Fracture healing is stimulated by micromotion at the fracture site, whereby there exists an optimal amount of strain to promote secondary bone formation. Surgical plates used for fracture fixation are often evaluated for their biomechanical performance using benchtop studies, where success is based on overall construct stiffness and strength measures. Integration of fracture gap tracking to this assessment would provide crucial information about how plates support the various fragments present in comminuted fractures, to ensure there are appropriate levels of micromotion during early healing. The goal of this study was to configure an optical tracking system to quantify 3D interfragmentary motion to assess the stability (and corresponding healing potential) of comminuted fractures. An optical tracking system (OptiTrack, Natural Point Inc, Corvallis, OR) was mounted to a material testing machine (Instron 1567, Norwood, MA, USA), with an overall marker tracking accuracy of 0.05 mm. Marker clusters were constructed that could be affixed to individual bone fragments, and segment-fixed coordinate systems were developed. The interfragmentary motion was calculated by tracking the segments while under load and was resolved into compression-extraction and shear components. This technique was evaluated using two cadaveric distal tibia-fibula complexes with simulated intra-articular pilon fractures. Normal and shear strains were tracked during cyclic loading (for stiffness tests), and a wedge gap was also tracked to assess failure in an alternate clinically relevant mode. This technique will augment the utility of benchtop fracture studies by moving beyond total construct response and providing anatomically relevant data on interfragmentary motion, a valuable proxy for healing potential.

This letter explores the capability of AI, specifically OpenAI's ChatGPT, in interpreting human behavior and its potential implications for mental health care. Data were collected from the Reddit forum "AmItheAsshole" (AITA) to assess the congruence between AI's verdict and the collective human opinion on this platform. AITA, with its vast range of interpersonal situations, provides rich insights into human behavioral evaluation and perception. Two key research questions were addressed: the degree of alignment between ChatGPT's judgment and collective verdicts of Redditors, and the consistency of ChatGPT in evaluating the same AITA post repeatedly. The results exhibited a promising level of agreement between ChatGPT and human verdicts. It also demonstrated high consistency across repeated evaluations of the same posts. These findings hint at the significant potential of AI in mental health care provision, underscoring the importance of continued research and development in this field.

Arguably ChatGPT jeopardizes the integrity and validity of the academic publications instead of ethically facilitating them. ChatGPT can apparently fulfill a portion of one of the four authorship criteria set by the International Committee of Medical Journal Editors (ICMJE), i.e., "drafting." However, the authorship criteria by ICMJE must all be collectively met, not singly or partially. Many published manuscripts or preprints have credited ChatGPT by including it in the author byline, and the academic publishing enterprise seems to be unguided on how to handle such manuscripts. Interestingly, PLoS Digital Health removed ChatGPT off a paper which had ChatGPT listed initially in the author byline of the preprint version. Revised publishing policies are, thus, promptly required to guide a consistent stance regarding ChatGPT or similar artificial content generators. Publishing policies must accord among publishers, preprint servers ( https://asapbio.org/preprint-servers ), universities, and research institutions worldwide and across different disciplines. Ideally, considering any declaration of the contribution of ChatGPT to writing any scientific article should be recognized as publishing misconduct immediately and be retracted. Meanwhile, all parties involved in the scientific reporting and publishing must be educated about how ChatGPT fails to meet the essential authorship criteria, so that no author must submit a manuscript with ChatGPT contributing as a "co-author." Meanwhile, using ChatGPT for writing laboratory reports or short summaries of experiments may be acceptable, but not for academic publishing or formal scientific reporting.

Prompt engineering is a relatively new discipline that refers to the practice of developing and optimizing prompts to effectively utilize large language models, particularly in natural language processing tasks. However, not many writers and researchers are familiar about this discipline. Hence, in this paper, I aim to highlight the significance of prompt engineering for academic writers and researchers, particularly the fledgling, in the rapidly evolving world of artificial intelligence. I also discuss the concepts of prompt engineering, large language models, and the techniques and pitfalls of writing prompts. Here, I contend that by acquiring prompt engineering skills, academic writers can navigate the changing landscape and leverage large language models to enhance their writing process. As artificial intelligence continues to advance and penetrate the arena of academic writing, prompt engineering equips writers and researchers with the essential skills to effectively harness the power of language models. This enables them to confidently explore new opportunities, enhance their writing endeavors, and remain at the forefront of utilizing cutting-edge technologies in their academic pursuits.

