Advancing Osteoporosis Evaluation Procedures: Detailed Computational Analysis of Regional Structural Vulnerabilities in Osteoporotic Bone.

Osteoporotic fractures of the femur are associated with poor healing, disability, reduced quality of life, and high mortality rates within 1 year. Moreover, osteoporotic fractures of the femur are still considered to be an unsolved problem in orthopedic surgery. In order to more effectively identify osteoporosis-related fracture risk and develop advanced treatment approaches for femur fractures, it is necessary to acquire a greater understanding of how osteoporosis alters the diaphyseal structure and biomechanical characteristics. The current investigation uses computational analyses to comprehensively examine how femur structure and its associated properties differ between healthy and osteoporotic bones. The results indicate statistically significant differences in multiple geometric properties between healthy femurs and osteoporotic femurs. Additionally, localized disparities in the geometric properties are evident. Overall, this approach will be beneficial in the development of new diagnostic procedures for highly detailed patient-specific detection of fracture risk, for establishing novel injury prevention treatments, and for informing advanced surgical solutions.

Developing Patient-Specific Statistical Reconstructions of Healthy Anatomical Structures to Improve Patient Outcomes.

There are still numerous problems with modern joint replacement prostheses, which negatively influence patient health and recovery. For example, it is especially important to avoid failures and complications following hip arthroplasty because the loss of hip joint function is commonly associated with increased demand on the healthcare system, reoperation, loss of independence, physical disability, and death. The current study uses hip arthroplasty as a model system to present a new strategy of computationally generating patient-specific statistical reconstructions of complete healthy anatomical structures from computed tomography (CT) scans of damaged anatomical structures. The 3D model morphological data were evaluated from damaged femurs repaired with prosthetic devices and the respective damaged femurs that had been restored using statistical reconstruction. The results from all morphological measurements (i.e., maximum femoral length, Hausdorff distance, femoral neck anteversion, length of rotational center divergence, and angle of inclination) indicated that the values of femurs repaired with traditional prostheses did not fall within the +/-3 standard deviations of the respective patient-specific healthy anatomical structures. These results demonstrate that there are quantitative differences in the morphology of femurs repaired with traditional prostheses and the morphology of patient-specific statistical reconstructions. This approach of generating patient-specific statistical reconstructions of healthy anatomical structures might help to inform prosthetic designs so that new prostheses more closely resemble natural healthy morphology and preserve biomechanical function. Additionally, the patient-specific statistical reconstructions of healthy anatomical structures might be valuable for surgeons in that prosthetic devices could be selected and positioned to more accurately restore natural biomechanical function. All in all, this contribution establishes the novel approach of generating patient-specific statistical reconstructions of healthy anatomical structures from the CT scans of individuals' damaged anatomical structures to improve treatments and patient outcomes.

Sleep and occupational well-being in active duty special operations soldiers: A replication and expansion.

OBJECTIVE: To assess the relationship between sleep quality and occupational well-being in active duty military Service Members. DESIGN: Longitudinal prospective analysis. SETTING: An annual military training event. PARTICIPANTS: US Army special operations Soldiers (n = 60; 100% male; age 25.41 ± 3.74). INTERVENTION: None. MEASUREMENTS: The Pittsburgh Sleep Quality Index (PSQI) was administered prior to the training event, and the Emotional Exhaustion Scale, the Role Overload Scale, the Walter Reed Army Institute of Research Soldier-Specific Functional Impairment Scale, and the Perceived Stress Scale were administered after the event. Linear regression models were used to assess the relationship between sleep and occupational wellness measures, and the outcome measures of "good" and "poor" sleepers (per the PSQI scoring criteria) were compared with Student's t tests. RESULTS: Higher (poorer) PSQI Global Scores predicted poorer occupational wellness of all measures (emotional exhaustion: B = 1.60, P < .001, R2 = 0.25; functional impairment: B = 0.29, P = .03, R2 = 0.14; role overload: B = 0.28, P = .008, R2 = 0.12; and perceived stress: B = 0.34, P = .004, R2 = 0.20). There were additional relationships between specific PSQI component scores and occupational wellness measures, which is a replication of This team's previous work. Furthermore, emotional exhaustion (t(58) = -4.18, P < .001), functional impairment (t(59)= -3.68, P = .001), role overload (t(58) = -3.20, P = .002), and perceived stress (t(58) = -2.43, P = .02) were all higher in poor sleepers. CONCLUSIONS: The findings of this study suggest that US Army special operations Soldiers who have poorer sleep quality may be at increased risk for having poorer occupational well-being.

Mentor's brain functional connectivity network during robotic assisted surgery mentorship.

In many complicated cognitive-motor tasks mentoring is inevitable during the learning process. Although mentors are expert in doing the task, trainee's operation might be new for a mentor. This makes mentoring a very difficult task which demands not only the knowledge and experience of a mentor, but also his/her ability to follow trainee's movements and patiently advise him/her during the operation. We hypothesize that information binding throughout the mentor's brain areas, contributed to the task, changes as the expertise level of the trainee improves from novice to intermediate and expert. This can result in the change of mentor's level of satisfaction. The brain functional connectivity network is extracted by using brain activity of a mentor during mentoring novice and intermediate surgeons, watching expert surgeon operation, and doing Urethrovesical Anasthomosis (UVA) procedure by himself. By using the extracted network, we investigate the role of modularity and neural activity efficiency in mentoring. Brain activity is measured by using a 24-channel ABM Neuro-headset with the frequency of 256 Hz. One mentor operates 26 UVA procedures and three trainees with the expertise level of novice, intermediate, and expert perform 26 UVA procedures under the supervision of mentor. Our results indicate that the modularity of functional connectivity network is higher when mentor performs the task or watches the expert operation comparing mentoring the novice and intermediate surgeons. At the end of each operation, mentor subjectively assesses the quality of operation by giving scores to NASA-TLX indexes. Performance score is used to discuss our results. The extracted significant positive correlation between performance level and modularity (r = 0.38, p - value <; 0.005) shows the increase of automaticity and decrease in neural activity cost by improving the performance.