Currently, there are no methods or tools available in clinical practice for classifying future knee osteoarthritis (KOA). In this study, we aimed to fill this gap by classifying future KOA into three severity grades: KL01 (healthy), KL2 (moderate), and KL34 (severe) based on the Kellgren-Lawrance scale. Due to the complex nature of multiclass classification, we used a two-stage method, which separates the classification task into two binary classifications (KL01 vs. KL234 in the first stage and KL2 vs. KL34 in the second stage). Our machine learning (ML) model used two Balanced Random Forest algorithms and was trained with gender, age, height, weight, and quantitative knee morphology obtained from magnetic resonance imaging. Our training dataset comprised longitudinal 8-year follow-up data of 1213 knees from the Osteoarthritis Initiative. Through extensive experimentation with various feature combinations, we identified KL baseline and weight as the most essential features, while gender surprisingly proved to be one of the least influential feature. Our best classification model generated a weighted F1 score of 79.0% and a balanced accuracy of 65.9%. The area under the receiver operating characteristic curve was 83.0% for healthy (KL01) versus moderate (KL2) or severe (KL34) KOA patients and 86.6% for moderate (KL2) versus severe (KL34) KOA patients. We found a statistically significant difference in performance between our two-stage classification model and the traditional single-stage classification model. These findings demonstrate the encouraging results of our two-stage classification model for multiclass KOA severity classification, suggesting its potential application in clinical settings in future.

Evaluating the bone-implant interface (BII) properties of osseointegrated transfemoral (TFA) implants is important for early failure detection and prescribing loads during rehabilitation. The objective of this work is to derive and validate a 1D finite element (FE) model of the Osseointegrated Prosthetic Limb (OPL) TFA system that can: (1) model its dynamic behaviour and (2) extract the BII properties. The model was validated by: (1) comparing the 1D FE formulation to the analytical and 3D FE solutions for a simplifiedcylinder, (2) comparing the vibration modes of the actual TFA geometry using 1D and 3D FE models, and (3) evaluating the BII properties for three extreme conditions (LOW, INTERMEDIATE, and HIGH) generated using 3D FE and experimental (where the implant was embedded, using different adhesives, in synthetic femurs) signals for additional validation. The modes predicted by the 1D FE model converged to the analytical and the 3D FE solutions for the cylinder. The 1D model also matched the 3D FE solution with a maximum frequency difference of 2.02% for the TFA geometry. Finally, the 1D model extracted the BII stiffness and the system's damping properties for the three conditions generated using the 3DFE simulations and the experimental INTERMEDIATE and HIGH signals. The agreement between the 1D FE and the 3D FE solutions for the TFA geometry indicates that the 1D model captures the system's dynamic behaviour. Distinguishing between the different BII conditions demonstrates the 1D model's potential usefor the non-invasive clinical evaluation of the TFA BII properties.

Sporting helmets contain force attenuating materials which reduce traumatic head injury risk and may influence sport-related concussion (SRC) sequelae. The purpose of this study was to examine the association of sport helmet status with SRC-clinical presentation and recovery trajectories in men's collegiate athletes. Sport helmet status was based on the nature of sports being either helmeted/non-helmeted. 1070 SRCs in helmeted (HELM) sports (Men's-Football, Ice Hockey, and Lacrosse), and 399 SRCs in non-helmeted (NOHELM) sports (Men's-Basketball, Cheerleading, Cross Country/Track & Field, Diving, Gymnastics, Soccer, Swimming, Tennis, and Volleyball) were analyzed. Multivariable negative binomial regression models analyzed associations between sport helmet status and post-injury cognition, balance, and symptom severity, adjusting for covariate effects (SRC history, loss of consciousness, anterograde/retrograde amnesia, event type). Kaplan-Meier curves evaluated median days to: initiation of return to play (iRTP) protocol, and unrestricted RTP (URTP) by sport helmet status. Log-rank tests were used to evaluate differential iRTP/URTP between groups. Two independent multivariable Weibull accelerated failure time models were used to examine differential iRTP and URTP between groups, after adjusting for aforementioned covariates and symptom severity score. Overall, the median days to iRTP and URTP was 6.3 and 12.0, respectively, and was comparable across NOHELM- and HELM-SRCs. Post-injury symptom severity was lower (Score Ratio 0.90, 95%CI 0.82, 0.98), and cognitive test performance was higher (Score Ratio 1.03, 95%CI 1.02, 1.05) in NOHELM-compared to HELM-SRCs. Estimated time spent recovering to iRTP/URTP was comparable between sport helmet status groups. Findings suggest that the grouping of sports into helmeted and non-helmeted show slight differences in clinical presentation but not recovery.