Ligamentum nuchae is a highly elastic tissue commonly used to study the structure and mechanics of elastin. This study combines imaging, mechanical testing, and constitutive modeling to examine the structural organization of elastic and collagen fibers and their contributions to the nonlinear stress-strain behavior of the tissue. Rectangular samples of bovine ligamentum nuchae cut in both longitudinal and transverse directions were tested in uniaxial tension. Purified elastin samples were also obtained and tested. It was observed that the stress-stretch response of purified elastin tissue follows a similar curve as the intact tissue initially, but the intact tissue shows a significant stiffening behavior for stretches above 1.29 with collagen engagement. Multiphoton and histology images confirm the elastin-dominated bulk of ligamentum nuchae interspersed with small bundles of collagen fibrils and sporadic collagen-rich regions with cellular components and ground substance. A transversely isotropic constitutive model that considers the longitudinal organization of elastic and collagen fibers was developed to describe the mechanical behavior of both intact and purified elastin tissue under uniaxial tension. These findings shed light on the unique structural and mechanical roles of elastic and collagen fibers in tissue mechanics and may aid in future use of ligamentum nuchae in tissue grafting.

Computational models can be used to predict the onset and progression of knee osteoarthritis. Ensuring the transferability of these approaches among computational frameworks is urgent for their reliability. In this work, we assessed the transferability of a template-based modeling strategy, based on the finite element (FE) method, by implementing it on two different FE softwares and comparing their results and conclusions. For that, we simulated the knee joint cartilage biomechanics of 154 knees using healthy baseline conditions and predicted the degeneration that occurred after 8 years of follow-up. For comparisons, we grouped the knees using their Kellgren-Lawrence grade at the 8-year follow-up time and the simulated volume of cartilage tissue that exceeded age-dependent thresholds of maximum principal stress. We considered the medial compartment of the knee in the FE models and used ABAQUS and FEBio FE softwares for simulations. The two FE softwares detected different volumes of overstressed tissue in corresponding knee samples (p < 0.01). However, both programs correctly distinguished between the joints that remained healthy and those that developed severe osteoarthritis after the follow-up (AUC = 0.73). These results indicate that different software implementations of a template-based modeling method similarly classify future knee osteoarthritis grades, motivating further evaluations using simpler cartilage constitutive models and additional studies on the reproducibility of these modeling strategies.

At the fascinating intersection of artificial intelligence and medicine, ChatGPT morphs into a compact, personal digital physician. With a simple click, it furnishes an abundance of health-related information, initial medical consultations, and a plethora of disease management recommendations. Moreover, it stands at the ready to provide immediate mental health assistance in times of psychological distress. Yet, each innovation carries inherent challenges. As we embrace the conveniences proffered by ChatGPT, it is imperative that we grapple with associated issues such as data privacy, risk of misdiagnosis, complexities in human-machine interaction, and particular situations that elude its understanding. Let's probe further into this intriguing world, brimming with contention and prospects, and observe how ChatGPT traverses the landscape of digital health, uncovering the potential it holds for the future evolution of medical practice.

Since OpenAI (San Francisco, CA) released its generative AI chatbot, ChatGPT, we are on the cusp of technological transformation. The tool is capable of generating text according to the input that the user adds to it. Due to its ability to imitate human speech tone while extracting encyclopedic knowledge, ChatGPT can be a platform for personalized patient interaction. Thus, it has the potential to revolutionize the healthcare system. Our study aims to evaluate how ChatGPT can answer the queries of patients suffering from obstructive sleep apnea and aid in self-diagnosis. By analyzing symptoms and guiding patients' behavior toward prevention, ChatGPT can play a major role in avoiding serious health repercussions that develop in the later course of obstructive sleep apnea.

Humanoid robotics is characterized by constant developments, which are supported by several research facilities across the world. Humanoid robots are used in many different industries. In this setting, this letter, written by people, makes use of ChatGPT answers to examine how humanoid robots might be used in the medical industry, particularly in light of the COVID-19 pandemic and in future. Although humanoid robots can help with certain jobs, it is important to recognize the indispensable importance of human healthcare professionals who have knowledge, empathy, and the capacity for critical judgment. Although humanoid robots can complement healthcare initiatives, they shouldn't be viewed as a full-fledged replacement for human care.

This discussion paper aims to examine the potential benefits and limitations of using artificial intelligence (AI) chatbots in nursing practice, with a particular focus on the ChatGPT example. The study discusses how chatbots can serve as a valuable tool for nurses' continuing education, consultation, and information access. It is suggested that ChatGPT can contribute to enhancing nurses' knowledge and skill levels, providing rapid and accurate information, and improving time management. However, the potential risks and limitations of using AI chatbots have also been evaluated. The study highlights the possibility of negative impacts on the nurse-patient relationship due to chatbots' inadequacy in emotional and empathetic communication. Additionally, concerns about chatbots providing inaccurate or biased information and issues regarding data privacy are addressed. The review draws attention to the limited existing literature on the use of AI chatbots in nursing and emphasizes the need for expanding research in this area. Future studies are suggested to focus on identifying the necessary training and support resources for nurses to effectively utilize this technology. This study underscores an important ethical and professional point for nurses, reminding them not to overlook the significance of human touch and emotional connection while evaluating the advantages offered by technology.

