Application of image recognition-based tracker-less augmented reality navigation system in a series of sawbone trials.

BACKGROUND: This study introduced an Augmented Reality (AR) navigation system to address limitations in conventional high tibial osteotomy (HTO). The objective was to enhance precision and efficiency in HTO procedures, overcoming challenges such as inconsistent postoperative alignment and potential neurovascular damage. METHODS: The AR-MR (Mixed Reality) navigation system, comprising HoloLens, Unity Engine, and Vuforia software, was employed for pre-clinical trials using tibial sawbone models. CT images generated 3D anatomical models, projected via HoloLens, allowing surgeons to interact through intuitive hand gestures. The critical procedure of target tracking, essential for aligning virtual and real objects, was facilitated by Vuforia's feature detection algorithm. RESULTS: In trials, the AR-MR system demonstrated significant reductions in both preoperative planning and intraoperative times compared to conventional navigation and metal 3D-printed surgical guides. The AR system, while exhibiting lower accuracy, exhibited efficiency, making it a promising option for HTO procedures. The preoperative planning time for the AR system was notably shorter (4 min) compared to conventional navigation (30.5 min) and metal guides (75.5 min). Intraoperative time for AR lasted 8.5 min, considerably faster than that of conventional navigation (31.5 min) and metal guides (10.5 min). CONCLUSIONS: The AR navigation system presents a transformative approach to HTO, offering a trade-off between accuracy and efficiency. Ongoing improvements, such as the incorporation of two-stage registration and pointing devices, could further enhance precision. While the system may be less accurate, its efficiency renders it a potential breakthrough in orthopedic surgery, particularly for reducing unnecessary harm and streamlining surgical procedures.

Promoting postoperative recovery in patients with metastatic epidural spinal cord compression based on the concept of ERAS: a multicenter analysis of 304 patients.

BACKGROUND CONTEXT: Enhanced recovery after surgery (ERAS) has proven beneficial for patients undergoing orthopedic surgery. However, the application of ERAS in the context of metastatic epidural spinal cord compression (MESCC) remains undefined. PURPOSE: This study aims to establish a medical pathway rooted in the ERAS concept, with the ultimate goal of scrutinizing its efficacy in enhancing postoperative outcomes among patients suffering from MESCC. STUDY DESIGN/SETTING: An observational cohort study. PATIENT SAMPLE: A total of 304 patients with MESCC who underwent surgery were collected between January 2016 and January 2023 at two large tertiary hospitals. OUTCOME MEASURES: Surgery-related variables, patient quality of life, and pain outcomes. Surgery-related variables in the study included surgery time, surgery site, intraoperative blood loss, and complication. METHODS: From January 2020 onwards, ERAS therapies were implemented for MESCC patients in both institutions. Thus, the ERAS cohort included 138 patients with MESCC who underwent surgery from January 2020 to January 2023, whereas the traditional cohort consisted of 166 patients with MESCC who underwent surgery from January 2016 to December 2019. Clinical baseline characteristics, surgery-related features, and surgical outcomes were collected. Patient quality of life was evaluated using the Functional Assessment of Cancer Therapy-General Scale (FACT-G), and pain outcomes were assessed using the Visual Analogue Scale (VAS). RESULTS: Comparison of baseline characteristics revealed that the two cohorts were similar (all p>.050), indicating comparable distribution of clinical characteristics. In terms of surgical outcomes, patients in the ERAS cohort exhibited lower intraoperative blood loss (p<.001), shorter postoperative hospital stays (p<.001), lower perioperative complication rates (p=.020), as well as significantly shorter time to ambulation (P<0.001), resumption of regular diet (p<.001), removal of urinary catheter (p<.001), initiation of radiation therapy (p<.001), and initiation of systemic internal therapy (p<.001) compared with patients in the traditional cohort. Regarding pain outcomes and quality of life, patients undergoing the ERAS program demonstrated significantly lower VAS scores (p<.010) and higher scores for physical (p<.001), social (p<.001), emotional (p<.001), and functional (p<.001) well-being compared with patients in the traditional cohort. CONCLUSIONS: The ERAS program, renowned for its ability to expedite postoperative recuperation, emerges as a promising approach to ameliorate the recovery process in MESCC patients. Not only does it exhibit potential in enhancing pain management outcomes, but it also holds the promise of elevating the overall quality of life for these individuals. Future investigations should delve deeper into the intricate components of the ERAS program, aiming to unravel the precise mechanisms that underlie its remarkable impact on patient outcomes.

