Application of multiplanar reconstruction and 3D printing in anterior cruciate ligament revision. / 多平面重建及3D打印在前交叉韧带翻修术中的应用.

OBJECTIVES: Anterior cruciate ligament injury is the most common type of knee joint ligament injury. Anterior cruciate ligament reconstruction has a high failure rate, with bone tunnel abnormalities as the most significant factor in these failures. Digital orthopedic technology can effectively develop implementation plans for the revision, thus increasing the success rate. This study aims to develop a surgical plan for anterior cruciate ligament revision by employing multiplanar reconstruction (MPR) for measuring bone tunnel position and diameter, and simulating bone tunnel creation via 3D printing preoperatively. METHODS: A total of 12 patients who underwent anterior cruciate ligament revision at the Third Xiangya Hospital of Central South University between 2014 and 2021 were retrospectively studied. The data included patient demographics, preoperative formulated knee joint 3D printing models, and preoperative knee CT scans. The study measured the bone tunnel's diameter and position to guide the establishment of revision bone tunnels during surgery, reassessed the postoperative bone tunnels, and evaluated knee joint functional scores [including International Knee Documentation Committee Knee Evaluation Form (IKDC) score, Lysholm score, and Tegner exercise level score]. RESULTS: Preoperative measurements revealed suboptimal femoral tunnels positions in 4 patients and tibial tunnels positions in 2 patients. MPR and 3D printing technology were used to guide the establishment of a new bone canal during surgery, and postoperative measurements were satisfactory for all patients. Preoperative measurements demonstrated the interclass correlation coefficient for femoral tunnels and tibial tunnels diameters were 0.843 (P<0.05) and 0.889 (P<0.001), respectively. Meanwhile, the intraclass correlation coefficient were 0.811 (P<0.05) and 0.784 (P<0.05), respectively. The intraoperative diameter of femoral and tibial tunnels showed excellent correlation with postoperative CT measurements, with intraclass correlation coefficient values of 0.995 (P<0.001) and 0.987 (P<0.001), respectively. All bone tunnel positions were within the normal range. At the final follow-up, knee joint function scores in all 12 patients improved significantly compared to pre-surgery (P<0.001), and the reoperation rate was zero. CONCLUSIONS: MPR and 3D printing technology can accurately measure the parameters of reconstructed anterior cruciate ligament bone tunnels. Personalized revision plans for patients with reconstruction failure enhances the success rate of revision surgery and improves patient prognosis.

Lightweight Attentive Graph Neural Network with Conditional Random Field for Diagnosis of Anterior Cruciate Ligament Tear.

Anterior cruciate ligament (ACL) tears are prevalent orthopedic sports injuries and are difficult to precisely classify. Previous works have demonstrated the ability of deep learning (DL) to provide support for clinicians in ACL tear classification scenarios, but it requires a large quantity of labeled samples and incurs a high computational expense. This study aims to overcome the challenges brought by small and imbalanced data and achieve fast and accurate ACL tear classification based on magnetic resonance imaging (MRI) of the knee. We propose a lightweight attentive graph neural network (GNN) with a conditional random field (CRF), named the ACGNN, to classify ACL ruptures in knee MR images. A metric-based meta-learning strategy is introduced to conduct independent testing through multiple node classification tasks. We design a lightweight feature embedding network using a feature-based knowledge distillation method to extract features from the given images. Then, GNN layers are used to find the dependencies between samples and complete the classification process. The CRF is incorporated into each GNN layer to refine the affinities. To mitigate oversmoothing and overfitting issues, we apply self-boosting attention, node attention, and memory attention for graph initialization, node updating, and correlation across graph layers, respectively. Experiments demonstrated that our model provided excellent performance on both oblique coronal data and sagittal data with accuracies of 92.94% and 91.92%, respectively. Notably, our proposed method exhibited comparable performance to that of orthopedic surgeons during an internal clinical validation. This work shows the potential of our method to advance ACL diagnosis and facilitates the development of computer-aided diagnosis methods for use in clinical practice.

Explainable cancer factors discovery: Shapley additive explanation for machine learning models demonstrates the best practices in the case of pancreatic cancer.

