Personalizing radiotherapy with adaptive radiotherapy: Interest and challenges.

Adaptive radiotherapy (ART) is a recent development in radiotherapy technology and treatment personalization that allows treatment to be tailored to the daily anatomical changes of patients. While it was until recently only performed "offline", i.e. between two radiotherapy sessions, it is now possible during ART to perform a daily online adaptive process for a given patient. Therefore, ART allows a daily customization to ensure optimal coverage of the treatment target volumes with minimized margins, taking into account only the uncertainties related to the adaptive process itself. This optimization appears particularly relevant in case of daily variations in the positioning of the target volume or of the organs at risk (OAR) associated with a proximity of these volumes and a tenuous therapeutic index. ART aims to minimize severe acute and late toxicity and allows tumor dose escalation. These new achievements have been possible thanks to technological development, the contribution of new multimodal and onboard imaging modalities and the integration of artificial intelligence tools for the contouring, planning and delivery of radiation therapy. Online ART is currently available on two types of radiotherapy machines: MR-linear accelerators and recently CBCT-linear accelerators. We will first describe the benefits, advantages, constraints and limitations of each of these two modalities, as well as the online adaptive process itself. We will then evaluate the clinical situations for which online adaptive radiotherapy is particularly indicated on MR- and CBCT-linear accelerators. Finally, we will detail some challenges and possible solutions in the development of online ART in the coming years.

Robot-Mediated Nudges for Workplace Health: Not a One-Size-Fits-All Modeling Problem.

Prolonged sedentary behavior in the vast population of office and remote workers leads to increased cardiovascular and musculoskeletal health challenges, and existing solutions for encouraging breaks are either costly health coaches or notification systems that are easily ignored. A socially assistive robot (SAR) for promoting healthy workplace practices could provide the physical presence of a health coach along with the scalability of a notification system. To investigate the impact of such a system, we implemented a SAR as an alternative break-taking support solution and examined its impact on individual users' break-taking habits over relatively long-term deployments. We conducted an initial two-month-long study (N = 7) to begin to understand the robot's influence beyond the point of novelty, and we followed up with a week-long data collection (N = 14) to augment the dataset size. The resulting data was used to inform a robot behavior model and formulate possible methods of personalizing robot behaviors. We found that uninterrupted sitting time tended to decrease with our SAR intervention. During model formulation, we found participant responsiveness to the break-taking prompts could be classified into three archetypes and that archetype-specific adjustments to the general model led to improved system success. These results indicate that break-taking prompts are not a one-size-fits-all problem, and that even a small dataset can support model personalization for improving the success of assistive robotic systems.

A conversational agent for enhanced Self-Management after cardiothoracic surgery.

BACKGROUND: Enhanced self-management is crucial for long-term survival following cardiothoracic surgery. OBJECTIVES: This study aimed to develop a conversational agent to enhance patient self-management after cardiothoracic surgery. METHODOLOGY: The solution was designed and implemented following the Design Science Research Methodology. A pilot study was conducted at the hospital to assess the feasibility, usability, and perceived effectiveness of the solution. Feedback was gathered to inform further interactions. Additionally, a focus group with clinicians was conducted to evaluate the acceptability of the solution, integrating insights from the pilot study. RESULTS: The conversational agent, implemented using a rule-based model, was successfully tested with patients in the cardiothoracic surgery unit (n = 4). Patients received one month of text messages reinforcing clinical team recommendations on a healthy diet and regular physical activity. The system received a high usability score, and two patients suggested adding a feature to answer user prompts for future improvements. The focus group feedback indicated that while the solution met the initial requirements, further testing with a larger patient cohort is necessary to establish personalized profiles. Moreover, clinicians recommended that future iterations prioritize enhanced personalization and interoperability with other hospital platforms. Additionally, while the use of artificial generative intelligence was seen as relevant for content personalization, clinicians expressed concerns regarding content safety, highlighting the necessity for rigorous testing. CONCLUSIONS: This study marks a significant step towards enhancing post-cardiothoracic surgery care through conversational agents. The integration of a diversity of stakeholder knowledge enriches the solution, grants ownership and ensures its sustainability. Future research should focus on automating message generation and delivery based on patient data and environmental factors. While the integration of artificial generative intelligence holds promise for enhancing patient interaction, ensuring the safety of its content is essential.

