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Prosthesis-associated infections are one of the main causes of implant failure; thus it is important to enhance the long-term antibacterial ability of orthopedic implants. Titanium dioxide nanotubes (TNTs) are biomaterials with good physicochemical properties and biocompatibility. Owing to their inherent antibacterial and drug-loading ability, the antibacterial application of TNTs has received increasing attention. In this review, the process of TNT anodizing fabrication is summarized. Also, the mechanism and the influencing factors of the antibacterial property of bare TNTs are explored. Furthermore, different antibacterial strategies for carrying drugs, as well as modifications to prolong the antibacterial effect and reduce drug-related toxicity are discussed. In addition, antibacterial systems based on TNTs that can automatically respond to infection are introduced. Finally, the currently faced problems are reviewed and potential solutions are proposed. This review provides new insight on TNT fabrication and summarizes the most advanced antibacterial strategies involving TNTs for the enhancement of long-term antibacterial ability and reduction of toxicity. Keywords: antibacterial property, drug delivery, titanium dioxide nanotube, orthopedic implant, surface modification

Background Accurately diagnosing supraspinatus tears based on magnetic resonance imaging (MRI) is challenging and time-combusting due to the experience level variability of the musculoskeletal radiologists and orthopedic surgeons. We developed a deep learning-based model for automatically diagnosing supraspinatus tears (STs) using shoulder MRI and validated its feasibility in clinical practice. Materials and methods A total of 701 shoulder MRI data (2804 images) were retrospectively collected for model training and internal test. An additional 69 shoulder MRIs (276 images) were collected from patients who underwent shoulder arthroplasty and constituted the surgery test set for clinical validation. Two advanced convolutional neural networks (CNN) based on Xception were trained and optimized to detect STs. The diagnostic performance of the CNN was evaluated according to its sensitivity, specificity, precision, accuracy, and F1 score. Subgroup analyses were performed to verify its robustness, and we also compared the CNN’s performance with that of 4 radiologists and 4 orthopedic surgeons on the surgery and internal test sets. Results Optimal diagnostic performance was achieved on the 2D model, from which F1-scores of 0.824 and 0.75, and areas under the ROC curves of 0.921 (95% confidence interval, 0.841–1.000) and 0.882 (0.817–0.947) were observed on the surgery and internal test sets. For the subgroup analysis, the 2D CNN model demonstrated a sensitivity of 0.33–1.000 and 0.625–1.000 for different degrees of tears on the surgery and internal test sets, and there was no significant performance difference between 1.5 and 3.0 T data. Compared with eight clinicians, the 2D CNN model exhibited better diagnostic performance than the junior clinicians and was equivalent to senior clinicians. Conclusions The proposed 2D CNN model realized the adequate and efficient automatic diagnoses of STs, which achieved a comparable performance of junior musculoskeletal radiologists and orthopedic surgeons. It might be conducive to assisting poor-experienced radiologists, especially in community scenarios lacking consulting experts.

Fungal diseases have posed a great challenge to global health, but have fewer solutions compared to bacterial and viral infections. Development and application of new treatment modalities for fungi are limited by their inherent essential properties as eukaryotes. The microorganism identification and drug sensitivity analyze are limited by their proliferation rates. Moreover, there are currently no vaccines for prevention. Polymer science and related interdisciplinary technologies have revolutionized the field of fungal disease management. To date, numerous advanced polymer-based systems have been developed for management of fungal diseases, including prevention, diagnosis, treatment and monitoring. In this review, we provide an overview of current needs and advances in polymer-based strategies against fungal diseases. We high light various treatment modalities. Delivery systems of antifungal drugs, systems based on polymers’ innate antifungal activities, and photodynamic therapies each follow their own mechanisms and unique design clues. We also discuss various prevention strategies including immunization and antifungal medical devices, and further describe point-of-care testing platforms as futuristic diagnostic and monitoring tools. The broad application of polymer-based strategies for both public and personal health management is prospected and integrated systems have become a promising direction. However, there is a gap between experimental studies and clinical translation. In future, well-designed in vivo trials should be conducted to reveal the underlying mechanisms and explore the efficacy as well as biosafety of polymer-based products.

With the increase in electricity demand, the ampacity calculation based on the dynamic thermal rating (DTR) technology is increasingly significant for assessing and improving the power transfer capacity of the existing overhead conductors. However, the DTR models now available present some inadequacies in measurement techniques related to wind speed. Therefore, it is essential to propose a new model instead of wind speed measuring in DTR technology. In this paper, the influence analysis of various weather parameters on the conductor ampacity is carried out by using the real weather data. Based on the analysis, it is confirmed that the impact of wind speed is significant, especially in the case of the low wind speed. Moreover, an equivalent heat transfer (EHT) model for DTR technology is proposed instead of wind speed measuring. For this EHT model, the calculation of conductor ampacity is realized through investigating the correlation of heat losses between the heating aluminum (Al) ball and conductor. Finally, combined with the finite element method (FEM), the EHT model proposed in this paper is verified by the Institute of Electrical and Electronic Engineers (IEEE) standard. The results indicate that the error of the EHT model is less than 6% when employing the steady thermal behavior of the Al ball to calculate the ampacity. The EHT model is useful in the real-time thermal rating of overhead conductors. It can increase the utilization of overhead conductors while also avoiding the limitation of the existing measurement techniques related to wind speed.