The immersed boundary (IB) method is a mathematical framework for fluid-structure interaction problems (FSI) that was originally developed to simulate flows around heart valves. Direct comparison of FSI simulations around heart valves against experimental data is challenging, however, due to the difficulty of performing robust and effective simulations, the complications of modeling a specific physical experiment, and the need to acquire experimental data that is directly comparable to simulation data. Such comparators are a necessary precursor for further formal validation studies of FSI simulations involving heart valves. In this work, we performed physical experiments of flow through a pulmonary valve in an in vitro pulse duplicator, and measured the corresponding velocity field using 4D flow MRI (4-dimensional flow magnetic resonance imaging). We constructed a computer model of this pulmonary artery setup, including modeling valve geometry and material properties via a technique called design-based elasticity, and simulated flow through it with the IB method. The simulated flow fields showed excellent qualitative agreement with experiments, excellent agreement on integral metrics, and reasonable relative error in the entire flow domain and on slices of interest. These results illustrate how to construct a computational model of a physical experiment for use as a comparator.

As a recently popular large language model, Chatbot Generative Pre-trained Transformer (ChatGPT) is highly valued in the field of clinical medicine. Due to the limited understanding of the potential impact of ChatGPT on the manufacturing side of clinical medical devices, we aim to fill this gap through this article. We elucidate the classification of medical devices and explore the positive contributions of ChatGPT in various aspects of medical device design, optimization, and improvement. However, limitations such as the potential for misinterpretation of user intent, lack of personal experience, and the need for human supervision should be taken into consideration. Striking a balance between ChatGPT and human expertise can ensure the safety, quality, and compliance of medical devices. This work contributes to the advancement of ChatGPT in the medical device manufacturing industry and highlights the synergistic relationship between artificial intelligence and human involvement in healthcare.

This letter highlights a pressing issue regarding the absence of established editorial policies for the utilization of AI tools (e.g., ChatGPT) in the peer review process. The increasing adoption of AI tools in academic publishing necessitates the formulation of standardized guidelines to ensure fairness, transparency, and accountability. Without clear editorial policies, there is a threat of compromising the integrity of the peer review process and undermining the credibility of academic publications. Urgent attention is needed to address this gap and establish robust protocols that govern the use of AI tools in peer review.

Recently, the interest in AI-guided ChatGPT has increased day-to-day, and different applications have been explored, including the medical field. The publication number is also increasing. At the same time, people are trying to get medical information from this Chartbot. However, researchers found that ChatGPT also provides partly correct or false information. Therefore, in this article, we urge the researchers to develop an AI-enabled, next-generation, advanced ChatGPT or large language models (LLMs) so that people can get accurate and error-free medical information.

Porcine models in injury biomechanics research often involve measuring head or brain kinematics. Translation of data from porcine models to other biomechanical models requires geometric and inertial properties of the pig head and brain, and a translationally relevant anatomical coordinate system (ACS). In this study, the head and brain mass, center of mass (CoM), and mass moments of inertia (MoI) were characterized, and an ACS was proposed for the pre-adolescent domestic pig. Density-calibrated computed tomography scans were obtained for the heads of eleven Large White × Landrace pigs (18-48 kg) and were segmented. An ACS with a porcine-equivalent Frankfort plane was defined using externally palpable landmarks (right/left frontal process of the zygomatic bone and zygomatic process of the frontal bone). The head and brain constituted 7.80 ± 0.79% and 0.33 ± 0.08% of the body mass, respectively. The head and brain CoMs were primarily ventral and caudal to the ACS origin, respectively. The mean head and brain principal MoI (in the ACS with origin at respective CoM) ranged from 61.7 to 109.7 kg cm2, and 0.2 to 0.6 kg cm2, respectively. These data may aid the comparison of head and brain kinematics/kinetics data and the translation between porcine and human injury models.

Methods for statistically analyzing patient-specific data that vary both spatially and over time are currently either limited to summary statistics or require elaborate surface registration. We propose a new method, called correspondence-based network analysis, which leverages particle-based shape modeling to establish correspondence across a population and preserve patient-specific measurements and predictions through statistical analysis. Herein, we evaluated this method using three published datasets of the hip describing cortical bone thickness of the proximal femur, cartilage contact stress, and dynamic joint space between control and patient cohorts to evaluate activity- and group-based differences, as applicable, using traditional statistical parametric mapping (SPM) and our proposed spatially considerate correspondence-based network analysis approach. The network approach was insensitive to correspondence density, while the traditional application of SPM showed decreasing area of the region of significance with increasing correspondence density. In comparison to SPM, the network approach identified broader and more connected regions of significance for all three datasets. The correspondence-based network analysis approach identified differences between groups and activities without loss of subject and spatial specificity which could improve clinical interpretation of results.