Did we personalize? Assessing personalization by an online reinforcement learning algorithm using resampling.

There is a growing interest in using reinforcement learning (RL) to personalize sequences of treatments in digital health to support users in adopting healthier behaviors. Such sequential decision-making problems involve decisions about when to treat and how to treat based on the user's context (e.g., prior activity level, location, etc.). Online RL is a promising datadriven approach for this problem as it learns based on each user's historical responses and uses that knowledge to personalize these decisions. However, to decide whether the RL algorithm should be included in an "optimized" intervention for real-world deployment, we must assess the data evidence indicating that the RL algorithm is actually personalizing the treatments to its users. Due to the stochasticity in the RL algorithm, one may get a false impression that it is learning in certain states and using this learning to provide specific treatments. We use a working definition of personalization and introduce a resampling-based methodology for investigating whether the personalization exhibited by the RL algorithm is an artifact of the RL algorithm stochasticity. We illustrate our methodology with a case study by analyzing the data from a physical activity clinical trial called HeartSteps, which included the use of an online RL algorithm. We demonstrate how our approach enhances data-driven truth-in-advertising of algorithm personalization both across all users as well as within specific users in the study.

Personalization of Intravaginal rings by droplet deposition modeling based 3D printing technology.

Intravaginal rings (IVRs) are long-acting drug device systems designed for controlled drug release in the vagina. Commercially available IVRs employ a one-size-fits-all development approach, where all patients receive the same drug in similar doses and frequencies, allowing no space for dosage individualization for specific patients' needs. To allow flexibility for dosage individualization, this study explores the impact of infill-density on critical characteristics of personalized IVRs, manufactured using droplet deposition modeling three-dimensional (3D) printing technology. The model drug was dispersed on the surface of thermoplastic polyurethane pellets using an oil coating method. IVR infill-density ranged from 60 to 100 %. The compatibility of the drug and matrix was assessed using thermal and spectroscopic analyses. The IVRs were evaluated for weight, porosity, surface morphology, mechanical properties, and in vitro drug release. The results demonstrated high dimensional accuracy and uniformity of 3D-printed IVRs, indicating the robustness of the printing process. Increasing infill-density resulted in greater weight, storage modulus, Young's modulus, Shore hardness, and compression strength, while reducing the porosity of IVRs. All IVRs showed a controlled drug release pattern when tested under accelerated conditions of temperature for 25 days. Notably, greater infill-densities were associated with a decrease in the percentage of drug released. Overall, the study demonstrated that infill-density was an important parameter for personalizing the critical characteristics of the 3D-printed IVRs to fit individual patient needs.

Optimal control of combination immunotherapy for a virtual murine cohort in a glioblastoma-immune dynamics model.

The immune checkpoint inhibitor anti-PD-1, commonly used in cancer immunotherapy, has not been successful as a monotherapy for the highly aggressive brain cancer glioblastoma. However, when used in conjunction with a CC-chemokine receptor-2 (CCR2) antagonist, anti-PD-1 has shown efficacy in preclinical studies. In this paper, we aim to optimize treatment regimens for this combination immunotherapy using optimal control theory. We extend a treatment-free glioblastoma-immune dynamics ODE model to include interventions with anti-PD-1 and the CCR2 antagonist. An optimized regimen increases the survival of an average mouse from 32 days post-tumor implantation without treatment to 111 days with treatment. We scale this approach to a virtual murine cohort to evaluate mortality and quality of life concerns during treatment, and predict survival, tumor recurrence, or death after treatment. A parameter identifiability analysis identifies five parameters suitable for personalizing treatment within the virtual cohort. Sampling from these five practically identifiable parameters for the virtual murine cohort reveals that personalized, optimized regimens enhance survival: 84% of the virtual mice survive to day 100, compared to 60% survival in a previously studied experimental regimen. Subjects with high tumor growth rates and low T cell kill rates are identified as more likely to die during and after treatment due to their compromised immune systems and more aggressive tumors. Notably, the MDSC death rate emerges as a long-term predictor of either disease-free survival or death.

