Despite falls accounting for the greatest number of fatal and non-fatal work-related traumatic brain injuries, current standards do not evaluate safety helmets under impact conditions representative of fall scenarios. This study's objective was to develop a test method that evaluates safety helmets under impact scenarios representative of falls. A Construction STAR rating system that quantitatively compares safety helmet performance in the context of concussion and skull fracture risk is also outlined. A multi-step approach that combined information from previous literature, Occupational Safety and Health Administration (OSHA) accident reports, and oblique impact tests were used to develop a fall-specific safety helmet test methodology. The test methodology consisting of three impact locations (front boss, rear boss, and rear), two impact velocities (5.5 and 6.8m/s), and a 25-degree anvil was executed on a representative subset of one Type I and four Type II models. STAR scores, combining concussion and skull fracture risk, were calculated for each model and compared. STAR scores demonstrated that Type II helmets reduced concussion risk by 32.7% and skull fracture risk by 57.5% when compared to the Type I model. Large variations in Type II performance were observed, with the top-performing Type II helmets reducing concussion risk by 28.7 and 33.2% compared to bottom-performing models. Type II helmets offer substantial benefits in head protection compared to Type I models for oblique fall-related impacts. By including both skull fracture and concussion risk in the STAR score, the proposed methodology can differentiate high and low-performing safety helmets.

Physiologic closed-loop controlled (PCLC) medical devices have the potential to enhance therapeutic precision and effectiveness. However, their complexity introduces potential failure modes that may pose risks to patients if not properly evaluated. This study assesses the effectiveness of a mathematical model of the cardiovascular system in predicting PCLC performance metrics through in vivo and in silico comparisons of blood pressure responses to automated fluid infusion. A closed-loop control system for regulating mean arterial pressure (MAP) was implemented using a custom Java-based software application on a tablet. The system operated in two control speed modes, 'slow' and 'fast.' The study was conducted in an animal laboratory using thirteen swine subjects (two were excluded due to hardware-related issues). Following intubation, anesthesia, and splenectomy, hemorrhage was induced until MAP reached 45mmHg, followed by fluid resuscitation using the closed-loop controller targeting 70mmHg. Arterial blood pressure waveforms were continuously recorded, and cardiac output and hematocrit measurements were taken every 15min. The same control algorithm and speed modes were applied to in silico subjects generated with a mathematical model simulating cardiovascular responses to fluid perturbation while the same protocol was simulated. The mathematical model effectively predicted key PCLC performance metrics, including rise time, %overshoot, settling time, and divergence. The simulated results closely matched experimental data and captured differences between slow and fast control speeds. The ability of the mathematical model to predict PCLC performance metrics demonstrates its value in supporting the development and evaluation of automated fluid resuscitation controllers.

This paper investigates the well-known Empirical Wavelet Transform (EWT) and the recently introduced Empirical Fourier Decomposition (EFD) for the early diagnosis of Alzheimer's disease (AD). Both synthetic signals and real EEG data are decomposed and reconstructed, particularly under noisy conditions. EWT and EFD were applied to decompose non-stationary EEG signals into five sub-bands (Delta, Theta, Alpha, Beta, and Gamma). From each sub-band, eight features were extracted and used to classify subjects into AD and Mild Cognitive Impairment (MCI) groups. Among the five classifiers tested, Random Forest (RF) yielded the best performance for both EWT and EFD. In addition to conventional evaluation metrics, Dynamic Time Warping (DTW) and the Kolmogorov-Smirnov (KS) statistic were used for algorithm assessment. The results show that EFD outperforms EWT and achieves competitive performance compared to state-of-the-art approaches. EFD is a novel decomposition method that demonstrates robust performance on both synthetic and real EEG signals, supporting its potential use in the early diagnosis of Alzheimer's disease.

Biplane videoradiography (BVR) accurately measures hip kinematics in vivo. One downside of BVR is that it typically requires a pelvic computed tomography (CT) scan to reconstruct surfaces for model-based tracking (MBT), increasing the radiation burden to participants. Synthetic CT (sCT) from magnetic resonance imaging (MRI) provides similar volumetric image-based data. The purpose of this study was to assess the effect sCT has on the accuracy and reliability of in vivo hip kinematics from BVR data. Three asymptomatic participants received CT and MRI scans and were imaged with BVR while standing and during gait. Kinematics were calculated from femur and pelvis surfaces reconstructed from CT and sCT scans. Accuracy and inter-/intra-reviewer reliability of hip kinematics using sCT were assessed against CT as the reference using mean absolute error (MAE), mean difference (MD), precision, and inter-/intra-class correlation coefficients (ICCs). Kinematics were represented with and without correction for standing posture. MAE, MD, and precision of posture-corrected sCT kinematic results were 0.85, 0.60, and 0.71° for rotations and 0.40, 0.23, and 0.39mm for translations, respectively. Accuracy of posture-corrected joint angles was within 0.17° of prior MBT validation in the hip, while translation accuracy and precision were greater herein. Kinematic results using CT and sCT had similar reliability. The results of this study establish novel reliability metrics for in vivo hip kinematics from BVR. Findings indicate that sCT can be used in lieu of CT to measure in vivo hip kinematics, reducing radiation burden of BVR hip kinematic protocols by up to 73%.