Vitrification could enable long-term organ preservation, but only after loading high-concentration, potentially toxic cryoprotective agents (CPAs) by perfusion. In this paper, we combine a two-compartment Krogh cylinder model with a toxicity cost function to theoretically optimize the loading of CPA (VMP) in rat kidneys as a model system. First, based on kidney perfusion experiments, we systematically derived the parameters for a CPA transport loading model, including the following: Vb = 86.0% (ra = 3.86 μm), Lp = 1.5 × 10-14 m3/(N·s), ω = 7.0 × 10-13 mol/(N·s), σ = 0.10. Next, we measured the toxicity cost function model parameters as α = 3.12 and β = 9.39 × 10-6. Combining these models, we developed an improved kidney-loading protocol predicted to achieve vitrification while minimizing toxicity. The optimized protocol resulted in shorter exposure (25 min or 18.5% less) than the gold standard kidney-loading protocol for VMP, which had been developed based on decades of empirical practice. After testing both protocols on rat kidneys, we found comparable physical and biological outcomes. While we did not dramatically reduce toxicity, we did reduce the time. As our approach is now validated, it can be used on other organs lacking defined toxicity data to reduce CPA exposure time and provide a rapid path toward developing CPA perfusion protocols for other organs and CPAs.

Musculoskeletal models can uniquely estimate in vivo demands and injury risk. In this study, we aimed to compare muscle activations from subject-specific thoracolumbar spine OpenSim models with recorded muscle activity from electromyography (EMG) during five dynamic tasks. Specifically, 11 older adults (mean = 65 years, SD = 9) lifted a crate weighted to 10% of their body mass in axial rotation, 2-handed sagittal lift, 1-handed sagittal lift, and lateral bending, and simulated a window opening task. EMG measurements of back and abdominal muscles were directly compared to equivalent model-predicted activity for temporal similarity via maximum absolute normalized cross-correlation (MANCC) coefficients and for magnitude differences via root-mean-square errors (RMSE), across all combinations of participants, dynamic tasks, and muscle groups. We found that across most of the tasks the model reasonably predicted temporal behavior of back extensor muscles (median MANCC = 0.92 ± 0.07) but moderate temporal similarity was observed for abdominal muscles (median MANCC = 0.60 ± 0.20). Activation magnitude was comparable to previous modeling studies, and median RMSE was 0.18 ± 0.08 for back extensor muscles. Overall, these results indicate that our thoracolumbar spine model can be used to estimate subject-specific in vivo muscular activations for these dynamic lifting tasks.

This article provides a critical analysis of the application of the advanced language model, GPT-4, in generating surgical operative notes, with a focus on its use in ophthalmology as presented by Waisberg et al. The discussion underscores the inherent complexity and specificity of operative notes, the issue of accountability, and the potential data protection issues associated with the use of AI in healthcare. The letter emphasizes the need for a more comprehensive understanding of the complexities involved in applying AI in healthcare and calls for a more nuanced and responsible approach to the integration of AI in surgical documentation.

Aging is a known risk factor for Osteoarthritis (OA), however, relations between cartilage composition and aging remain largely unknown in understanding human OA. T2 imaging provides an approach to assess cartilage composition. Whether these T2 relaxation times in the joint contact region change with time during gait remain unexplored. The study purpose was to demonstrate a methodology for linking dynamic joint contact mechanics to cartilage composition as measured by T2 relaxometry. T2 relaxation times for unloaded cartilage were measured in a 3T General Electric magnetic resonance (MR) scanner in this preliminary study. High-speed biplanar video-radiography (HSBV) was captured for five 20-30-year-old and five 50-60-year-old participants with asymptomatic knees. By mapping the T2 cartilages to the dynamic contact regions, T2 values were averaged over the contact area at each measurement within the gait cycle. T2 values demonstrated a functional relationship across the gait cycle. There were no statistically significant differences between 20- and 30-year-old and 50-60-year-old participant T2 values at first force peak of the gait cycle in the medial femur (p = 1.00, U = 12) or in the medial tibia (p = 0.31, U = 7). In the medial and lateral femur in swing phase, the joint moved from a region of high T2 values at 75% of gait to a minimum at 85-95% of swing. The lateral femur and tibia demonstrated similar patterns to the medial compartments but were less pronounced. This research advances understanding of the linkage between cartilage contact and cartilage composition. The change from a high T2 value at ~ 75% of gait to a lower value near the initiation of terminal swing (90% gait) indicates that there are changes to T2 averages corresponding to changes in the contact region across the gait cycle. No differences were found between age groups for healthy participants. These preliminary findings provide interesting insights into the cartilage composition corresponding to dynamic cyclic motion and inform mechanisms of osteoarthritis.