A New Treatment Strategy for Spinal Metastasis: The "Systemic Conditions, Effectiveness of Systemic Treatment, Neurology, and Oncology" Decision Framework System.

BACKGROUND AND OBJECTIVES: Treating metastatic spinal tumors poses a significant challenge because there are currently no universally applied guidelines for managing spinal metastases. This study aims to propose a new decision framework for the 12-point epidural spinal cord compression grading system to treat patients with metastatic spinal tumors and investigate its clinical effectiveness in a multicenter analysis. METHODS: This study analyzed 940 patients with metastatic spinal tumors between December 2017 and March 2023. The study provided the clinical evidence for the systemic conditions, effectiveness of systemic treatment, neurology, and oncology (SENO) decision framework among spine metastases. The SENO decision framework was launched in January 2021 in our hospitals, classifying patients into 2 groups: The non-SENO group (n = 489) consisted of patients treated between December 2017 and January 2021, while the SENO group (n = 451) comprised patients treated from January 2021 to March 2023. RESULTS: Patients in the SENO group were more likely to receive minimally invasive surgery (67.85% vs 58.69%) and less chance of receiving spinal cord circular decompression surgery (14.41% vs 24.74%) than patients in the non-SENO group ( P < .001). Furthermore, patients in the SENO group experienced fewer perioperative complications (9.09% vs 15.34%, P = .004), incurred lower hospitalization costs ( P < .001), had shorter length of hospitalization ( P < .001), and received systematic treatments for tumors earlier ( P < .001). As a result, patients in the SENO group (329.00 [95% CI: 292.06-365.94] days) demonstrated significantly improved survival outcomes compared with those in the non-SENO group (279.00 [95% CI: 256.91-301.09], days) ( P < .001). At 3 months postdischarge, patients in the SENO group reported greater improvements in their quality of life, encompassing physical, social, emotional, and functional well-being, when compared with patients in the non-SENO group. CONCLUSION: The SENO decision framework is a promising approach for treating patients with metastatic spinal tumors.

Mathematical modeling and application of IL-1ß/TNF signaling pathway in regulating chondrocyte apoptosis.

Introduction: Mathematical model can be used to model complex biological processes, and have shown potential in describing apoptosis in chondrocytes. Method: In order to investigate the regulatory mechanisms of TNF signaling pathway in regulating chondrocyte apoptosis, a fractional-order differential equation model is proposed to describe the dynamic behavior and mutual interaction of apoptosis-related genes under the activation of TNF signaling pathway. Compared with the traditional molecular biology techniques, the proposed mathematical modeling has advantages to providing a more comprehensive understanding of the regulatory mechanisms of TNF signaling pathway in chondrocyte apoptosis. Result: In this paper, differentially expressed genes induced by IL-1ß in human chondrocyte apoptosis are screened using high-throughput sequencing. It is found that they were significantly enriched in the TNF signaling pathway. Therefore, a mathematical model of the TNF signaling pathway is built. Using real-time PCR experiments, mRNA data is measured and used to identify the model parameters, as well as the correlation coefficient. Finally, the sensitivity of the model parameters is discussed by using numerical simulation methods, which can be used to predict the effects of different interventions and explore the optimal intervention strategies for regulating chondrocyte apoptosis. Discussion: Therefore, fractional-order differential equation modeling plays an important role in understanding the regulatory mechanisms of TNF signaling pathway in chondrocyte apoptosis and its potential clinical applications.

A Web-Based Calculator to Predict Early Death Among Patients With Bone Metastasis Using Machine Learning Techniques: Development and Validation Study.

