3D printing for BTE applications. It begins with a concise summary of the common synthesis and surface modification methods of 2D NMs. Then, it offers a comprehensive overview of recent advancements in their use within BTE. Finally, it discusses current challenges and future perspectives regarding the application of 2D NMs-based 3D-printed scaffolds in bone tissue regeneration.

The development of high-performance narrowband red organic light-emitting diodes (OLEDs) has garnered significant attention, offering both exciting opportunities and formidable challenges. In this study, we report the synthesis of a novel red thermally activated delayed fluorescence (TADF) molecule, 10,10',10''-([1,2,4]triazolo[1,5-a][1,3,5]triazine-2,5,7-triyltris(benzene-4,1-diyl))tris(10H-phenoxazine) (TPXZ-TAZTRZ), which integrates a highly electron-deficient triazolotriazine unit as the acceptor and three strongly electron-donating phenoxazine (PXZ) moieties as donors. TPXZ-TAZTRZ exhibits a small singlet-triplet energy gap, enabling a rapid reverse intersystem crossing rate. Additionally, it shows a broad emission spectrum peaking at 634 nm, spanning the yellow-to-red region. These features render TPXZ-TAZTRZ as an ideal TADF sensitizer for narrowband red fluorescent OLEDs. Accordingly, TPXZ-TAZTRZ was employed to sensitize the conventional fluorescent emitter DBP. The resulting TADF-sensitized fluorescence OLEDs (TSF-OLEDs) demonstrated efficient energy transfer from the TADF sensitizer to the emitter, effectively addressing the limitations previous encountered with TADF systems. The devices achieved high-performance pure red emission, with Commission International de l'Éclairage (CIE) coordinates of [0.67, 0.33], an emission peak at 612 nm, a narrow full width at half maximum (FWHM) of 27 nm, and a maximum external quantum efficiency of 16.2%.

Aims: This study aims to enhance the design of augmented reality (AR) technologies for remote collaboration by examining the complex relationships among individual factors (user characteristics), psychological and physiological states during AR-mediated remote collaboration, and outcomes within an Input-Mediator-Output (IMO) model. The goal is to evaluate how individual characteristics influence psychological and physiological experiences, as well as task performance, in AR-mediated collaboration.

Methods: We hypothesize and evaluate an IMO model and use correlation analyses to examine the relationships among person-related input variables (e.g., predispositions, traits, attitudes, states, and contextual factors), psychological and physiological emergent states, and performance-related output variables.

Conclusion: Our study provides a foundational framework for understanding the interplay between individuals, tasks, and technology, underscoring the need for AR tools that align with user characteristics. It also lays the groundwork for future IMO research in AR-mediated remote collaboration, contributing to the development of more effective and health-promoting AR technologies.

High-performance and long-lifetime blue organic light-emitting diodes (OLEDs) are crucial for meeting the demands of advanced display and lighting technologies. Despite high device efficiency has been achieved in blue OLEDs, development of high-performance and long-lifetime blue OLEDs still lag far behind their red/green counterparts due to the presence of long-lived high-energy triplet excitons and polarons. Given the critical role of charge and exciton management in both the emission and degradation processes of OLEDs, this review systematically summarizes strategies for suppressing charge leakage and exciton quenching, as well as for enhancing exciton utilization in blue fluorescent, phosphorescent, and thermally activated delayed fluorescent (TADF) OLEDs. In this context, we further discuss the roles of conventional fluorescent hosts, triplet-triplet annihilation/hot exciton hosts, TADF assistant hosts, phosphorescent assistant hosts, and exciplex/electroplex hosts in regulating charge and exciton dynamics in blue OLEDs. Additionally, the modification of emitting layer materials is highlighted as a key strategy for managing charge and exciton processes in efficient and stable solution-processed blue OLEDs. Based on current insights into the efficiency and operational stability of blue OLEDs, this review proposes feasible charge and exciton management strategies to address the current challenges.