Operative notes are an essential piece of documentation made by an ophthalmic team following ocular or ophthalmic surgery. While the advanced language processing capabilities of GPT-4 can be utilized to enhance the comprehension of natural language in healthcare applications, the utilization of GPT-4 in writing operative notes is not explicitly referenced in the current literature. In this paper, we describe the potential applications for GPT-4 to write ophthalmic operative notes. It is imperative to acknowledge that despite the impressive capabilities exhibited by GPT-4, it remains constrained by certain limitations, and therefore, ought to be employed in tandem with human expertise and subjected to critical evaluation.

The nursing field has undergone notable changes over time and is projected to undergo further modifications in the future, owing to the advent of sophisticated technologies and growing healthcare needs. The advent of ChatGPT, an AI-powered language model, is expected to exert a significant influence on the nursing profession, specifically in the domains of patient care and instruction. The present article delves into the ramifications of ChatGPT within the nursing domain and accentuates its capacity and constraints to transform the discipline.

Artificial Intelligence (AI) has proven to be an effective tool in predicting, detecting, and mitigating the spread of infectious diseases, especially during the COVID-19 pandemic. The technology is increasingly playing a role in preventing future health crises by predicting outbreaks, identifying high-risk areas, and aiding in vaccine development. AI can track and trace infected individuals and identify potential hotspots, help reduce the spread of infectious diseases, and monitor patient symptoms, enabling healthcare professionals to provide effective treatment.

Joint loading may affect the development of osteoarthritis, but patient-specific load estimation requires cumbersome motion laboratory equipment. This reliance could be eliminated using artificial neural networks (ANNs) to predict loading from simple input predictors. We used subject-specific musculoskeletal simulations to estimate knee joint contact forces for 290 subjects during over 5000 stance phases of walking and then extracted compartmental and total joint loading maxima from the first and second peaks of the stance phase. We then trained ANN models to predict the loading maxima from predictors that can be measured without motion laboratory equipment (subject mass, height, age, gender, knee abduction-adduction angle, and walking speed). When compared to the target data, our trained models had NRMSEs (RMSEs normalized to the mean of the response variable) between 0.14 and 0.42 and Pearson correlation coefficients between 0.42 and 0.84. The loading maxima were predicted most accurately using the models trained with all predictors. We demonstrated that prediction of knee joint loading maxima may be possible without laboratory-measured motion capture data. This is a promising step in facilitating knee joint loading predictions in simple environments, such as a physician's appointment. In future, the rapid measurement and analysis setup could be utilized to guide patients in rehabilitation to slow development of joint disorders, such as osteoarthritis.

The emergence of new technologies continues to break barriers and transform the way we perceive and interact with the world. In this scientific article, we explore the potential impact of the new Apple XR headset on revolutionizing accessibility for individuals with visual deficits. With its rumored exceptional 4-K displays per eye and 5000 nits of brightness, this headset has the potential to enhance the visual experience and provide a new level of accessibility for users with visual impairments. We delve into the technical specifications, discuss the implications for accessibility, and envision how this groundbreaking technology could open up new possibilities for individuals with visual deficits.

ChatGPT, an advanced language generation model developed by OpenAI, has the potential to revolutionize healthcare delivery and support for individuals with various conditions, including Down syndrome. This article explores the applications of ChatGPT in assisting children with Down syndrome, highlighting the benefits it can bring to their education, social interaction, and overall well-being. While acknowledging the challenges and limitations, we examine how ChatGPT can be utilized as a valuable tool in enhancing the lives of these children, promoting their cognitive development, and supporting their unique needs.

Recently, Chatbot Generative Pre-trained Transformer (ChatGPT) is recognized as a promising clinical decision support system (CDSS) in the medical field owing to its advanced text analysis capabilities and interactive design. However, ChatGPT primarily focuses on learning text semantics rather than learning complex data structures and conducting real-time data analysis, which typically necessitate the development of intelligent CDSS employing specialized machine learning algorithms. Although ChatGPT cannot directly execute specific algorithms, it aids in algorithm design for intelligent CDSS at the textual level. In this study, besides discussing the types of CDSS and their relationship with ChatGPT, we mainly investigate the benefits and drawbacks of employing ChatGPT as an auxiliary design tool for intelligent CDSS. Our findings indicate that by collaborating with human expertise, ChatGPT has the potential to revolutionize the development of robust and effective intelligent CDSS.