BACKGROUND: Patients with bone metastasis often experience a significantly limited survival time, and a life expectancy of <3 months is generally regarded as a contraindication for extensive invasive surgeries. In this context, the accurate prediction of survival becomes very important since it serves as a crucial guide in making clinical decisions. OBJECTIVE: This study aimed to develop a machine learning-based web calculator that can provide an accurate assessment of the likelihood of early death among patients with bone metastasis. METHODS: This study analyzed a large cohort of 118,227 patients diagnosed with bone metastasis between 2010 and 2019 using the data obtained from a national cancer database. The entire cohort of patients was randomly split 9:1 into a training group (n=106,492) and a validation group (n=11,735). Six approaches-logistic regression, extreme gradient boosting machine, decision tree, random forest, neural network, and gradient boosting machine-were implemented in this study. The performance of these approaches was evaluated using 11 measures, and each approach was ranked based on its performance in each measure. Patients (n=332) from a teaching hospital were used as the external validation group, and external validation was performed using the optimal model. RESULTS: In the entire cohort, a substantial proportion of patients (43,305/118,227, 36.63%) experienced early death. Among the different approaches evaluated, the gradient boosting machine exhibited the highest score of prediction performance (54 points), followed by the neural network (52 points) and extreme gradient boosting machine (50 points). The gradient boosting machine demonstrated a favorable discrimination ability, with an area under the curve of 0.858 (95% CI 0.851-0.865). In addition, the calibration slope was 1.02, and the intercept-in-large value was -0.02, indicating good calibration of the model. Patients were divided into 2 risk groups using a threshold of 37% based on the gradient boosting machine. Patients in the high-risk group (3105/4315, 71.96%) were found to be 4.5 times more likely to experience early death compared with those in the low-risk group (1159/7420, 15.62%). External validation of the model demonstrated a high area under the curve of 0.847 (95% CI 0.798-0.895), indicating its robust performance. The model developed by the gradient boosting machine has been deployed on the internet as a calculator. CONCLUSIONS: This study develops a machine learning-based calculator to assess the probability of early death among patients with bone metastasis. The calculator has the potential to guide clinical decision-making and improve the care of patients with bone metastasis by identifying those at a higher risk of early death.

Prediction of postoperative health-related quality of life among patients with metastatic spinal cord compression secondary to lung cancer.

Background: Health-related quality of life (HRQoL) is a critical aspect of overall well-being for patients with lung cancer, particularly those with metastatic spinal cord compression (MSCC). However, there is currently a lack of universal evaluation of HRQoL in this specific patient population. The aim of this study was to develop a nomogram that can accurately predict HRQoL outcomes in patients with lung cancer-related MSCC. Methods: A total of 119 patients diagnosed with MSCC secondary to lung cancer were prospectively collected for analysis in the study. The least absolute shrinkage and selection operator (LASSO) regression analysis, along with 10-fold cross-validation, was employed to select the most significant variables for inclusion in the nomogram. Discriminative and calibration abilities were assessed using the concordance index (C-index), discrimination slope, calibration plots, and goodness-of-fit tests. Net reclassification index (NRI) and integrated discrimination improvement (IDI) analyses were conducted to compare the nomogram's performance with and without the consideration of comorbidities. Results: Four variables were selected to construct the final nomogram, including the Eastern Cooperative Oncology Group (ECOG) score, targeted therapy, anxiety scale, and number of comorbidities. The C-index was 0.87, with a discrimination slope of 0.47, indicating a favorable discriminative ability. Calibration plots and goodness-of-fit tests revealed a high level of consistency between the predicted and observed probabilities of poor HRQoL. The NRI (0.404, 95% CI: 0.074-0.734, p = 0.016) and the IDI (0.035, 95% CI: 0.004-0.066, p = 0.027) confirmed the superior performance of the nomogram with the consideration of comorbidities. Conclusions: This study develops a prediction nomogram that can assist clinicians in evaluating postoperative HRQoL in patients with lung cancer-related MSCC. This nomogram provides a valuable tool for risk stratification and personalized treatment planning in this specific patient population.

Comparative Biomechanical Analysis of Anterior Process Locking Plate Alone versus Combined with Percutaneous Cannulated Screw Fixation for Sanders Type II Calcaneal Fractures: A Finite Element Study.