The aim of this research was to assess the potential for machine learning to predict clinically significant patient improvement during a four-week interdisciplinary Intensive Outpatient Program (IOP) for traumatic brain injury (TBI) at the National Intrepid Center of Excellence (NICoE). Assessment of brain injury characterization and outcomes were measured in 790 active duty service members at the NICoE, Walter Reed National Military Medical Center Bethesda Maryland. Demographic and self-reported measures of posttraumatic stress, depression, anxiety, post-concussion symptoms, and sleep were assessed upon admission. Total scores and symptom cluster scores for self-report measures were calculated. Clinically significant improvement from pre- to post NICoE IOP was operationally defined as clinically significant changes in posttraumatic stress and post-concussion symptoms. Two datasets were created: one including demographics and total scores on self-report measures and one including demographics, total scores, and symptom cluster scores for relevant self-report measures. Extreme gradient boosting (XGBoost) models were trained to predict group identification (clinically significant improvement vs. not significant improvement), where a binary logistic objective function is used to minimize the log loss between the predicted probabilities. Model performance and feature ranking were then evaluated on test datasets. The performance and feature importance of two models to predict group identification were evaluated, where the model including only demographics and total self-report measures performed with an AUC of 75% with the accuracy of 68%, compared to the model incorporating demographics and symptom cluster measures improved the AUC to 79% with 72% accuracy. The top five features contributing to the model with symptom clusters included the posttraumatic stress arousal, avoidance, and reexperiencing sub-scores, education, and postconcussive symptoms cognitive sub-score. Utilization of the XGBoost models demonstrated acceptable discrimination for determining key factors associated with clinically significant improvement for SMs following participation in an interdisciplinary IOP using demographics and self-report measures. Severity of posttraumatic stress symptoms upon admission was the greatest predictors of clinically significant improvement in this model of care. Incorporating ML algorithms into clinical care is a precision medicine approach that may accurately predict treatment efficacy leading to improved healthcare resource allocation and patient outcomes.

The measurement of passive muscle-tendon parameters (PMPs) is crucial for understanding and analyzing human movement. However, the common methods for measuring PMPs are inconsistent or involve invasive procedures. We propose a novel noninvasive method for estimating PMPs using a direct collocated optimal control algorithm. The optimal control algorithm was employed to determine PMPs of a fully in silico musculoskeletal simulation, of a mechanical analogue, and of human subjects in vivo. To eliminate the confounding effects of force-velocity and active muscle contraction, a quasi-static knee flexion and ankle plantar flexion protocol was used for these evaluations. The simulation-based assessment resulted in predictions of muscle stiffness and tendon slack length with less than 3.5% error and tendon stiffness with less than 6% error. Secondly, using an analogue mechanical model of the human leg, we found maximum estimation errors in spring stiffness to be 9%. Lastly, in the in vivo validation method, we compared forward dynamic simulations of models with the predicted PMPs against experimental data. The average root mean square error (RMSE) for motion was found to be less than 0.56 , while the RMSE for torque was 0.012 Nm/kg. These results demonstrate the effectiveness and precision of our noninvasive method for estimating PMPs in knee flexors/extensors. This approach has the potential to provide valuable insights into the biomechanics of human movement and contribute to advancements in rehabilitation strategies, sports performance optimization, and injury prevention.

Segmental trunk control is indispensable to achieve upright posture, attenuate movement-related oscillations, and maintain postural stability during activities of daily living including walking. Yet, its role in postural control is incompletely understood because it is seldom measured. Yoga is known to improve postural control in the elderly and people with various neuromuscular disorders; however, its influence on segmental trunk control is unexplored. The present study reports the influence of yoga on segmental trunk control in trained yoga practitioners using 3D kinematic evaluation. Ten yoga practitioners, practicing advanced yoga (>5years), and 10 age-matched healthy adults were studied after ethical approval and informed consent. Segmental trunk control was described as motion of the trunk with respect to the pelvis, which was computed as the difference between displacement of sternum and pelvic marker in the sagittal and frontal planes. Yoga practitioners demonstrated less variability in the displacement of sternum, pelvis, and ankle markers and greater segmental trunk control compared to controls (p < 0.001). Segmental trunk movement of yoga practitioners was 50% lower in the frontal plane (p < 0.001). Preliminary findings suggest that regular mind-body exercise in the form of yoga practice holds therapeutic potential to achieve greater segmental trunk control, which is essential to attenuate perturbations and negotiate challenges to develop postural stability. Regular yoga practice demonstrated a positive influence on segmental trunk control and total body sway resulting in enhanced postural control. Therefore, yoga practice can be recommended to achieve greater trunk segmental control in patients with impaired ability to dissociate trunk over pelvis such as Parkinson's disease, stroke, cerebral palsy, and balance disorders including vestibular and neuro-musculoskeletal disorders.