Ground wire breakage accidents can destroy the stable operation of overhead lines. The excessive temperature increase arising from the contact resistance between the ground wire and armor rod in the contact terminal is one of the main reasons causing the breakage of ground wires. Therefore, it is necessary to calculate the equivalent resistance for ground wires twined with armor rods in contact terminals. According to the actual distribution characteristics of the contact points in the contact terminal, a three-dimensional electromagnetic field simulation model of the contact terminal was established. Based on the model, the current distribution in the contact terminal was obtained. Subsequently, the equivalent resistance of a ground wire twined with the armor rod in the contact terminal was calculated. The effects of the factors influencing the equivalent resistance were also discussed. The corresponding verification experiments were conducted on a real ground wire on a contact terminal. The measurement results of the equivalent resistance for the armor rod segment showed good agreement with the electromagnetic modeling results.

BackgroundCystic lesions are frequently observed in knee joint diseases and are usually associated with joint pain, degenerative disorders, or acute injury. Magnetic resonance imaging-based, artificial intelligence-assisted cyst detection is an effective method to improve the whole knee joint analysis. However, few studies have investigated this method. This study is the first attempt at auto-detection of knee cysts based on deep learning methods.MethodsThis retrospective study collected data from 282 subjects with knee cysts confirmed at our institution from January to October 2021. A Squeeze-and-Excitation (SE) inception attention-based You only look once version 5 (SE-YOLOv5) model was developed based on a self-attention mechanism for knee cyst-like lesion detection and differentiation from knee effusions, both characterized by high T2-weighted signals in magnetic resonance imaging (MRI) scans. Model performance was evaluated via metrics including accuracy, precision, recall, mean average precision (mAP), F1 score, and frames per second (fps).ResultsThe deep learning model could accurately identify knee MRI scans and auto-detect both obvious cyst lesions and small ones with inconspicuous contrasts. The SE-YOLO V5 model constructed in this study yielded superior performance (F1 = 0.879, precision = 0.887, recall = 0.872, all class mAP0.5 = 0.944, effusion mAP = 0.945, cyst mAP = 0.942) and improved detection speed compared to a traditional YOLO model.ConclusionThis proof-of-concept study examined whether deep learning models could detect knee cysts and distinguish them from knee effusions. The results demonstrated that the classical Yolo V5 and proposed SE-Yolo V5 models could accurately identify cysts.

Background: The diagnosis of osteoporosis is still one of the most critical topics for orthopedic surgeons worldwide. One research direction is to use existing clinical imaging data for accurate measurements of bone mineral density (BMD) without additional radiation. Methods: A novel phantom-less quantitative computed tomography (PL-QCT) system was developed to measure BMD and diagnose osteoporosis, as our previous study reported. Compared with traditional phantom-less QCT, this tool can conduct an automatic selection of body tissues and complete the BMD calibration with high efficacy and precision. The function has great advantages in big data screening and thus expands the scope of use of this novel PL-QCT. In this study, we utilized lung cancer or COVID-19 screening low-dose computed tomography (LDCT) of 649 patients for BMD calibration by the novel PL-QCT, and we made the BMD changes with age based on this PL-QCT. Results: The results show that the novel PL-QCT can predict osteoporosis with relatively high accuracy and precision using LDCT, and the AUC values range from 0.68 to 0.88 with DXA results as diagnosis reference. The relationship between PL-QCT BMD with age is close to the real trend population (from ∼160 mg/cc in less than 30 years old to ∼70 mg/cc in greater than 80 years old for both female and male groups). Additionally, the calculation results of Pearson’s r-values for correlation between CT values with BMD in different CT devices were 0.85–0.99. Conclusion: To our knowledge, it is the first time for automatic PL-QCT to evaluate the performance against dual-energy X-ray absorptiometry (DXA) in LDCT images. The results indicate that it may be a promising tool for individuals screened for low-dose chest computed tomography.

Prosthesis-associated infections and aseptic loosening are major causes of implant failure. There is an urgent need to improve the antibacterial ability and osseointegration of orthopedic implants. Zinc oxide nanoparticles (ZnO-NPs) are a common type of zinc-containing metal oxide nanoparticles that have been widely studied in many fields, such as food packaging, pollution treatment, and biomedicine. The ZnO-NPs have low toxicity and good biological functions, as well as antibacterial, anticancer, and osteogenic capabilities. Furthermore, ZnO-NPs can be easily obtained through various methods. Among them, green preparation methods can improve the bioactivity of ZnO-NPs and strengthen their potential application in the biological field. This review discusses the antibacterial abilities of ZnO-NPs, including mechanisms and influencing factors. The toxicity and shortcomings of anticancer applications are summarized. Furthermore, osteogenic mechanisms and synergy with other materials are introduced. Green preparation methods are also briefly reviewed.

When overhead ground wire (OGW) is struck by lightning, the damage or rupture accident occur. This is unacceptable for the stable operation of the power system. Therefore, the investigation on the lightning-induced damage mechanism of OGW is significant for the optimization of lightning protection measures. Combined with the structural characteristics of OGW, this paper evaluated the thermal ablation damage of OGW caused by lightning strike, which provided the data support for the damage mechanism research. Firstly, the magnetohydrodynamics (MHD) models based on OGW and plate structure were established. The current density distributions as well as the arc root radii of OGW and plate under lightning strikes were compared. The thermal ablation damage model of OGW was also established. The influence of arc root radius on thermal ablation damage evaluation of OGW under continuing component of lightning current was analyzed. The results show that the arc root radius of lightning striking OGW is reduced by at least 50% compared with that of lightning striking plate. When using arc root radius of OGW for modeling, the results of the thermal ablation damage model are consistent with the reported experimental results. The maximum error is 9.80%. While the arc root radius of plate is adopted, there is an obvious difference between the thermal ablation damage model and the experimental results. The maximum error exceeds 20%. Therefore, in the numerical modeling of thermal ablation damage of OGW, it is necessary to consider the influence of structural characteristics of OGW on arc root radius.