BACKGROUND Calcaneal fractures are the most common tarsal bone fractures, and account for 75% of intra-articular fractures. The purpose of this study was to compare the biomechanical stability of the anterior process locking plate combined with the percutaneous cannulated screw fixation (screw group) versus the anterior process locking plate fixation alone (plate group) for the treatment of Sanders type II calcaneal fractures using finite element analysis to provide a theoretical basis for clinical work. MATERIAL AND METHODS We established a 3D model of Sanders type II calcaneal fracture; assigned material properties to the internal fixation systems; applied loads; set up analysis criteria; analyzed the displacement of the fracture, relative displacement, stress state of bone tissue, and internal fixation; and compared mechanical stability. RESULTS For Sanders type II A, II B, and II C calcaneal fractures, the degree of displacement and relative displacement of the fracture in the screw group was less than that of the plate group. For all subtypes of Sanders type II calcaneal fractures, the screw group had better mechanical stability than the plate group. CONCLUSIONS Both fixation methods (screw and plate group) were within a reasonable range for restoring the levelling effect of the joint surface and maintaining the strength of fixation, and both had good mechanical stability. Finite element analysis is a relatively reliable method, and biomechanics and clinical studies must further verify the experimental results.

[Targeted muscle reinnervation: a surgical technique of human-machine interface for intelligent prosthesis].

Objective: To review targeted muscle reinnervation (TMR) surgery for the construction of intelligent prosthetic human-machine interface, thus providing a new clinical intervention paradigm for the functional reconstruction of residual limbs in amputees. Methods: Extensively consulted relevant literature domestically and abroad and systematically expounded the surgical requirements of intelligent prosthetics, TMR operation plan, target population, prognosis, as well as the development and future of TMR. Results: TMR facilitates intuitive control of intelligent prostheses in amputees by reconstructing the "brain-spinal cord-peripheral nerve-skeletal muscle" neurotransmission pathway and increasing the surface electromyographic signals required for pattern recognition. TMR surgery for different purposes is suitable for different target populations. Conclusion: TMR surgery has been certified abroad as a transformative technology for improving prosthetic manipulation, and is expected to become a new clinical paradigm for 2 million amputees in China.

Deep learning based ultrasonic visualization of distal humeral cartilage for image-guided therapy: a pilot validation study.

Background: Ultrasound is widely used for image-guided therapy (IGT) in many surgical fields, thanks to its various advantages, such as portability, lack of radiation and real-time imaging. This article presents the first attempt to utilize multiple deep learning algorithms in distal humeral cartilage segmentation for dynamic, volumetric ultrasound images employed in minimally invasive surgery. Methods: The dataset, consisting 5,321 ultrasound images were collected from 12 healthy volunteers. These images were randomly split into training and validation sets in an 8:2 ratio. Based on deep learning algorithms, 9 semantic segmentation networks were developed and trained using our dataset at Southern University of Science and Technology Hospital in September 2022. The performance of the networks was evaluated based on their segmenting accuracy and processing efficiency. Furthermore, these networks were implemented in an IGT system to assess their feasibility in 3-dimentional imaging precision. Results: In 2D segmentation, Medical Transformer (MedT) showed the highest accuracy result with a Dice score of 89.4%, however, the efficiency in processing images was relatively lower at 2.6 frames per second (FPS). In 3D imaging, the average root mean square (RMS) between ultrasound (US)-generated models based on the networks and magnetic resonance imaging (MRI)-generated models was no more than 1.12 mm. Conclusions: The findings of this study indicate the technological feasibility of a novel method for real-time visualization of distal humeral cartilage. The increased precision of ultrasound calibration and segmentation are both important approaches to improve the accuracy of 3D imaging.

Development of a novel 12-point grading system for evaluating epidural spinal cord compression and its clinical implications.

