Is there an alternative to the Delbet-Colonna classification? Introduction and reliability assessment of a new classification system for paediatric femoral neck fractures: preliminary results.

PURPOSE: The purpose of this study was to introduce a new classification system for paediatric femoral neck fractures (PFNFs) and to evaluate its reliability. METHODS: Two hundred and eight unilateral PFNFs (mean patient age: 9.0 ± 4.8 years) were included. Based on preoperative radiographs, the new classification system distinguished PFNFs without anterior or posterior translation (Type I), PFNFs with anterior (Type II) or posterior (Type III) translation, PFNFs with a comminuted medial or posterior column (Type IV), and subtrochanteric femoral fractures (SFFs; Type V). Radiographs were evaluated twice with an interval of two weeks by 19 raters with different specialties, experiences and geographical origins. The results were compared with a selection of 50 patient age-matched unilateral PFNFs and SFFs (mean patient age: 9.1 ± 4.9 years). These were graded twice by the same graders according to the Delbet-Colonna (D-C) classification. RESULTS: Four radiologists and 15 paediatric orthopaedic surgeons from Europe and Asia graded the radiographs. Fair agreement was found between radiologists (κ = 0.296 ± 0.01) and surgeons (κ = 0.3 ± 0.005) (P = 0.17), although more experienced surgeons performed better than less experienced ones; a similar fair assessment was found for raters from Europe (κ = 0.309 ± 0.021) and Asia (κ = 0.3 ± 0.006) and for type II, III and IV fractures; the κ value in the first evaluation (0.309) was similar to that in the second evaluation (0.298). The overall κ value of the D-C classification subtypes was significantly higher (0.599 ± 0.217) than that of the new classification, 0.326 ± 0.162 (t = 3.190 P = 0.005). CONCLUSIONS: The new classification system showed fair reliability relative to the D-C classification. The reliability of the new classification system was not affected by the specialty or geographic origin of the rater or the evaluation round, only by rater experience level. The concordance was worse for PFNFs with anterior or posterior translation or with a comminuted medial or posterior columns.

Deep learning assisted diagnosis system: improving the diagnostic accuracy of distal radius fractures.

Objectives: To explore an intelligent detection technology based on deep learning algorithms to assist the clinical diagnosis of distal radius fractures (DRFs), and further compare it with human performance to verify the feasibility of this method. Methods: A total of 3,240 patients (fracture: n = 1,620, normal: n = 1,620) were included in this study, with a total of 3,276 wrist joint anteroposterior (AP) X-ray films (1,639 fractured, 1,637 normal) and 3,260 wrist joint lateral X-ray films (1,623 fractured, 1,637 normal). We divided the patients into training set, validation set and test set in a ratio of 7:1.5:1.5. The deep learning models were developed using the data from the training and validation sets, and then their effectiveness were evaluated using the data from the test set. Evaluate the diagnostic performance of deep learning models using receiver operating characteristic (ROC) curves and area under the curve (AUC), accuracy, sensitivity, and specificity, and compare them with medical professionals. Results: The deep learning ensemble model had excellent accuracy (97.03%), sensitivity (95.70%), and specificity (98.37%) in detecting DRFs. Among them, the accuracy of the AP view was 97.75%, the sensitivity 97.13%, and the specificity 98.37%; the accuracy of the lateral view was 96.32%, the sensitivity 94.26%, and the specificity 98.37%. When the wrist joint is counted, the accuracy was 97.55%, the sensitivity 98.36%, and the specificity 96.73%. In terms of these variables, the performance of the ensemble model is superior to that of both the orthopedic attending physician group and the radiology attending physician group. Conclusion: This deep learning ensemble model has excellent performance in detecting DRFs on plain X-ray films. Using this artificial intelligence model as a second expert to assist clinical diagnosis is expected to improve the accuracy of diagnosing DRFs and enhance clinical work efficiency.

Deep learning system assisted detection and localization of lumbar spondylolisthesis.