Worldwide metabolic syndrome (MeTS) rates have been alarmingly increased in the last decades. The use of Chat GPT technology allows for the provision of individualized guidance on MeTS-related health issues like dietary restriction programs, nutritional plans, and exercise regimens/protocols. The use of Chat GPT technology in providing MeTS patients with health advices may be limited by continuous need for high-speed internet sources or advanced/sophisticated computing technologies, providing incorrect/harmful medical/lifestyle advice to patients, and concerns around the security/privacy of MeTS patients' information.

Many artificial intelligence (AI) algorithms have been developed for medical practice, but few have led to clinically used products. The recent hype of ChatGPT shows us that simple, user-friendly interfaces are one major factor in the applications' popularity. The majority of AI-based applications in clinical practice are still far from simple-to-use applications with user-friendly interfaces. Therefore, simplifying operations is one key to AI-based medical applications' success.

We may lessen the detrimental effects of global warming on human thought processes by reducing greenhouse gas emissions, encouraging sustainability, and giving adaption measures top priority. The purpose of the letter is to draw attention to the necessity of net-zero energy buildings (NZEB) in academic institutions in order to reduce academic stress, promote well-being, and improve cognitive functions. While some levels of stress might be advantageous, excessive and mismanaged stress can be detrimental to students' well-being. To foster a healthy academic atmosphere, it is essential to offer resources, support networks, and stress-reduction techniques. As human authors, we thoroughly edited ChatGPT's responses to create this letter.

Osteoarthritis degenerates cartilage and impairs joint function. Early intervention opportunities are missed as current diagnostic methods are insensitive to early tissue degeneration. We investigated the capability of visible light-near-infrared spectroscopy (Vis-NIRS) to differentiate normal human cartilage from early osteoarthritic one. Vis-NIRS spectra, biomechanical properties and the state of osteoarthritis (OARSI grade) were quantified from osteochondral samples harvested from different anatomical sites of human cadaver knees. Two support vector machines (SVM) classifiers were developed based on the Vis-NIRS spectra and OARSI scores. The first classifier was designed to distinguish normal (OARSI: 0-1) from general osteoarthritic cartilage (OARSI: 2-5) to check the general suitability of the approach yielding an average accuracy of 75% (AUC = 0.77). Then, the second classifier was designed to distinguish normal from early osteoarthritic cartilage (OARSI: 2-3) yielding an average accuracy of 71% (AUC = 0.73). Important wavelength regions for differentiating normal from early osteoarthritic cartilage were related to collagen organization (wavelength region: 400-600 nm), collagen content (1000-1300 nm) and proteoglycan content (1600-1850 nm). The findings suggest that Vis-NIRS allows objective differentiation of normal and early osteoarthritic tissue, e.g., during arthroscopic repair surgeries.

When a cell or tissue is exposed to a pulsed electric field (100-1000 V/cm), the cellular membrane permeabilizes for biomolecules that cannot pass an intact cellular membrane. During this electropermeabilization (EP), plasmid deoxyribonucleic acid sequences encoding therapeutic or regulatory genes can enter the cell, which is called gene electrotransfer (GET). GET using micro-/nano technology provides higher spatial resolution and operates with lower voltage amplitudes compared to conventional bulk EP. Microelectrode arrays (MEAs), which are usually used for the recording and stimulation of neuronal signals, can be utilized for GET as well. In this study, we developed a specialized MEA for local EP of adherent cells. Our manufacturing process provides a most flexible electrode and substrate material selection. We used electrochemical impedance spectroscopy to characterize the impedance of the MEAs and the impact of an adherent cellular layer. We verified the local EP functionality of the MEAs by loading a fluorophore dye into human embryonic kidney 293T cells. Finally, we demonstrated a GET with a subsequent green fluorescent protein expression by the cells. Our experiments prove that a high spatial resolution of GET can be obtained using MEAs.

Grip strength loss in extended and flexed wrist postures has been explained by reduced force-generating capacities of extrinsic finger flexor resulting from non-optimal length, owing to the force-length relationship. Recent works suggested that other muscles, especially wrist extensors, participate in this grip strength loss. The objective of this study was to clarify the role of the force-length relationship in finger force production. 18 participants performed maximal isometric finger force production during pinch grip (Pinch) and four-finger pressing (Press) tasks in four different wrist postures (extended, flexed, neutral, spontaneous). The maximum finger force (MFF), finger and wrist joint angles, as well as activation of four muscles were determined using dynamometry, motion capture, and electromyography. The force and length of the four muscles were estimated from joint angles and muscle activation using a musculoskeletal model. MFF decreased for flexed wrist during Pinch but remained stable across wrist postures during Press. The results suggested that the loss of pinch grip force in deviated wrist posture is partially related to force-length relationship of finger extensors. In opposition, MFF during Press was not influenced by the modulation of muscle capacities but was probably first limited by mechanical and neural factors related to finger interdependence.