BACKGROUND CONTEXT: The assessment of epidural spinal cord compression (ESCC) plays a crucial role in clinical decision-making, yet the current grading system lacks reliability and requires improvements. PURPOSE: The study aims to develop a reliable grading system for evaluating ESCC and to investigate its association with the neurological status of patients. STUDY DESIGN/SETTING: A prospective cohort study. PATIENT SAMPLE: A total of 330 patients with metastatic spinal disease were included in the study. OUTCOME MEASURES: The main outcome was the neurological status evaluated using the American Spinal Injury Association (ASIA) scale. METHODS: We proposed a novel grading system, called the 12-point ESCC grading system, to evaluate ESCC based on findings from spinal magnetic resonance imaging (MRI). This new grading system consists of 12 grades, ranging from Grade 0 to 3, with higher grades indicating more severe ESCC. The detailed information about the sagittal image of the spine and the severity of spinal cord swelling was considered in this new grading system. The Spearman correlation analysis and logistic regression analysis were employed to investigate the correlation between the previous 6-point grading system and ASIA, as well as between the new 12-point ESCC grading system and ASIA. The prediction effectiveness was evaluated using the area under curve (AUC) analysis. RESULTS: Patients with higher grades in the 12-point ESCC grading system exhibited a higher likelihood of experiencing a worse neurological condition. Specifically, patients with grades 2a to 2d and 3a to 3d according to the new 12-point ESCC grading system were significantly associated with more complete paralysis (p<.001) compared with patients with grade 0. The Spearman correlation coefficient was 0.729 between the previous 6-point ESCC grading system and ASIS and 0.750 between the new 12-point ESCC grading system and ASIS. When categorizing ASIS into complete paralysis and other neurological statuses, the 6-point ESCC score yielded an AUC of 0.820, which increased to 0.860 with the new 12-point ESCC grading system. Furthermore, when ASIS was divided into normal and abnormal neurological statuses, the AUC increased from 0.889 to 0.906. Additionally, spinal cord swelling was significantly associated with more complete paralysis (p<.001) and abnormal neurological status (p<.001) based on the new 12-point ESCC grading system. CONCLUSIONS: The new 12-point ESCC grading system provides more detailed information and further improves the prediction effectiveness for evaluating neurological status compared with the previous 6-point ESCC grading system. In the new 12-point ESCC grading system, higher grades or the presence of spinal cord swelling are indicative of a worse neurological condition.

Machine learning-based algorithms to predict severe psychological distress among cancer patients with spinal metastatic disease.

BACKGROUND CONTEXT: Metastatic spinal disease is an advanced stage of cancer patients and often suffer from terrible psychological health status; however, the ability to estimate the risk probability of this adverse outcome using current available data is very limited. PURPOSE: The goal of this study was to propose a precise model based on machine learning techniques to predict psychological status among cancer patients with spinal metastatic disease. STUDY DESIGN/SETTING: A prospective cohort study. PATIENT SAMPLE: A total of 1043 cancer patients with spinal metastatic disease were included. OUTCOME MEASURES: The main outcome was severe psychological distress. METHODS: The total of patients was randomly divided into a training dataset and a testing dataset on a ratio of 9:1. Patients' demographics, lifestyle choices, cancer-related features, clinical manifestations, and treatments were collected as potential model predictors in the study. Five machine learning algorithms, including XGBoosting machine, random forest, gradient boosting machine, support vector machine, and ensemble prediction model, as well as a logistic regression model were employed to train and optimize models in the training set, and their predictive performance was assessed in the testing set. RESULTS: Up to 21.48% of all patients who were recruited had severe psychological distress. Elderly patients (p<0.001), female (p =0.045), current smoking (p=0.002) or drinking (p=0.003), a lower level of education (p<0.001), a stronger spiritual desire (p<0.001), visceral metastasis (p=0.005), and a higher Eastern Cooperative Oncology Group (ECOG) score (p<0.001) were significantly associated with worse psychological health. With an area under the curve (AUC) of 0.865 (95% CI: 0.788-0.941) and an accuracy of up to 0.843, the gradient boosting machine algorithm performed best in the prediction of the outcome, followed by the XGBooting machine algorithm (AUC: 0.851, 95% CI: 0.768-0.934; Accuracy: 0.826) and ensemble prediction (AUC: 0.851, 95% CI: 0.770-0.932; Accuracy: 0.809) in the testing set. In contrast, the AUC of the logistic regression model was only 0.836 (95% CI: 0.756-0.916; Accuracy: 0.783). CONCLUSIONS: Machine learning models have greater predictive power and can offer useful tools to identify individuals with spinal metastatic disease who are experiencing severe psychological distress.