Objective: Explore a new deep learning (DL) object detection algorithm for clinical auxiliary diagnosis of lumbar spondylolisthesis and compare it with doctors' evaluation to verify the effectiveness and feasibility of the DL algorithm in the diagnosis of lumbar spondylolisthesis. Methods: Lumbar lateral radiographs of 1,596 patients with lumbar spondylolisthesis from three medical institutions were collected, and senior orthopedic surgeons and radiologists jointly diagnosed and marked them to establish a database. These radiographs were randomly divided into a training set (n = 1,117), a validation set (n = 240), and a test set (n = 239) in a ratio of 0.7 : 0.15: 0.15. We trained two DL models for automatic detection of spondylolisthesis and evaluated their diagnostic performance by PR curves, areas under the curve, precision, recall, F1-score. Then we chose the model with better performance and compared its results with professionals' evaluation. Results: A total of 1,780 annotations were marked for training (1,242), validation (263), and test (275). The Faster Region-based Convolutional Neural Network (R-CNN) showed better precision (0.935), recall (0.935), and F1-score (0.935) in the detection of spondylolisthesis, which outperformed the doctor group with precision (0.927), recall (0.892), f1-score (0.910). In addition, with the assistance of the DL model, the precision of the doctor group increased by 4.8%, the recall by 8.2%, the F1-score by 6.4%, and the average diagnosis time per plain X-ray was shortened by 7.139 s. Conclusion: The DL detection algorithm is an effective method for clinical diagnosis of lumbar spondylolisthesis. It can be used as an assistant expert to improve the accuracy of lumbar spondylolisthesis diagnosis and reduce the clinical workloads.

Deep learning-based algorithm improves radiologists' performance in lung cancer bone metastases detection on computed tomography.

Purpose: To develop and assess a deep convolutional neural network (DCNN) model for the automatic detection of bone metastases from lung cancer on computed tomography (CT). Methods: In this retrospective study, CT scans acquired from a single institution from June 2012 to May 2022 were included. In total, 126 patients were assigned to a training cohort (n = 76), a validation cohort (n = 12), and a testing cohort (n = 38). We trained and developed a DCNN model based on positive scans with bone metastases and negative scans without bone metastases to detect and segment the bone metastases of lung cancer on CT. We evaluated the clinical efficacy of the DCNN model in an observer study with five board-certified radiologists and three junior radiologists. The receiver operator characteristic curve was used to assess the sensitivity and false positives of the detection performance; the intersection-over-union and dice coefficient were used to evaluate the segmentation performance of predicted lung cancer bone metastases. Results: The DCNN model achieved a detection sensitivity of 0.894, with 5.24 average false positives per case, and a segmentation dice coefficient of 0.856 in the testing cohort. Through the radiologists-DCNN model collaboration, the detection accuracy of the three junior radiologists improved from 0.617 to 0.879 and the sensitivity from 0.680 to 0.902. Furthermore, the mean interpretation time per case of the junior radiologists was reduced by 228 s (p = 0.045). Conclusions: The proposed DCNN model for automatic lung cancer bone metastases detection can improve diagnostic efficiency and reduce the diagnosis time and workload of junior radiologists.

Laminin α1 as a target for the treatment of epidural fibrosis by regulating fibrotic mechanisms.

Excessive proliferation and migration of fibroblasts in the lumbar laminectomy area can lead to epidural fibrosis, eventually resulting in failed back surgery syndrome. It has been reported that laminin α1, a significant biofunctional glycoprotein in the extracellular matrix, is involved in several fibrosis­related diseases, such as pulmonary, liver and keloid fibrosis. However, the underlying mechanism of laminin α1 in epidural fibrosis remains unknown. The present study aimed to explore the effect and mechanism of laminin α1 in fibroblast proliferation, apoptosis and migration, and epidural fibrosis. Following the establishment of a laminectomy model, hematoxylin and eosin, Masson's trichrome and immunohistochemical staining were performed to determine the degree of epidural fibrosis, the number of fibroblasts, collagen content and the epidural expression levels of laminin α1, respectively. Furthermore, a stable small interfering RNA system was used to knock down the expression of laminin α1 in fibroblasts. The transfection efficiency was confirmed by reverse transcription­quantitative PCR and immunofluorescence staining. Western blot analysis, scratch wound assay, EdU incorporation assay, flow cytometric analysis and Cell Counting Kit 8 assay were performed to assess the proliferation, apoptosis, migration and viability of fibroblasts, as well as the expression levels of the AKT/mechanistic target of rapamycin (mTOR) signaling­related proteins. In vivo experiments revealed that laminin α1 was positively and time­dependently associated with epidural fibrosis. In addition, laminin α1 knockdown attenuated cell proliferation, viability and migration, and promoted apoptosis. Furthermore, the results revealed that the activation of the AKT/mTOR signaling pathway was involved in the aforementioned processes. Overall, the current study illustrated the positive association between laminin α1 and epidural fibrosis, and also verified the effect of laminin α1 on fibroblast proliferation, apoptosis and migration. Furthermore, the results suggested that the AKT/mTOR signaling pathway may serve a significant role in regulating the behavior of laminin α1­induced fibroblasts.