The field of 3D bioprinting is rapidly emerging within the realm of regenerative medicine, offering significant potential in dealing with the issue of organ shortages. Despite being in its early stages, it has the potential to replicate tissue structures accurately, providing new potential solutions for reconstructive surgery. This review explores the diverse applications of 3D bioprinting in regenerative medicine, pharmaceuticals, and the food industry, specifically focusing on ear, skin, and bone tissues due to their unique challenges and implications in the field. Significant progress has been made in cartilage and bone scaffold fabrication in ear reconstruction, yet challenges in functional maturation persist. Recent advancements highlight the potential for patient-specific ear substitutes, emphasizing the need for extensive clinical trials. In skin regeneration, 3D bioprinting addresses limitations in existing models, offering opportunities for improved wound healing and realistic skin models. While challenges exist, progress in biomaterials and in-situ bioprinting holds promise. In bone regeneration, 3D bioprinting presents personalized solutions for defects, but scaffold design refinement and addressing regulatory and ethical considerations are crucial. The transformative potential of 3D bioprinting in the field of medicine holds the promise of redefining therapeutic approaches and delivering personalized treatments and functional tissues. Interdisciplinary collaboration is essential for fully realizing the capabilities of 3D bioprinting. This review provides a detailed analysis of current methodologies, challenges, and prospects in 3D bioprinting for ear, skin, and bone tissue regeneration.

Individual facial soft tissue properties are necessary for creating individualized finite element (FE) models to evaluate medical devices such as continuous positive airway pressure (CPAP) masks. There are no standard tools available to measure facial soft tissue elastic moduli, and techniques in literature require advanced equipment or custom parts to replicate. We propose a simple and inexpensive soft tissue measurement (STM) indenter device to estimate facial soft tissue elasticity at five sites: chin, cheek near lip, below cheekbone, cheekbone, and cheek. The STM device consists of a probe with a linear actuator and force sensor, an adjustment system for probe orientation, a head support frame, and a controller. The device was validated on six ballistics gel samples and then tested on 28 subjects. Soft tissue thickness was also collected for each subject using ultrasound. Thickness and elastic modulus measurements were successfully collected for all subjects. The mean elastic modulus for each site is Ec = 53.04 ± 20.97kPa for the chin, El = 16.33 ± 8.37kPa for the cheek near lip, Ebc = 27.09 ± 11.38kPa for below cheekbone, Ecb = 64.79 ± 17.12kPa for the cheekbone, and Ech = 16.20 ± 5.09kPa for the cheek. The thickness and elastic modulus values are in the range of previously reported values. One subject's measured soft tissue elastic moduli and thickness were used to evaluate custom-fit CPAP mask fit in comparison to a model of that subject with arbitrary elastic moduli and thickness. The model with measured values more closely resembles in vivo leakage results. Overall, the STM provides a first estimate of facial soft tissue elasticity and is affordable and easy to build with mostly off-the-shelf parts. These values can be used to create personalized FE models to evaluate custom-fit CPAP masks.

AI shaming refers to the practice of criticizing or looking down on individuals or organizations for using AI to generate content or perform tasks. AI shaming has emerged as a recent phenomenon in academia. This paper examines the characteristics, causes, and effects of AI shaming on academic writers and researchers. AI shaming often involves dismissing the validity or authenticity of AI-assisted work, suggesting that using AI is deceitful, lazy, or less valuable than human-only efforts. The paper identifies various profiles of individuals who engage in AI shaming, including traditionalists, technophobes, and elitists, and explores their motivations. The effects of AI shaming are multifaceted, ranging from inhibited technology adoption and stifled innovation to increased stress among researchers and missed opportunities for efficiency. These consequences may hinder academic progress and limit the potential benefits of AI in research and scholarship. Despite these challenges, the paper argues that academic writers and researchers should not be ashamed of using AI when done responsibly and ethically. By embracing AI as a tool to augment human capabilities and by being transparent about its use, academic writers and researchers can lead the way in demonstrating responsible AI integration.