[Finite element analysis of anatomic plate fixation for proximal clavicular fractures].

OBJECTIVE: To explore establishment and finite element analysis of personalized proximal clavicular anatomical plate screw fixation model. METHODS: A 40-year-old male healthy volunteer was selected and the finite element analysis modules of 3D reconstruction software Mimics 15.01, Hypermesh 2019 and Abaqus 2020 were used. The finite element model of anatomic plate at the proximal clavicle was established, and a vertical load of 250 N was applied to the distal end of long axis of clavicle about 15 mm, then the overall structure, plate and screw displacement cloud image, Mises stress distribution were observed. RESULTS: The displacement distribution of the overall structure shows the maximum displacement was distributed on the distal clavicle. Under the four conditions of normal upper limb weight, longitudinal clavicle fracture, oblique fracture and shoulder impact violence during fall, longitudinal clavicle fracture and oblique fracture, the maximum displacement were 1.04 mm, 1.03 mm, 1.35 mm and 1.33 mm, respectively. The displacement cloud map of titanium alloy steel plate showed the largest displacement was distributed near the distal clavicular bone, and the maximum displacement were 0.89 mm, 0.88 mm, 1.10 mm and 1.09 mm, respectively. The displacement cloud map of titanium alloy screw showed the largest displacement was distributed at the root of the distal screw, and the maximum displacement were 0.88 mm, 0.87 mm, 1.08 mm and 1.06 mm, respectively. Mises stress distribution showed the maximum stress was mainly distributed on titanium alloy plates and screws, and the stress on the clavicle was very small. Mises stress distribution cloud showed the maximum Mises stress was distributed at the second row of screw holes near the clavicle, and the maximum Mises stress were 673.1, 678.1, 648.5, 654.4 MPa, respectively. The maximum stresses of titanium alloy screws were 414.5, 417.4, 415.8 and 419.7 MPa, respectively. CONCLUSION: The biomechanical changes of personalized proximal clavicular anatomical plates are demonstrated by using 3D finite element method to provide biomechanical data for personalized proximal clavicular anatomical plates.

Predicting the outcome of psychotherapy for chronic depression by person-specific symptom networks.

Psychotherapies are efficacious in the treatment of depression, albeit only with a moderate effect size. It is hoped that personalization of treatment can lead to better outcomes. The network theory of psychopathology offers a novel approach suggesting that symptom interactions as displayed in person-specific symptom networks could guide treatment planning for an individual patient. In a sample of 254 patients with chronic depression treated with either disorder-specific or non-specific psychotherapy for 48 weeks, we investigated if person-specific symptom networks predicted observer-rated depression severity at the end of treatment and one and two years after treatment termination. Person-specific symptom networks were constructed based on a time-varying multilevel vector autoregressive model of patient-rated symptom data. We used statistical parameters that describe the structure of these person-specific networks to predict therapy outcome. First, we used symptom centrality measures as predictors. Second, we used a machine learning approach to select parameters that describe the strength of pairwise symptom associations. We found that information on person-specific symptom networks strongly improved the accuracy of the prediction of observer-rated depression severity at treatment termination compared to common covariates recorded at baseline. This was also shown for predicting observer-rated depression severity at one- and two-year follow-up. Pairwise symptom associations were better predictors than symptom centrality parameters for depression severity at the end of therapy and one year later. Replication and external validation of our findings, methodological developments, and work on possible ways of implementation are needed before person-specific networks can be reliably used in clinical practice. Nevertheless, our results indicate that the structure of person-specific symptom networks can provide valuable information for the personalization of treatment for chronic depression.

A new modular neuroprosthesis suitable for hybrid FES-robot applications and tailored assistance.