Large Language Models (LLMs) such as ChatGPT and Bard have emerged as groundbreaking interactive chatbots, capturing significant attention and transforming the biomedical research landscape. These powerful tools offer immense potential for advancing scientific inquiry, but they also present challenges and pitfalls. Leveraging large language models, researchers can streamline literature reviews, summarize complex findings, and even generate novel hypotheses, enabling the exploration of uncharted scientific territories. However, the inherent risk of misinformation and misleading interpretations underscores the critical importance of rigorous validation and verification processes. This article provides a comprehensive overview of the current landscape and delves into the opportunities and pitfalls associated with employing LLMs in biomedical research. Furthermore, it sheds light on strategies to enhance the utility of LLMs in biomedical research, offering recommendations to ensure their responsible and effective implementation in this domain. The findings presented in this article contribute to the advancement of biomedical engineering by harnessing the potential of LLMs while addressing their limitations.

OBJECTIVE: To differentiate healthy from artificially degraded articular cartilage and estimate its structural, compositional, and functional properties using Raman spectroscopy (RS). DESIGN: Visually normal bovine patellae (n = 12) were used in this study. Osteochondral plugs (n = 60) were prepared and artificially degraded either enzymatically (via Collagenase D or Trypsin) or mechanically (via impact loading or surface abrasion) to induce mild to severe cartilage damage; additionally, control plugs were prepared (n = 12). Raman spectra were acquired from the samples before and after artificial degradation. Afterwards, reference biomechanical properties, proteoglycan (PG) content, collagen orientation, and zonal (%) thickness of the samples were measured. Machine learning models (classifiers and regressors) were then developed to discriminate healthy from degraded cartilage based on their Raman spectra and to predict the aforementioned reference properties. RESULTS: The classifiers accurately categorized healthy and degraded samples (accuracy = 86%), and successfully discerned moderate from severely degraded samples (accuracy = 90%). On the other hand, the regression models estimated cartilage biomechanical properties with reasonable error (≤ 24%), with the lowest error observed in the prediction of instantaneous modulus (12%). With zonal properties, the lowest prediction errors were observed in the deep zone, i.e., PG content (14%), collagen orientation (29%), and zonal thickness (9%). CONCLUSION: RS is capable of discriminating between healthy and damaged cartilage, and can estimate tissue properties with reasonable errors. These findings demonstrate the clinical potential of RS.

Pulse wave velocity (PWV) is a key, independent risk factor for future cardiovascular events. The Moens-Korteweg equation describes the relation between PWV and the stiffness of arterial tissue with an assumption of isotopic linear elastic property of the arterial wall. However, the arterial tissue exhibits highly nonlinear and anisotropic mechanical behaviors. There is a limited study regarding the effect of arterial nonlinear and anisotropic properties on the PWV. In this study, we investigated the impact of the arterial nonlinear hyperelastic properties on the PWV, based on our recently developed unified-fiber-distribution (UFD) model. The UFD model considers the fibers (embedded in the matrix of the tissue) as a unified distribution, which expects to be more physically consistent with the real fiber distribution than existing models that separate the fiber distribution into two/several fiber families. With the UFD model, we fitted the measured relation between the PWV and blood pressure which obtained a good accuracy. We also modeled the aging effect on the PWV based on observations that the stiffening of arterial tissue increases with aging, and the results agree well with experimental data. In addition, we did parameter studies on the dependence of the PWV on the arterial properties of fiber initial stiffness, fiber distribution, and matrix stiffness. The results indicate the PWV increases with increasing overall fiber component in the circumferential direction. The dependences of the PWV on the fiber initial stiffness, and matrix stiffness are not monotonic and change with different blood pressure. The results of this study could provide new insights into arterial property changes and disease information from the clinical measured PWV data.

Mechanical loading has been described as having the potential to affect bone growth. In order to experimentally study the potential clinical applications of mechanical loading as a novel treatment to locally modulate bone growth, there is a need to develop a portable mechanical loading device enabling studies in small bones. Existing devices are bulky and challenging to transfer within and between laboratories and animal facilities, and they do not offer user-friendly mechanical testing across both ex vivo cultured small bones and in vivo animal models. To address this, we developed a portable loading device comprised of a linear actuator fixed within a stainless-steel frame equipped with suitable structures and interfaces. The actuator, along with the supplied control system, can achieve high-precision force control within the desired force and frequency range, allowing various load application scenarios. To validate the functionality of this new device, proof-of-concept studies were performed in ex vivo cultured rat bones of varying sizes. First, very small fetal metatarsal bones were microdissected and exposed to 0.4 N loading applied at 0.77 Hz for 30 s. When bone lengths were measured after 5 days in culture, loaded bones had grown less than unloaded controls (p < 0.05). Next, fetal rat femur bones were periodically exposed to 0.4 N loading at 0.77 Hz while being cultured ex vivo for 12 days. Interestingly, this loading regimen had the opposite effect on bone growth, i.e., loaded femur bones grew significantly more than unloaded controls (p < 0.001). These findings suggest that complex relationships between longitudinal bone growth and mechanical loading can be determined using this device. We conclude that our new portable mechanical loading device allows experimental studies in small bones of varying sizes, which may facilitate further preclinical studies exploring the potential clinical applications of mechanical loading.

