OBJECTIVE:A comprehensive understanding of the anatomical structure of the femur is crucial for optimizing surgical approaches and improving prosthesis design. This study aims to conduct a thorough analysis of the anatomical structure of the Chinese femur using statistical shape models (SSM), thereby providing scientific evidence for clinical applications. METHOD:In this study, the femoral CT data of 209 Chinese patients were collected for detailed 3D reconstruction to obtain a 3D model of the bilateral femur. Advanced 3D model alignment techniques and dense homologous mesh mapping methods were used to ensure the high accuracy and consistency of the models. Three-dimensional statistical shape modeling (SSM) and principal component analysis (PCA) methods were used to extract the main patterns of femoral morphology changes and further analyze the femoral morphology changes. On this basis, the effects of gender differences on femur morphology were further comparatively analyzed. RESULTS:This study successfully established a statistical shape model of the femur in the Chinese population and extracted patterns of femoral shape variation through principal component analysis. The first six principal components shape change patterns accounted for 82.7%, 3.4%, 2.7%, 2.5%, 2.0%, and 1.5% of the total change, respectively. The model of variation for each of the first six principal components accounted for more than 1% of the total anatomical variance and together explained 94.8% of the variance. PC01, PC02, PC03, PC04, and PC06 exhibited significant differences between sexes (p < 0.05). CONCLUSION:By constructing a three-dimensional statistical shape model of the femur, this study reveals individual morphological variations as well as differences based on sex. This model not only deepens the understanding of the anatomical morphology of the Chinese femur but also provides an important scientific basis for the optimization of clinical surgical plans and the improvement of prosthesis design.

For bone segmentation, the classical geodesic active contour model is usually limited by its indiscriminate feature extraction, and then struggles to handle the phenomena of edge obstruction, edge leakage and bone fracture. Thus, we propose a fracture interactive geodesic active contour algorithm tailored for bone segmentation, which can better capture bone features and perform robustly to the presence of bone fractures and soft tissues. Inspired by orthopedic knowledge, we construct a novel edge-detector function that combines the intensity and gradient norm, which guides the contour towards bone edges without being obstructed by other soft tissues and therefore reduces mis-segmentation. Furthermore, distance information, where fracture prompts can be embedded, is introduced into the contour evolution as an adaptive step size to stabilize the evolution and help the contour stop at bone edges and fractures. This embedding provides a way to interact with bone fractures and improves the accuracy in the fracture regions. Experiments in pelvic and ankle segmentation demonstrate the effectiveness on addressing the aforementioned problems and show an accurate, stable and consistent performance, indicating a broader application in other bone anatomies. Our algorithm also provides insights into combining the domain knowledge and deep neural networks.

ABSTRACT Objectives Clavicle fracture fixation is frequently complicated by implant mismatch and mechanical failure due to the complex and highly variable S‐shaped anatomy of the clavicle. Conventional morphometric classification systems rely on subjective assessments of curvature and fail to capture the continuous spectrum of clavicular shape variation, limiting their utility for personalized implant design and preoperative planning. Furthermore, large‐scale statistical shape modeling studies focusing on Asian populations remain scarce. Therefore, this study aimed to characterize clavicular morphology in an Asian cohort using statistical shape modeling (SSM), investigate sex‐ and side‐related differences, and evaluate the validity of traditional morphological classification systems. Methods A retrospective study analyzed 288 clavicles reconstructed from CT scans of 144 adults (94 females, 50 males). Three‐dimensional models were segmented in 3D Slicer, aligned, and processed using the Scalismo platform. Principal component analysis (PCA) was performed to establish the SSM and extract modes of variation (MoV). Morphometric parameters were calculated automatically. Independent t‐tests assessed sex and side differences, and clustering analysis was conducted to compare data‐driven groupings with traditional three‐type classifications. Results The first six MoV explained 82.38% of total variance. PC01 (50.84%) reflected clavicular length and midshaft width; PC02–PC06 represented curvature and rotational variations. Significant sex differences were observed in PC01, PC02, and PC06 (p < 0.05), whereas no side differences were detected. Agglomerative clustering identified two morphological groups with poor concordance with traditional three‐type classifications (Adjusted Rand Index≈0), indicating a continuous rather than discrete distribution of clavicular shapes. Conclusion Clavicular morphology exhibits sex‐dependent but not side‐dependent variability. Traditional categorical classifications inadequately capture anatomical diversity. Large‐scale SSM provides objective morphometric evidence to guide personalized preoperative planning and improve implant design in clavicle fracture fixation.