BACKGROUND: To overcome the application limitations of functional electrical stimulation (FES), such as fatigue or nonlinear muscle response, the combination of neuroprosthetic systems with robotic devices has been evaluated, resulting in hybrid systems that have promising potential. However, current technology shows a lack of flexibility to adapt to the needs of any application, context or individual. The main objective of this study is the development of a new modular neuroprosthetic system suitable for hybrid FES-robot applications to meet these needs. METHODS: In this study, we conducted an analysis of the requirements for developing hybrid FES-robot systems and reviewed existing literature on similar systems. Building upon these insights, we developed a novel modular neuroprosthetic system tailored for hybrid applications. The system was specifically adapted for gait assistance, and a technological personalization process based on clinical criteria was devised. This process was used to generate different system configurations adjusted to four individuals with spinal cord injury or stroke. The effect of each system configuration on gait kinematic metrics was analyzed by using repeated measures ANOVA or Friedman's test. RESULTS: A modular NP system has been developed that is distinguished by its flexibility, scalability and personalization capabilities. With excellent connection characteristics, it can be effectively integrated with robotic devices. Its 3D design facilitates fitting both as a stand-alone system and in combination with other robotic devices. In addition, it meets rigorous requirements for safe use by incorporating appropriate safety protocols, and features appropriate battery autonomy, weight and dimensions. Different technological configurations adapted to the needs of each patient were obtained, which demonstrated an impact on the kinematic gait pattern comparable to that of other devices reported in the literature. CONCLUSIONS: The system met the identified technical requirements, showcasing advancements compared to systems reported in the literature. In addition, it demonstrated its versatility and capacity to be combined with robotic devices forming hybrids, adapting well to the gait application. Moreover, the personalization procedure proved to be useful in obtaining various system configurations tailored to the diverse needs of individuals.

A review of artificial intelligence methods enabled music-evoked EEG emotion recognition and their applications.

Music is an archaic form of emotional expression and arousal that can induce strong emotional experiences in listeners, which has important research and practical value in related fields such as emotion regulation. Among the various emotion recognition methods, the music-evoked emotion recognition method utilizing EEG signals provides real-time and direct brain response data, playing a crucial role in elucidating the neural mechanisms underlying music-induced emotions. Artificial intelligence technology has greatly facilitated the research on the recognition of music-evoked EEG emotions. AI algorithms have ushered in a new era for the extraction of characteristic frequency signals and the identification of novel feature signals. The robust computational capabilities of AI have provided fresh perspectives for the development of innovative quantitative models of emotions, tailored to various emotion recognition paradigms. The discourse surrounding AI algorithms in the context of emotional classification models is gaining momentum, with their applications in music therapy, neuroscience, and social activities increasingly coming under the spotlight. Through an in-depth analysis of the complete process of emotion recognition induced by music through electroencephalography (EEG) signals, we have systematically elucidated the influence of AI on pertinent research issues. This analysis offers a trove of innovative approaches that could pave the way for future research endeavors.

The good, the bad, and the GPT: Reviewing the impact of generative artificial intelligence on psychology.

This review explores the impact of Generative Artificial Intelligence (GenAI)-a technology capable of autonomously creating new content, ideas, or solutions by learning from extensive data-on psychology. GenAI is changing research methodologies, diagnostics, and treatments by enhancing diagnostic accuracy, personalizing therapeutic interventions, and providing deeper insights into cognitive processes. However, these advancements come with significant ethical concerns, including privacy, bias, and the risk of depersonalization in therapy. By focusing on the current capabilities of GenAI, this study aims to provide a balanced understanding and guide the ethical integration of AI into psychological practices and research. We argue that while GenAI presents profound opportunities, its integration must be approached cautiously using robust ethical frameworks.

Optimal control of combination immunotherapy for a virtual murine cohort in a glioblastoma-immune dynamics model.

The immune checkpoint inhibitor anti-PD-1, commonly used in cancer immunotherapy, has not been successful as a monotherapy for the highly aggressive brain cancer glioblastoma. However, when used in conjunction with a CC-chemokine receptor-2 (CCR2) antagonist, anti-PD-1 has shown efficacy in preclinical studies. In this paper, we aim to optimize treatment regimens for this combination immunotherapy using optimal control theory. We extend a treatment-free glioblastoma-immune dynamics ODE model to include interventions with anti-PD-1 and the CCR2 antagonist. An optimized regimen increases the survival of an average mouse from 32 days post-tumor implantation without treatment to 111 days with treatment. We scale this approach to a virtual murine cohort to evaluate mortality and quality of life concerns during treatment, and predict survival, tumor recurrence, or death after treatment. A parameter identifiability analysis identifies five parameters suitable for personalizing treatment within the virtual cohort. Sampling from these five practically identifiable parameters for the virtual murine cohort reveals that personalized, optimized regimens enhance survival: 84% of the virtual mice survive to day 100, compared to 60% survival in a previously studied experimental regimen. Subjects with high tumor growth rates and low T cell kill rates are identified as more likely to die during and after treatment due to their compromised immune systems and more aggressive tumors. Notably, the MDSC death rate emerges as a long-term predictor of either disease-free survival or death.