Histology is an essential step to visualize and analyze the microstructure of any biological tissue; however, histological processing is often irreversible, and histological samples are unable to be imaged or tested further. In this work, a novel non-destructive protocol is proposed for morphological analysis of skeletal muscles, combining Optical Coherence Tomography (OCT) imaging with Tissue Clearing. Imaging combining OCT and Propylene Glycol (PG) as a tissue-clearing agent, was performed on rat tail and extensor digitorum longus (EDL) muscle. The results show that the extracellular matrix morphology of skeletal muscles, including muscular fibers and the whole microstructure architecture were clearly identified. PG improved OCT imaging as measured by image quality metric Contrast Per Pixel CPP (increases by 3.9%), Naturalness Image Quality Evaluator NIQE (decreases by 23%), and Volume of Interest VOI size (higher for CPP and lower for NIQE values). The tendon microstructure was observed with less precision, as collagen fibers could not be clearly detected. The reversibility of the optical effects of the PG on the immersed tissue (in a Phosphate-Buffered Saline solution) was studied comparing native and rehydrated OCT image acquisition from a single EDL sample. Optical properties and microstructure visibility (CPP and NIQE) have been recovered to 99% of the native sample values. Moreover, clearing process caused shrinkage of the tissue recovered to 86% of the original width. Future work will aim to employ the proposed experimental protocol to identify the local mechanical properties of biological tissues.

Wireless communication in medical applications improves patient monitoring, care coordination, early disease detection, and patient empowerment. It improves healthcare and patient outcomes. The design and configuration of a solar-powered emergency battery backup system for 5G telecommunication base stations, including medical applications, may vary depending on local climate, power requirements, and resources. In this connection, uninterrupted power supply to the base stations become crucial. The author utilizes the ChatGPT-AI features and prepared this comprehensive letter for realizing the role of sustainable practices towards climatic changes.

This study aimed to develop and validate a novel flexion axis concept by calculating the points on femoral condyles that could maintain constant heights during knee flexion. Twenty-two knees of 22 healthy subjects were investigated when performing a weightbearing single leg lunge. The knee positions were captured using a validated dual fluoroscopic image system. The points on sagittal planes of the femoral condyles that had minimal changes in heights from the tibial plane along the flexion path were calculated. It was found that the points do formulate a medial-lateral flexion axis that was defined as the iso-height axis (IHA). The six degrees of freedom (6DOF) kinematics data calculated using the IHA were compared with those calculated using the conventional transepicondylar axis and geometrical center axis. The IHA measured minimal changes in proximal-distal translations and varus-valgus rotations along the flexion path, indicating that the IHA may have interesting clinical implications. Therefore, identifying the IHA could provide an alternative physiological reference for improvement of contemporary knee surgeries, such as ligament reconstruction and knee replacement surgeries that are aimed to reproduce normal knee kinematics and medial/lateral soft tissue tensions during knee flexion.

Type I thyroplasty is widely used to improve voice production in patients affected by unilateral vocal fold paralysis. Almost two-thirds of laryngologists report using Silastic® implants to medialize the vocal fold, with implant size, shape, and location determined experientially. However, post-surgical complications arising from this procedure (extrusion, migration, resizing) necessitate revision in 4.5-16% of patients. To improve initial surgical outcomes, we have developed a subject-specific modeling tool, PhonoSim, which uses model reconstruction from MRI scans to predict the optimal implantation location. Eleven vocal fold sample sides from eight larynges of New Zealand white rabbits were randomized to two groups: PhonoSim informed (n = 6), and control (no model guidance, n = 5). Larynges were scanned ex vivo in the abducted configuration using a vertical-bore 11.7 T microimaging system, and images were used for subject-specific modeling. The PhonoSim tool simulated vocal fold adduction for multiple implant location placements to evaluate vocal fold adduction at the medial surface. The best implant placement coordinates were output for the 6 samples in the PhonoSim group. Control placements were determined by the same surgeon based on anatomical landmarks. Post-surgical MRI scans were performed for all samples to evaluate medialization in implanted vocal folds. Results show that PhonoSim-guided implantation achieved higher vocal fold medialization relative to controls (28 to 55% vs. - 29 to 39% respectively, in the glottal area reduction), suggesting that this tool has the potential to improve outcomes and revision rates for type I thyroplasty.