Early diagnosis of kidney disease remains an unmet clinical challenge, preventing timely and effective intervention. Diabetes and hypertension are two main causes of kidney disease, can often appear together, and can only be distinguished by invasive biopsy. In this study, we developed a modelling approach to simulate blood velocity, volumetric flow rate, and pressure wave propagation in arterial networks of ageing, diabetic, and hypertensive virtual populations. The model was validated by comparing our predictions for pressure, volumetric flow rate and waveform-derived indexes with in vivo data on ageing populations from the literature. The model simulated the effects of kidney disease, and was calibrated to align quantitatively with in vivo data on diabetic and hypertensive nephropathy from the literature. Our study identified some potential biomarkers extracted from renal blood flow rate and flow pulsatility. For typical patient age groups, resistive index values were 0.69 (SD 0.05) and 0.74 (SD 0.02) in the early and severe stages of diabetic nephropathy, respectively. Similar trends were observed in the same stages of hypertensive nephropathy, with a range from 0.65 (SD 0.07) to 0.73 (SD 0.05), respectively. Mean renal blood flow rate through a single diseased kidney ranged from 329 (SD 40, early) to 317 (SD 38, severe) ml/min in diabetic nephropathy and 443 (SD 54, early) to 388 (SD 47, severe) ml/min in hypertensive nephropathy, showing potential as a biomarker for early diagnosis of kidney disease. This modelling approach demonstrated its potential application in informing biomarker identification and facilitating the setup of clinical trials.

As full-scale detailed hemodynamic simulations of the entire vasculature are not feasible, numerical analysis should be focused on specific regions of the cardiovascular system, which requires the identification of lumped parameters to represent the patient behavior outside the simulated computational domain. We present a novel technique for estimating cardiovascular model parameters using gappy Proper Orthogonal Decomposition (g-POD). A POD basis is constructed with FSI simulations for different values of the lumped model parameters, and a linear operator is applied to retain information that can be compared to the available patient measurements. Then, the POD coefficients of the reconstructed solution are computed either by projecting patient measurements or by solving a minimization problem with constraints. The POD reconstruction is then used to estimate the model parameters. In the first test case, the parameter values of a 3-element Windkessel model are approximated using artificial patient measurements, obtaining a relative error of less than 4.2%. In the second case, 4 sets of 3-element Windkessel are approximated in a patient's aorta geometry, resulting in an error of less than 8% for the flow and less than 5% for the pressure. The method shows accurate results even with noisy patient data. It automatically calculates the delay between measurements and simulations and has flexibility in the types of patient measurements that can handle (at specific points, spatial or time averaged). The method is easy to implement and can be used in simulations performed in general-purpose FSI software.

Endometrial cancer is the most common gynecological cancer in the developed world. However, the accuracy of current diagnostic methods is still unsatisfactory and time-consuming. Here, we presented an alternate approach to monitoring the progression of endometrial cancer via multiphoton microscopy imaging and analysis of collagen, which is often overlooked in current endometrial cancer diagnosis protocols but can offer a crucial signature in cancer biology. Multiphoton microscopy (MPM) based on the second-harmonic generation and two-photon excited fluorescence was introduced to visualize the microenvironment of endometrium in normal, hyperplasia without atypia, atypical hyperplasia, and endometrial cancer specimens. Furthermore, automatic image analysis based on the MPM image processing algorithm was used to quantify the differences in the collagen morphological features among them. MPM enables the visualization of the morphological details and alterations of the glands in the development process of endometrial cancer, including irregular changes in the structure of the gland, increased ratio of the gland to the interstitium, and atypical changes in the glandular epithelial cells. Moreover, the destructed basement membrane caused by gland proliferation and fusion is clearly shown in SHG images, which is a key feature for identifying endometrial cancer progression. Quantitative analysis reveals that the formation of endometrial cancer is accompanied by an increase in collagen fiber length and width, a progressive linearization and loosening of interstitial collagen, and a more random arrangement of interstitial collagen. Observation and quantitative analysis of interstitial collagen provide invaluable information in monitoring the progression of endometrial cancer. Label-free multiphoton imaging reported here has the potential to become an in situ histological tool for effective and accurate early diagnosis and detection of malignant lesions in endometrial cancer.