HSA-templated self-generation of gold nanoparticles for tumor vaccine delivery and combinational therapy.

Drug delivery systems (DDS) play a vital role in the construction of tumor vaccines and can promote their therapeutic effect. Taking advantage of the versatile binding sites and bioreduction ability of human serum albumin (HSA), Au ions could be absorbed, reduced and nucleated to generate gold nanoparticles (AuNPs) on HSA without complicated intermediates, forming a DDS that can transform light to heat. Here, we designed self-generated AuNPs templated by HSA (HSA@AuNP). The HSA@AuNPs can deliver peptides, amplify the immune response and achieve combined photothermal therapy and immunotherapy. Human melanoma antigen gp10025-33 (hgp100) peptide, a common hydrophilic tumor vaccine peptide that can be easily encapsulated in HSA, was chosen to be incorporated into the HSA@AuNPs. The in vitro and in vivo studies demonstrated that the nanoparticles can mediate light-to-heat transduction under near-infrared irradiation (NIR), achieving tumor ablation and enhancing antitumor immunity. Our design can insulate toxic agents, streamline flux, increase the transition efficiency of interactants and improve the product yield, contributing a novel modality for facile and green synthesis of nanovaccines.

Artificial intelligence to detect the femoral intertrochanteric fracture: The arrival of the intelligent-medicine era.

Objective: To explore a new artificial intelligence (AI)-aided method to assist the clinical diagnosis of femoral intertrochanteric fracture (FIF), and further compare the performance with human level to confirm the effect and feasibility of the AI algorithm. Methods: 700 X-rays of FIF were collected and labeled by two senior orthopedic physicians to set up the database, 643 for the training database and 57 for the test database. A Faster-RCNN algorithm was applied to be trained and detect the FIF on X-rays. The performance of the AI algorithm such as accuracy, sensitivity, miss diagnosis rate, specificity, misdiagnosis rate, and time consumption was calculated and compared with that of orthopedic attending physicians. Results: Compared with orthopedic attending physicians, the Faster-RCNN algorithm performed better in accuracy (0.88 vs. 0.84 ± 0.04), specificity (0.87 vs. 0.71 ± 0.08), misdiagnosis rate (0.13 vs. 0.29 ± 0.08), and time consumption (5 min vs. 18.20 ± 1.92 min). As for the sensitivity and missed diagnosis rate, there was no statistical difference between the AI and orthopedic attending physicians (0.89 vs. 0.87 ± 0.03 and 0.11 vs. 0.13 ± 0.03). Conclusion: The AI diagnostic algorithm is an available and effective method for the clinical diagnosis of FIF. It could serve as a satisfying clinical assistant for orthopedic physicians.

Applications of Mixed Reality Technology in Orthopedics Surgery: A Pilot Study.