Treatment of patients with metastatic epidural spinal cord compression using an enhanced recovery after surgery program.

Purpose: The aims of this study were to introduce a new medical, pathway based on the concept of "enhanced recovery after surgery" (ERAS) for patients with metastatic epidural spinal cord compression (MESCC), and to test whether the ERAS program could improve clinical metrics among such patients. Methods: Data from patients with MESCC (n = 98), collected between December 2016 and December 2019 (Non-ERAS cohort), and from 86 patients with metastatic epidural spinal cord compression collected between January 2020 and December 2022 (ERAS cohort), were retrospectively analyzed. Patients were treated by decompressive surgery combined with transpedicular screw implantation and internal fixation. Patient baseline clinical characteristics were collected and compared between the two cohorts. Surgical outcomes analyzed included operation time; intraoperative blood loss; postoperative length of hospital stay; time to ambulation, regular diet, urinary catheter removal, and radiation therapy; perioperative complications; anxiety; depression; and satisfaction with treatment. Results: No significant differences in clinical characteristics were found between the non-ERAS and enhanced recovery after surgery cohorts (all p > 0.050), indicating that the two cohorts were comparable. Regarding surgical outcomes, the enhanced recovery after surgery cohort had significantly less intraoperative blood loss (p < 0.001); shorter length of postoperative hospital stay (p < 0.001); shorter time to ambulation (p < 0.001), regular diet (p < 0.001), urinary catheter removal (p < 0.001), radiation administration (p < 0.001), and systemic internal therapy (p < 0.001); lower perioperative complication rate (p = 0.024); less postoperative anxiety (p = 0.041); and higher score for satisfaction with treatment (p < 0.001); whereas operation time (p = 0.524) and postoperative depression (p = 0.415) were similar between the two cohorts. Compliance analysis demonstrated that ERAS interventions were successfully conducted in the vast majority of patients. Conclusion: The enhanced recovery after surgery intervention is beneficial to patients with metastatic epidural spinal cord compression, according to data on intraoperative blood loss; length of hospital stay; time to ambulation, regular diet, urinary catheter removal, radiation exposure, and systemic internal therapy; perioperative complication; alleviation of anxiety; and improvement of satisfaction. However, clinical trials to investigate the effect of enhanced recovery after surgery are needed in the future.

Fractional differential equation modeling of the HBV infection with time delay and logistic proliferation.

In this article, a fractional-order differential equation model of HBV infection was proposed with a Caputo derivative, delayed immune response, and logistic proliferation. Initially, infection-free and infection equilibriums and the basic reproduction number were computed. Thereafter, the stability of the two equilibriums was analyzed based on the fractional Routh-Hurwitz stability criterion, and the results indicated that the stability will change if the time delay or fractional order changes. In addition, the sensitivity of the basic reproduction number was analyzed to find out the most sensitive parameter. Lastly, the theoretical analysis was verified by numerical simulations. The results showed that the time delay of immune response and fractional order can significantly affect the dynamic behavior in the HBV infection process. Therefore, it is necessary to consider time delay and fractional order in modeling HBV infection and studying its dynamics.

Distal Humerus Morphological Analysis of Chinese Individuals: A Statistical Shape Modeling Approach.