ABSTRACTBackgroundHigh tibial osteotomy (HTO) is a surgical procedure for treating certain knee conditions. Proper execution of HTO can preserve joint function and delay or avoid the need for total knee replacement. This study compared different 3D printing techniques (fused deposition modeling, selective laser sintering, and direct metal laser sintering) and a navigation system for their suitability in assisting HTO surgeries.MethodsTibial saw‐bones were used as models, and surgical guides and the navigation system were employed during the procedures. Six parameters (planning time, manufacturing time, delivery time, material cost, operation time, and accuracy) were evaluated. One‐way analysis of variance (ANOVA) and t‐test were used for the analysis.ResultsThe results showed that the metal surgical guides had the highest accuracy (angle differences mean, 2.4°) and operation time (mean 9.75 min), followed by plastic guides, classic guides, and the navigation system. The differences in accuracy were attributed to factors like rigidity, melting point, and errors during incisions.ConclusionsThe study recommended metal surgical guides as the best option for assisting HTO due to their accuracy and operation time. And the results have implications for orthopedic surgeons performing HTO surgeries, as they can use this information to improve postoperative outcomes, such as mechanical axis alignment and quality of life for HTO patients.

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.

BackgroundThis 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.MethodsThe 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.ResultsIn 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).ConclusionsThe 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.

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. (c) 2023 Elsevier Inc. All rights reserved.

To construct a three-dimensional statistical shape model of the pelvis and investigate its three-dimensional morphological changes. We collected CT data from 201 Chinese individuals and used deep learning to reconstruct three-dimensional models of the pelvis automatically. Through three-dimensional model registration, dense correspondence mesh mapping, and the use of statistical shape modeling (SSM) and principal component (PC) analysis methods, we extracted models of variations (MoVs) of pelvic shape changes and statistically compared the shape MoVs between males and females. We analyzed the top 10 principal components of shape variations, which accounted for 86.1% of the total variability. Among them, PC1, PC2, and PC4 showed significant differences between genders (p-values of 0.000, 0.000, and 0.010), accounting for a total variability of 60.1%. PC8 and PC10 demonstrated pelvic asymmetry, accounting for a total variability of 3.8%. We constructed a three-dimensional statistical shape model of the pelvis in Chinese individuals, deepening our understanding of anatomical variations in pelvic morphology. This model can also be further applied in anatomy education and implant design.

BackgroundPatients 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. ObjectiveThis 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. MethodsThis 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. ResultsIn 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. ConclusionsThis 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.

The annotation scarcity of medical image segmentation poses challenges in collecting sufficient training data for deep learning models. Specifically, models trained on limited data may not generalize well to other unseen data domains, resulting in a domain shift issue. Consequently, domain generalization (DG) is developed to boost the performance of segmentation models on unseen domains. However, the DG setup requires multiple source domains, which impedes the efficient deployment of segmentation algorithms in clinical scenarios. To address this challenge and improve the segmentation model's generalizability, we propose a novel approach called the Frequency-mixed Single-source Domain Generalization method (FreeSDG). By analyzing the frequency's effect on domain discrepancy, FreeSDG leverages a mixed frequency spectrum to augment the single-source domain. Additionally, self-supervision is constructed in the domain augmentation to learn robust context-aware representations for the segmentation task. Experimental results on five datasets of three modalities demonstrate the effectiveness of the proposed algorithm. FreeSDG outperforms state-of-the-art methods and significantly improves the segmentation model's generalizability. Therefore, FreeSDG provides a promising solution for enhancing the generalization of medical image segmentation models, especially when annotated data is scarce. The code is available at https://github.com/liamheng/Non-IID_Medical_Image_Segmentation.

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-1β 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.

BackgroundHealth-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.MethodsA 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.ResultsFour 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.ConclusionsThis 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.

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.

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/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.

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.

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.

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.

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.

Calcaneus is the most susceptible tarsal bone to fractures, presenting the most challenging treatment for associated tissue damage. Intra-articular calcaneal fractures present substantial challenges for patients and surgeons due to their association with both immediate and delayed complications. The purposes of this study were to establish a method for three dimensional morphological measurements of the normal calcaneus, we develop a statistical shape model (SSM) of the calcaneus that incorporates CT scans to enable a comprehensive assessment of its three-dimensional morphology. Though surface-based registration and point-wise correspondence analysis, the left and right calcaneus bones were compared with a variety of shape analysis. The compactness and parallel analysis test on the statistical shape model yielded 7 prominent shape modes of variations (MoVs), which accounted for approximately 89% of the total 3D variations in the population of shapes. Among these modes, two captured discriminating features from both the left and right calcaneus bones (p value < 0.05). Visual inspection confirmed that these two shape modes represented abnormalities in the anterior and anteromedial parts of the calcaneus, highlighting them as the primary bony risk factors in ankle injuries. In conclusion, our study utilizing a Statistical Shape Model (SSM) has identified significant shape variations (MoVs) of the calcaneus bone which correlate significantly with the left and right sides of the body. The results of our study also demonstrate the potential utility of the SSM as a tool for providing guidance in surgical planning and treatment of calcaneus pathologies.

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.