Objective: The aim of this study is to explore the potential of mixed reality (MR) technology in the visualization of orthopedic surgery. Methods: The visualization system with MR technology is widely used in orthopedic surgery. The system is composed of a 3D imaging workstation, a cloud platform, and an MR space station. An intelligent segmentation algorithm is adopted on the 3D imaging workstation to create a 3D anatomical model with zooming and rotation effects. This model is then exploited for efficient 3D reconstruction of data for computerized tomography (CT) and magnetic resonance imaging (MRI). Additionally, the model can be uploaded to the cloud platform for physical parameter tuning, model positioning, rendering and high-dimensional display. Using Microsoft's HoloLens glasses in combination with the MR system, we project and view 3D holograms in real time under different clinical scenarios. After each procedure, nine surgeons completed a Likert-scale questionnaire on communication and understanding, spatial awareness and effectiveness of MR technology use. In addition to that, the National Aeronautics and Space Administration Task Load Index (NASA-TLX) is also used to evaluate the workload of MR hologram support. Results: 1) MR holograms can clearly show the 3D structures of bone fractures, which improves the understanding of different fracture types and the design of treatment plans; 2) Holograms with three-dimensional lifelike dynamic features provide an intuitive communication tool among doctors and also between doctors and patients; 3) During surgeries, a full lesion hologram can be obtained and blended in real time with a patient's virtual 3D digital model in order to give surgeons superior visual guidance through novel high-dimensional "perspectives" of the surgical area; 4) Hologram-based magnetic navigation improves the accuracy and safety of the screw placement in orthopaedics surgeries; 5) The combination of mixed reality cloud platform and telemedicine system based on 5G provides a new technology platform for telesurgery collaboration. Results of qualitative study encourage the usage of MR technology for orthopaedics surgery. Analysis of the Likert-scale questionnaire shows that MR adds significant value to understanding and communication, spatial awareness, learning and effectiveness. Based on the NASA TLX-scale questionnaire results, mixed reality scored significantly lower under the "mental," "temporal," "performance," and "frustration" categories compared to usual 2D. Conclusion: The integration of MR technology in orthopaedic surgery reduces the dependence on surgeons' experience and provides personalized 3D visualization models for accurate diagnosis and treatment of orthopaedic abnormalities. This integration is clearly one of the prominent future development directions in medical surgery.

Application and Development of Intelligent Medicine in Traditional Chinese Medicine.

As modern science and technology constantly progresses, the fields of artificial intelligence, mixed reality technology, remote technology, etc. have rapidly developed. Meanwhile, these technologies have been gradually applied to the medical field, leading to the development of intelligent medicine. What's more, intelligent medicine has greatly promoted the development of traditional Chinese medicine (TCM), causing huge changes in the diagnosis of TCM ailments, remote treatment, teaching, etc. Therefore, there are both opportunities and challenges for inheriting and developing TCM. Herein, the related research progress of intelligent medicine in the TCM in China and abroad over the years is analyzed, with the purpose of introducing the present application status of intelligent medicine in TCM and providing reference for the inheritance and development of TCM in a new era.

Artificial Intelligence to Diagnose Tibial Plateau Fractures: An Intelligent Assistant for Orthopedic Physicians.

OBJECTIVE: To explore a new artificial intelligence (AI)-aided method to assist the clinical diagnosis of tibial plateau fractures (TPFs) and further measure its validity and feasibility. METHODS: A total of 542 X-rays of TPFs were collected as a reference database. An AI algorithm (RetinaNet) was trained to analyze and detect TPF on the X-rays. The ability of the AI algorithm was determined by indexes such as detection accuracy and time taken for analysis. The algorithm performance was also compared with orthopedic physicians. RESULTS: The AI algorithm showed a detection accuracy of 0.91 for the identification of TPF, which was similar to the performance of orthopedic physicians (0.92±0.03). The average time spent for analysis of the AI was 0.56 s, which was 16 times faster than human performance (8.44±3.26 s). CONCLUSION: The AI algorithm is a valid and efficient method for the clinical diagnosis of TPF. It can be a useful assistant for orthopedic physicians, which largely promotes clinical workflow and further guarantees the health and security of patients.

Application of Artificial Intelligence in Medicine: An Overview.

Artificial intelligence (AI) is a new technical discipline that uses computer technology to research and develop the theory, method, technique, and application system for the simulation, extension, and expansion of human intelligence. With the assistance of new AI technology, the traditional medical environment has changed a lot. For example, a patient's diagnosis based on radiological, pathological, endoscopic, ultrasonographic, and biochemical examinations has been effectively promoted with a higher accuracy and a lower human workload. The medical treatments during the perioperative period, including the preoperative preparation, surgical period, and postoperative recovery period, have been significantly enhanced with better surgical effects. In addition, AI technology has also played a crucial role in medical drug production, medical management, and medical education, taking them into a new direction. The purpose of this review is to introduce the application of AI in medicine and to provide an outlook of future trends.