Personalized movie recommendations based on probabilistic linguistic sentiment and integrated DEMATEL-TODIM methods.

With the development of society, online reviews are increasingly becoming a crucial factor in decision-making. Especially for entertainment products such as movies, they are preferred for their affordability and high entertainment factor. Therefore, this paper proposes a movie recommendation model that considers user personalization using a probabilistic linguistic approach based on online reviews. Firstly, the method constructs a quantitative sentiment framework that transforms comments into a multi-granular probabilistic sentiment language. Secondly, we build the decision-making trial and evaluation laboratory (DEMATEL) method for probabilistic linguistic environments to explore interrelationships between product attributes, and improve the distance measure and score function to better integrate probabilistic linguistic information into DEMATEL weight calculations. Furthermore, to account for risk preferences, the model employs the extended TODIM (an acronym in Portuguese for interactive and multicriteria decision making) methodology to determine the ranking of alternatives. Finally, we design Douban movie ranking experiments to demonstrate the validity of the model. Compared with other methods, this paper incorporates the emotional tendency of movie attributes and user preference into the decision-making process leading to more reasonable results.

A genetic algorithm-based method to modulate the difficulty of serious games along consecutive robot-assisted therapy sessions.

BACKGROUND AND OBJECTIVE: One of the biggest challenges during neurorehabilitation therapies is finding an appropriate level of therapy intensity for each patient to ensure the recovery of movement of the affected limbs while maintaining motivation. Different studies have proposed adapting the difficulty of exercises based on psychophysiological state, based on success rate, or by modeling the user's skills. However, all studies propose solutions for a single session, requiring a calibration process before using it in each session. We propose a dynamic adaptation method that can be used during different rehabilitation sessions, without the need for recalibration between sessions. METHODS: The adaptation architecture is based on a genetic algorithm that aims to maintain a certain score level and to motivate the user to move. The method has been evaluated with two serious games for five sessions using a rehabilitation robot. A common initial evaluation was made for all the users involved in the study, and the game parameters that best suited each user from the previous session were introduced as the starting point of the next session. In addition, the desired score rate was lowered between sessions to increase the difficulty level. The psychophysiological state of the users was measured based on the Self-Assessment Manikin test, as well as different cardiorespiratory and galvanic skin response signals were analyzed. RESULTS: The adaptation architecture proposed can find those game parameters that maximize the user movement for both games. In one of the games, the score rate set for each session is followed with high fidelity. The degree of personalization in the games increases between sessions as the dispersion of the game parameters grows. The Self-Assessment Manikin test and the physiological signals results would indicate that the psychophysiological state remains equal between sessions despite an increase in game difficulty. CONCLUSIONS: The genetic algorithm-based game adaptation has proven efficacy in maximizing the therapy performance through the sessions without needing recalibration. It also can be concluded that the design of the game influences the adaptation performance. Additionally, adaptive game design facilitated by our method does not significantly impact players' emotional or physiological states.

Evaluation of additional resources used in therapist-assisted transdiagnostic internet-delivered cognitive behaviour therapy.