OBJECTIVE: A detailed analysis of the morphology of distal humeral articulation can help in the creation of anatomic prostheses of hemiarthroplasty. This study used statistical shape modeling to evaluate the 3D morphology of the distal humerus in healthy Chinese individuals and to investigate the proper articular morphology differences. METHODS: A statistical shape model (SSM) of the distal humerus was created using CT scans of 106 survey-confirmed nonpathologic elbows. In addition, the articular components of each principal component (PC) were selected and fitted on the mean mode. The Euclidean point-to-mesh distance of articular modes was calculated as a measurement the proper change in the morphology of the articulation. RESULTS: The first seven PCs jointly accounted for 80.9% of the total variation (44.4%, 12.2%, 7.9%, 5.9%, 4.1%, 3.4% and 3%, respectively). In the mean model, the distance between the medial and lateral epicondyles was 57.4 mm, the width of the articulation was 42.1 mm, and the angle of the transepicondylar line (TEL) and C line was 4.8°. The articular surface differences of the first PC were significant (RMS: 1.43 mm in the -3 SD model and 2.38 mm in the +3 SD model), whereas under other conditions, the differences were not remarkable despite the maximum deformation not exceeding 1 mm. CONCLUSION: A novel method (SSM) was used to evaluate the 3D morphology of the distal humerus in healthy Chinese individuals and investigate the proper articular shape differences. We found the proper shape of articular surface basically transformed into one variation pattern which was relevant to the bone size, even though the morphology of distal humerus possessed complicated variation modes. The findings of this study can be helpful to design the next generation of elbow hemiarthroplasty in the future.

Schwann Cells Accelerate Osteogenesis via the Mif/CD74/FOXO1 Signaling Pathway In Vitro.

Schwann cells have been found to promote osteogenesis by an unclear molecular mechanism. To better understand how Schwann cells accelerate osteogenesis, RNA-Seq and LC-MS/MS were utilized to explore the transcriptomic and metabolic response of MC3T3-E1 to Schwann cells. Osteogenic differentiation was determined by ALP staining. Lentiviruses were constructed to alter the expression of Mif (macrophage migration inhibitory factor) in Schwann cells. Western blot (WB) analysis was employed to detect the protein expression. The results of this study show that Mif is essential for Schwann cells to promote osteogenesis, and its downstream CD74/FOXO1 is also involved in the promotion of Schwann cells on osteogenesis. Further, Schwann cells regulate amino acid metabolism and lipid metabolism in preosteoblasts. These findings unveil the mechanism for Schwann cells to promote osteogenesis where Mif is a key factor.

Comparison of oblique triangular configuration and inverted equilateral triangular configuration of three cannulated screws in treating unstable femoral neck fracture: A finite element analysis.

BACKGROUND: The cross-sectional area of three parallel screws might affect the stability of the internal fixation of femoral neck fractures. The screws fixed in the oblique-triangle configuration (OTC) were assumed to have a larger cross-sectional area, but the biomechanical stability has not yet been validated. In this study, finite element analyses were performed to compare the biomechanical properties of the internal fixation fixed by the OTC and the traditional Inverted Equilateral Triangle Configuration (IETC). METHOD: Pauwels type III fracture was established on the three-dimensional femoral model and three cannulated screws with the OTC and traditional IETC methods were applied. The oblique-triangle configuration with the largest area inscribed the femoral neck isthmus by the three screws was determined, the area and circumference of the cross-section formed by the OTC and IETC model were compared. Stress, strain, and displacement peaks of the two configuration models under different loads were compared. Twelve pairs of nodes on the fracture ends were selected and the displacement of the fracture ends was evaluated through the displacement between these nodes. RESULTS: The area and circumference of the cross-section formed by the OTC were larger than those in the IETC model. The degree of stress dispersion around the screw holes in the OTC model was better than that of the IETC, but the stress distribution order of the three screws in the two models was consistent. The maximum stress, strain, displacement, and displacement of the fracture end in the OTC model were smaller than those in the IETC model. The stress, strain, displacement, and fracture end displacement peaks of the two fixed models gradually increase with the increase of loads. CONCLUSION: The oblique-triangle configuration showed superior mechanical properties than the IETC in finite element analyses. This study suggests that when three screws are fixed in parallel method, the larger the cross-sectional area of the screw configuration, the better stability of the internal fixation might be obtained. Furthermore, the biomechanical properties of various spatial configurations and screw holes of the three parallel screws need to be considered before clinical practice.

Machine Learning-based Prediction of Prolonged Intensive Care Unit Stay for Critical Patients with Spinal Cord Injury.