Applications of Blockchain in the Medical Field: Narrative Review.

BACKGROUND: As a distributed technology, blockchain has attracted increasing attention from stakeholders in the medical industry. Although previous studies have analyzed blockchain applications from the perspectives of technology, business, or patient care, few studies have focused on actual use-case scenarios of blockchain in health care. In particular, the outbreak of COVID-19 has led to some new ideas for the application of blockchain in medical practice. OBJECTIVE: This paper aims to provide a systematic review of the current and projected uses of blockchain technology in health care, as well as directions for future research. In addition to the framework structure of blockchain and application scenarios, its integration with other emerging technologies in health care is discussed. METHODS: We searched databases such as PubMed, EMBASE, Scopus, IEEE, and Springer using a combination of terms related to blockchain and health care. Potentially relevant papers were then compared to determine their relevance and reviewed independently for inclusion. Through a literature review, we summarize the key medical scenarios using blockchain technology. RESULTS: We found a total of 1647 relevant studies, 60 of which were unique studies that were included in this review. These studies report a variety of uses for blockchain and their emphasis differs. According to the different technical characteristics and application scenarios of blockchain, we summarize some medical scenarios closely related to blockchain from the perspective of technical classification. Moreover, potential challenges are mentioned, including the confidentiality of privacy, the efficiency of the system, security issues, and regulatory policy. CONCLUSIONS: Blockchain technology can improve health care services in a decentralized, tamper-proof, transparent, and secure manner. With the development of this technology and its integration with other emerging technologies, blockchain has the potential to offer long-term benefits. Not only can it be a mechanism to secure electronic health records, but blockchain also provides a powerful tool that can empower users to control their own health data, enabling a foolproof health data history and establishing medical responsibility.

Mixed reality assists the fight against COVID-19.

Coronavirus disease 2019 (COVID-19) made a huge effect globally. With the assistance of mixed reality (MR) technology, complicated clinical works became easier to carry out and the condition had been greatly improved with high-tech advantages such as improved convenience, better understanding and communication, higher security, and medical resource saving. This study aimed to introduce one kind of MR application in the fight against COVID-19 and anticipate more feasible smart healthcare applications to enhance our strength for the final victory.

Running Promotes Transformation of Brain Astrocytes Into Neuroprotective Reactive Astrocytes and Synaptic Formation by Targeting Gpc6 Through the STAT3 Pathway.

Ischemic stroke is caused by cerebral ischemia upon the blockage of an artery, which results in a high disability rate. Little is known regarding the mechanism of astrocyte function in cerebral ischemia. We aimed to determine the effects of running on the transformation of astrocytes, and subsequent synapse formation. A study of middle cerebral artery occlusion (MCAO) after running in vivo showed that running can promote the transformation of astrocytes toward the neuroprotective phenotype. Our findings of oxygen-glucose deprived astrocytes in vitro after running revealed that these astrocytes transformed into the neuroprotective phenotype, and that the expression of STAT3 and Gpc6 was increased. We confirmed that mechanistically, running can target Gpc6 through the STAT3 pathway and then regulate the number of synapses. We concluded that running promotes synapse proliferation by polarizing astrocytes toward the neuroprotective phenotype and ultimately leads to nerve regeneration.

Risk factors for femoral neck fracture in elderly population. / è€å¹´è‚¡éª¨é¢ˆéª¨æŠ˜çš„å±é™©å› ç´ .

OBJECTIVES: To explore the risk factors for femoral neck fracture in elderly population. METHODS: A total of 124 elderly patients (≥60 years old) in hospital for trauma were enrolled, including 71 patients (57%) with femoral neck fracture and 53 non-femoral neck fracture patients (43%). All patients' age, gender, body mass index (BMI), bone mineral density (BMD), thigh length and average circumference were collected. Single factor analysis and multivariate logistic regression analysis were performed to explore whether the above factors were risk factors for femoral neck fracture. RESULTS: Single factor analysis showed that the age, gender, BMI, BMD, thigh length, and average thigh circumference between the 2 groups were statistically different (all P<0.05), and multi-factor logistic regression analysis showed that age, gender, BMI, BMD, thigh length, and average thigh circumference were influencing factors for femoral neck fracture in elderly population (all P<0.05). CONCLUSIONS: Older age, female, lower BMI index (low body weight), lower BMD (osteoporosis), longer thigh length, and lower average circumference are risk factors for femoral neck fracture in the elderly population.