Background: In internet-delivered cognitive behavioural therapy (ICBT) programs, beyond standardized core ICBT lessons, brief additional resources are sometimes available to clients to address comorbid concerns or offer additional information/strategies. These resources remain understudied in terms of how they are selected and perceived by clients, as well as their relationship to satisfaction and outcomes. Methods: Among clients (N = 793) enrolled in a 5-lesson transdiagnostic ICBT course, we examined client use and perceptions of 18 additional resources at 8 weeks in terms of whether clients found resources informative (yes/no) and or helpful (yes/no). Resources elaborated on cognitive strategies (managing beliefs, risk calculation) or on managing specific problems (agricultural stress, alcohol misuse, anger, assertiveness, chronic conditions, communication, grief, health anxiety, motivation, pain, panic, postpartum depression/anxiety, PTSD, sleep, workplace accomodations, worry). Clients also completed symptom measures and ICBT satisfaction questions at 8 weeks. Results: Approximately 50 % (n = 398) of clients rated the resources and, on average, clients reported that 3.35 (SD = 3.34) resources were informative and 2.35 (SD = 2.52) resources were helpful as measured by direct questions developed for this study. Higher pre-treatment PTSD and GAD scores were related to a greater number of resources perceived as informative and or helpful. Rating more resources as informative and or helpful had a weak but positive association with ICBT satisfaction and depression, anxiety, PTSD and insomnia change scores. Limitations of the study include that 31 % (n = 245) did not respond to questions about use of resources and 18.9 % (n = 150) said they did not review resources. Conclusions: There is considerable use of diverse additional resources in ICBT in routine care. Associations suggest that clients are using resources to personalize treatment to their needs and these resources are associated with treatment satisfaction and outcomes. The correlational associations between symptoms and perceived helpfulness of resources can help inform personalization algorithms to optimize ICBT delivery for clients. Further research on how to match clients with, encourage use of, and maximize benefits of resources would be beneficial.

A Survey of Autonomous Vehicle Behaviors: Trajectory Planning Algorithms, Sensed Collision Risks, and User Expectations.

Autonomous vehicles are rapidly advancing and have the potential to revolutionize transportation in the future. This paper primarily focuses on vehicle motion trajectory planning algorithms, examining the methods for estimating collision risks based on sensed environmental information and approaches for achieving user-aligned trajectory planning results. It investigates the different categories of planning algorithms within the scope of local trajectory planning applications for autonomous driving, discussing and differentiating their properties in detail through a review of the recent studies. The risk estimation methods are classified and introduced based on their descriptions of the sensed collision risks in traffic environments and their integration with trajectory planning algorithms. Additionally, various user experience-oriented methods, which utilize human data to enhance the trajectory planning performance and generate human-like trajectories, are explored. The paper provides comparative analyses of these algorithms and methods from different perspectives, revealing the interconnections between these topics. The current challenges and future prospects of the trajectory planning tasks in autonomous vehicles are also discussed.

Towards new realities: implications of personalized online layers in our daily lives.

We are currently in a period of upheaval, as many new technologies are emerging that open up new possibilities to shape our everyday lives. Particularly, within the field of Personalized Human-Computer Interaction we observe high potential, but also challenges. In this article, we explore how an increasing amount of online services and tools not only further facilitates our lives, but also shapes our lives and how we perceive our environments. For this purpose, we adopt the metaphor of personalized 'online layers' and show how these layers are and will be interwoven with the lives that we live in the 'human layer' of the real world.

Discordances in patients' and physicians' perspectives on which contextual factors should be accounted for during smoking cessation.

BACKGROUND: Smoking cessation interventions requires attending to the circumstances and needs of individual patients. We aimed at highlighting the discordances between patients' and physicians' perspectives on contextual factors that should be considered during smoking cessation. METHODS: We identified 36 contextual factors identified that should be considered during smoking cessation using PubMed and interviewing general practitioners. Physicians recruited through social networks campaigns and smoker or former smoker patients from the ComPaRe cohort selected the factors they considered most relevant in two online paired comparison experiment. Bradley Terry Luce models estimated the ability of each factor (i.e. the probability to be preferred). We calculated the Pearson's correlation and the intraclass correlation coefficients for the contextual factor from each perspective and compared the ranking of the 10 contextual factors with the highest abilities. RESULTS: Seven hundred and ninety-three patients' and 795 physicians' perspectives estimated the ability (i.e., importance) of the contextual factors in 11 963 paired comparisons. We found a high correlation between physicians' and patients' perspectives of the contextual factors to be considered for smoking cessation (r = 0.76, P < 0.0001). However, the agreement between the abilities of contextual factors was poor (ICC = 0.42 [-0.10; 0.75]; P = 0.09). Fine-grain analysis of participants' answers revealed many discrepancies. For example, 40% factors ranked in the top 10 most important for physicians were not in patients' top 10 ranking. CONCLUSION: Our results highlight the importance of patient-centered care, the need to engage discussions about patients' values, beyond what is thought to be important, to avoid overlooking their real context.