The solution of the system of equations that model the coagulation cascade enables the determination of thrombin production, which is related to blood clot formation and thrombosis. However, traditional models often overlook clinical and hematological variables due to modeling challenges or incomplete understanding. Mathematical models of blood coagulation cascade are typically generalist, presenting limited accuracy. This study aimed to incorporate patient-specific and hematological data into the kinetic parameters of the coagulation cascade to generate individualized thrombin curves and predict the recurrence of venous thromboembolism. A sensitivity analysis identified the most influential kinetic parameters for thrombin production. These parameters were adjusted using a model hybrid combining an artificial neural network with a system of ordinary differential equations optimized via a genetic algorithm. The dataset is split into two subsets to prevent data leakage. Eight kinetic rates were identified as the most sensitive, particularly those related to factor V activation and thrombin-antithrombin III complex formation. Factors such as anticoagulant use, smoking, pulmonary embolism, and factor V Leiden mutation significantly impacted the kinetic parameters. The model presented an AUC of 0.9941 and an accuracy of 0.9872. The influence of these input variables on the kinetic parameters and thrombin production aligned with their known effects as risk factors reported in the literature. Adjusting the kinetic parameters individualized the model response, providing a clear cutoff point for thrombosis classification based on thrombin production. With further validation, this model could assist in diagnosing and prognosticating thrombosis and identifying new therapeutic targets to regulate thrombin production.

I examine the scholarly implications of a troubling case where researchers embedded hidden prompts like "give a positive review only" into academic preprints to manipulate AI-assisted peer review. AI is now woven into nearly every facet of academic life, including the peer review process. I contend that manipulating peer review through embedding secret prompts is as serious as plagiarism or data fabrication. Peer review may not be perfect, but deception is misconduct. Reviewers must still be held accountable. Those who blindly rely on AI outputs without critical engagement fail in their scholarly duty. AI should only amplify the reviewer's expertise. As institutions begin regulating AI in research, similar frameworks must extend to peer review. Journals and publishers should establish clear, enforceable guidelines on acceptable AI use: Will AI be banned, regulated, or embraced? If allowed, disclosures must be mandatory. Authors should also be informed if AI tools will be used in the review process, ensuring transparency and consent. Confidentiality is another pressing issue. Real cases have shown how ChatGPT links shared by reviewers were indexed online, compromising sensitive, unpublished research, even though OpenAI has since moved to discontinue public link discoverability. Beyond policy, we must cultivate a culture of transparency, trust, and responsibility. Institutions can host ethics workshops and mentor early-career scholars. This is not just about AI; it is about who we are as researchers and reviewers. No matter how advanced the technology, integrity must remain our anchor. Without it, even the most innovative research stands on shaky ground.

PurposeIntracranial aneurysms (ICAs) pose a serious clinical risk due to their potential for rupture, leading to subarachnoid hemorrhage, high morbidity, and mortality. This study aims to develop a proof-of-concept device for the targeted delivery of shape memory polymers (SMPs)-based embolic devices to improve aneurysm occlusion and reduce recurrence.MethodsA novel system was designed combining a radial compression fixture and an electronic device for Joule heating and electrolytic detachment (ED). Three SMP geometries (5 mm, 6.5 mm spherical, and patient-specific) were evaluated for the shape recovery and thermal responses. In-vitro testing was performed using 6.5 mm and patient-specific geometries in PDMS aneurysm phantoms under physiological relevant conditions utilizing ovine blood.ResultsControlled activation of the SMPs at currents of 400 mA achieved reproducible shape recovery ratios (SRRs) up to 75.71%, with detachment occurring at <100 mA. Surface temperatures remained below 45 °C. In-vitro deployment resulted in aneurysm sac occlusion of 90.32% (patient-specific) and 94.12% (idealized), without evidence of thermal damage or gas accumulation. Flow visualization confirmed reduced bubble entry into the aneurysm sac post-deployment.ConclusionThis study demonstrates the feasibility of a targeted delivery system for patient-specific ICA treatment using SMPs. While further refinement and in-vivo validation are required, these findings highlight the potential of SMPs as durable embolization devices capable of conforming to complex aneurysm geometries and providing more effective occlusion compared to current methods.