Mesenchymal Stem Cell-Derived Exosomes as a Novel Strategy for the Treatment of Intervertebral Disc Degeneration.

Intervertebral disc degeneration (IVDD) has been reported to be the most prevalent contributor to low back pain, posing a significant strain on the healthcare systems on a global scale. Currently, there are no approved therapies available for the prevention of the progressive degeneration of intervertebral disc (IVD); however, emerging regenerative strategies that aim to restore the normal structure of the disc have been fundamentally promising. In the last decade, mesenchymal stem cells (MSCs) have received a significant deal of interest for the treatment of IVDD due to their differentiation potential, immunoregulatory capabilities, and capability to be cultured and regulated in a favorable environment. Recent investigations show that the pleiotropic impacts of MSCs are regulated by the production of soluble paracrine factors. Exosomes play an important role in regulating such effects. In this review, we have summarized the current treatments for disc degenerative diseases and their limitations and highlighted the therapeutic role and its underlying mechanism of MSC-derived exosomes in IVDD, as well as the possible future developments for exosomes.

The efficacy of 3D printing-assisted surgery in treating distal radius fractures: systematic review and meta-analysis.

Aim: To compare the efficacy of 3D printing-assisted surgery with routine surgery in the treatment of distal radius fractures to evaluate whether 3D printing technology has more advantages. Materials & methods: To retrieve all published studies that compared the efficacy of 3D printing-assisted surgery with routine surgery for distal radius fractures. Operation time, frequency of intraoperative fluoroscopy, blood loss and other outcomes were assessed. Results: The results suggested that 3D printing-assisted surgery was better than routine surgery in the fields of operation time, frequency of intraoperative fluoroscopy, and blood loss. Conclusion: In the treatment of distal radius fractures, 3D printing-assisted surgery may be superior to routine surgery.

Laminin α5 modulates fibroblast proliferation in epidural fibrosis through the PI3K/AKT/mTOR signaling pathway.

Lumbar laminectomy is commonly deemed as the most valid surgery for a series of lumbar illnesses, such as lumbar disc herniation, which could lead to spinal canal stenosis. However, epidural fibrosis is one of the most common complications that limits the application of lumbar laminectomy, which is mainly caused by proliferation of local fibroblasts. Laminins are glycoproteins that consist of α, ß and γ chains, which serve a crucial role in biological cell behaviors, such as adhesion, differentiation, migration and proliferation, especially the isoform with the fifth α chain­laminin α5. The PI3K/AKT/mTOR signaling pathway was demonstrated to be associated with various biological functions in cells. The aim of the present study was to explore whether laminin α5 is an important factor in epidural fibrosis by modulating the proliferation of fibroblasts through the activation of PI3K/AKT/mTOR signaling pathway. In the animal model, the results of the hematoxylin­eosin staining, cell counting, Masson's trichrome staining and immunohistochemical staining showed laminin α5 to be positively associated with epidural fibrosis. Furthermore, to verify the assumption that laminin α5 could modulate fibroblast proliferation through the PI3K/AKT/mTOR signal pathway, fibroblasts were transfected with laminin α5­small interfering (si)RNA. The results of western blotting (proliferating cell nuclear antigen and cyclin D1), the Cell Counting Kit­8 and EdU incorporation assays indicated that the proliferative level of fibroblasts decreased, and the expression of phosphorylated (p)­focal adhesion kinase 1, p­AKT and p­mTOR was reduced. Subsequently, laminin α5 was overexpressed and the change in cell proliferation and expression of associated proteins contrasted with that observed in siRNA. The results demonstrated that laminin α5 could interfere the activation of the PI3K/AKT/mTOR signaling pathway. Finally, the inhibition of the PI3K/AKT/mTOR signaling pathway by LY294002 resulted in decreased fibroblast proliferation. In conclusion, laminin α5 could modulate fibroblast proliferation in epidural fibrosis through the PI3K/AKT/mTOR signaling pathway.