STUDY DESIGN: A retrospective cohort study. OBJECTIVE: The objective of the study was to develop machine-learning (ML) classifiers for predicting prolonged intensive care unit (ICU)-stay and prolonged hospital-stay for critical patients with spinal cord injury (SCI). SUMMARY OF BACKGROUND DATA: Critical patients with SCI in ICU need more attention. SCI patients with prolonged stay in ICU usually occupy vast medical resources and hinder the rehabilitation deployment. METHODS: A total of 1599 critical patients with SCI were included in the study and labeled with prolonged stay or normal stay. All data were extracted from the eICU Collaborative Research Database and the Medical Information Mart for Intensive Care III-IV Database. The extracted data were randomly divided into training, validation and testing (6:2:2) subdatasets. A total of 91 initial ML classifiers were developed, and the top three initial classifiers with the best performance were further stacked into an ensemble classifier with logistic regressor. The area under the curve (AUC) was the main indicator to assess the prediction performance of all classifiers. The primary predicting outcome was prolonged ICU-stay, while the secondary predicting outcome was prolonged hospital-stay. RESULTS: In predicting prolonged ICU-stay, the AUC of the ensemble classifier was 0.864 ±â€Š0.021 in the three-time five-fold cross-validation and 0.802 in the independent testing. In predicting prolonged hospital-stay, the AUC of the ensemble classifier was 0.815 ±â€Š0.037 in the three-time five-fold cross-validation and 0.799 in the independent testing. Decision curve analysis showed the merits of the ensemble classifiers, as the curves of the top three initial classifiers varied a lot in either predicting prolonged ICU-stay or discriminating prolonged hospital-stay. CONCLUSION: The ensemble classifiers successfully predict the prolonged ICU-stay and the prolonged hospital-stay, which showed a high potential of assisting physicians in managing SCI patients in ICU and make full use of medical resources.Level of Evidence: 3.

The Oval-like Cross-section of Femoral Neck Isthmus in Three-dimensional Morphological Analysis.

OBJECTIVES: To investigate the cross-section shape of the femoral neck isthmus (FNI) in three-dimensional reconstruction model of the femoral neck. METHODS: From December 2009 to December 2012, computed tomography (CT) data of bilateral hip joint from 200 consecutive patients (137 males and 63 females, 69.41 ± 9.21 years old, ranged from 50-85 years old) who underwent surgical treatments for proximal femoral fracture were retrospectively reviewed. The 3D model of the proximal femur was reconstructed, and the "inertia axis" method, which was applied to measure the long and short axes of the cross-section of the FNI, was established. The cross-sectional area and perimeter were calculated by a formula using the length of the long and short axes and then compared with the actual measured values by the software. Correlation between the descriptive parameters of the FNI cross-section (area, perimeter, and eccentricity) and patients' demographics (age, height, and weight) was analyzed. Stepwise linear regression analysis was used to determine the main relevant factors. RESULTS: The ICC results showed excellent data reproducibility ranged from 0.989 to 0.996. There was no significant difference in the cross-sectional area of the FNI between the actual measured values and the predicted values using the formula (732.83 ± 126.74 mm2 vs 731.62 ± 128.15 mm2 , P = 0.322). The perimeter using the two methods showed narrow while significant difference (97.86 ± 8.60 mm vs 92.84 ± 8.65 mm, P < 0.001), the actual measured values were about 5 mm greater than the predicted values. The parameters (area, perimeter, and eccentricity) were significantly larger in male than female (P < 0.001). A positive correlation between the cross-sectional area, perimeter, height, and weight was observed. The stepwise linear regression analysis showed that the regression equation of the FNI area was as follows: Y = -1083.75 + 1033.86 × HEIGHT + 1.92 × WEIGHT, R2 = 0.489. CONCLUSION: The cross-section shape of the FNI appears to be oval-like in the 3D model, which is separated according to the inertia axis, and the findings proposed an anatomical basis for the further study of the spatial configuration of cannulated screws in the treatment of femoral neck fractures.

Erratum to radiographic assessment of the cup orientation after total hip arthroplasty: a literature review.

[This corrects the article DOI: 10.21037/atm.2019.12.150.].