Advantages of customization of osseointegrated implants in transfemoral amputees: a comparative analysis of surgical planning.

BACKGROUND: Commercially available osseointegrated devices for transfemoral amputees are limited in size and thus fail to meet the significant anatomical variability in the femoral medullary canal. This study aimed to develop a customized osseointegrated stem to better accommodate a variety of femoral anatomies in transfemoral amputees than off-the-shelf stems. Customization is expected to enhance cortical bone preservation and increase the stem-bone contact area, which are critical for the long-term stability and success of implants. METHODS: A customized stem (OsteoCustom) was designed based on the statistical shape variability of the medullary canal. The implantability of the OsteoCustom stem was tested via 70 computed tomography (CT) images of human femurs and compared to that of a commercial device (OFI-C) for two different resection levels. The evaluations included the volume of cortical bone removed and the percentage of stem-bone contact area for both resection levels. Statistical significance was analyzed using paired and unpaired t tests. RESULTS: The OsteoCustom stem could be virtually implanted in all 70 femurs, while the OFI-C was unsuitable in 19 cases due to insufficient cortical thickness after implantation, further emphasizing its adaptability to varying anatomical conditions. The OsteoCustom stem preserved a greater volume of cortical bone than did the OFI-C. In fact, 42% less bone was removed at the proximal resection level (3.15 cm³ vs. 5.42 cm³, p ≤ 0.0001), and 33% less at the distal resection level (2.25 cm³ vs. 3.39 cm³, p = 0.003). The stem-bone contact area was also greater for the OsteoCustom stem, particularly at the distal resection level, showing a 20% increase in contact area (52.3% vs. 32.2%, p = 0.002) compared to that of the OFI-C. CONCLUSIONS: The OsteoCustom stem performed better than the commercial stem by preserving more cortical bone and achieving a greater stem-bone contact area, especially at distal resection levels where the shape of the medullary canal exhibits more inter-subject variability. Optimal fit in the distal region is of paramount importance for ensuring the stability of osseointegrated implants. This study highlights the potential benefits of customized osseointegrated stems in accommodating a broader range of femoral anatomies, with enhanced fit in the medullary canal.

Influence of personality traits on generation Z consumers' click-through intentions towards personalized advertisements: A mixed-methods study.

Personalized social media advertisements (PSMAs) are developed by using consumers' personal information like names, demographic details, location, past buying history, and lifestyle interests to quickly grab consumers' attention within the cluttered space of digital advertisements. Generation Z consumers are highly connected to social media. Hence, this study attempts to understand how Generation Z consumers with different personality traits perceive personalized advertisements (PAs) on Facebook, and subsequently, how their perceived personalization influences their intention to click on PAs based on perceived usefulness and privacy concerns associated with those advertisements. The theoretical underpinning of this research is based on the self-congruency theory and privacy-calculus theory. An explanatory sequential mixed-methods design has been adopted, where a quantitative analysis (Study 1) is followed by a qualitative approach (Study 2). For Study 1, responses were collected from 324 Generation Z consumers through a structured questionnaire and the data was analyzed using the structural equation modeling technique to measure relationships among the constructs. Further, in Study 2, in-depth interviews were conducted with 15 Generation Z consumers, a purposively selected subset of informants from Study 1, to explore the potential causes of those relationships observed in Study 1. It has been found from the study that consumers' perception of PSMAs varies based on their personality traits. Consumers with dominant extraversion, conscientiousness, and neurotic personality traits perceive PSMAs positively whereas the openness to experience and agreeableness dominant consumers perceive those negatively. Positive perception of PSMAs among consumers increases the perceived usefulness of communications and subsequently improves the click-through intentions of the consumers. Generation Z Consumers' perception of PSMAs does not have any influence on their privacy concerns. Consumers' high privacy concerns reduce the click-through intention rate toward PSMAs. The study will help digital marketing managers to strategically deliver PSMAs, thereby enhancing the efficiency of their advertisements.