Pancreatic cancer is one of digestive tract cancers with high mortality rate. Despite the wide range of available treatments and improvements in surgery, chemotherapy, and radiation therapy, the five-year prognosis for individuals diagnosed pancreatic cancer remains poor. There is still research to be done to see if immunotherapy may be used to treat pancreatic cancer. The goals of our research were to comprehend the tumor microenvironment of pancreatic cancer, found a useful biomarker to assess the prognosis of patients, and investigated its biological relevance. In this paper, machine learning methods such as random forest were fused with weighted gene co-expression networks for screening hub immune-related genes (hub-IRGs). LASSO regression model was used to further work. Thus, we got eight hub-IRGs. Based on hub-IRGs, we created a prognosis risk prediction model for PAAD that can stratify accurately and produce a prognostic risk score (IRG_Score) for each patient. In the raw data set and the validation data set, the five-year area under the curve (AUC) for this model was 0.9 and 0.7, respectively. And shapley additive explanation (SHAP) portrayed the importance of prognostic risk prediction influencing factors from a machine learning perspective to obtain the most influential certain gene (or clinical factor). The five most important factors were TRIM67, CORT, PSPN, SCAMP5, RFXAP, all of which are genes. In summary, the eight hub-IRGs had accurate risk prediction performance and biological significance, which was validated in other cancers. The result of SHAP helped to understand the molecular mechanism of pancreatic cancer.

A Digital Mask-Voiceprint System for Postpandemic Surveillance and Tracing Based on the STRONG Strategy.

Lockdowns and border closures due to COVID-19 imposed mental, social, and financial hardships in many societies. Living with the virus and resuming normal life are increasingly being advocated due to decreasing virus severity and widespread vaccine coverage. However, current trends indicate a continued absence of effective contingency plans to stop the next more virulent variant of the pandemic. The COVID-19-related mask waste crisis has also caused serious environmental problems and virus spreads. It is timely and important to consider how to precisely implement surveillance for the dynamic clearance of COVID-19 and how to efficiently manage discarded masks to minimize disease transmission and environmental hazards. In this viewpoint, we sought to address this issue by proposing an appropriate strategy for intelligent surveillance of infected cases and centralized management of mask waste. Such an intelligent strategy against COVID-19, consisting of wearable mask sample collectors (masklect) and voiceprints and based on the STRONG (Spatiotemporal Reporting Over Network and GPS) strategy, could enable the resumption of social activities and economic recovery and ensure a safe public health environment sustainably.

Evaluation of drug sensitivity, immunological characteristics, and prognosis in melanoma patients using an endoplasmic reticulum stress-associated signature based on bioinformatics and pan-cancer analysis.

We aimed to develop endoplasmic reticulum (ER) stress-related risk signature to predict the prognosis of melanoma and elucidate the immune characteristics and benefit of immunotherapy in ER-related risk score-defined subgroups of melanoma based on a machine learning algorithm. Based on The Cancer Genome Atlas (TCGA) melanoma dataset (n = 471) and GTEx database (n = 813), 365 differentially expressed ER-associated genes were selected using the univariate Cox model and LASSO penalty Cox model. Ten genes impacting OS were identified to construct an ER-related signature by using the multivariate Cox regression method and validated with the Gene Expression Omnibus (GEO) dataset. Thereafter, the immune features, CNV, methylation, drug sensitivity, and the clinical benefit of anticancer immune checkpoint inhibitor (ICI) therapy in risk score subgroups, were analyzed. We further validated the gene signature using pan-cancer analysis by comparing it to other tumor types. The ER-related risk score was constructed based on the ARNTL, AGO1, TXN, SORL1, CHD7, EGFR, KIT, HLA-DRB1 KCNA2, and EDNRB genes. The high ER stress-related risk score group patients had a poorer overall survival (OS) than the low-risk score group patients, consistent with the results in the GEO cohort. The combined results suggested that a high ER stress-related risk score was associated with cell adhesion, gamma phagocytosis, cation transport, cell surface cell adhesion, KRAS signalling, CD4 T cells, M1 macrophages, naive B cells, natural killer (NK) cells, and eosinophils and less benefitted from ICI therapy. Based on the expression patterns of ER stress-related genes, we created an appropriate predictive model, which can also help distinguish the immune characteristics, CNV, methylation, and the clinical benefit of ICI therapy. KEY MESSAGES: Melanoma is the cutaneous tumor with a high degree of malignancy, the highest fatality rate, and extremely poor prognosis. Model usefulness should be considered when using models that contained more features. We constructed the Endoplasmic Reticulum stress-associated signature using TCGA and GEO database based on machine learning algorithm. ER stress-associated signature has excellent ability for predicting prognosis for melanoma.

Preoperative ultrasound can accurately predict the diameter of double-stranded peroneus longus tendon autografts.

PURPOSE: This study aimed to determine the correlation between the intraoperative diameter of double-stranded peroneus longus tendon (2PLT) and length of the PLT autograft and preoperative ultrasound (US) measurements, as well as radiographic and anthropometric measurements. The hypothesis was that US can accurately predict the diameter of 2PLT autografts during operation. METHODS: Twenty-six patients underwent ligament reconstruction with 2PLT autografts were included. Preoperative US was used to calculate the in situ PLT cross-sectional area (CSA) at seven levels (0, 1, 2, 3, 4, 5, 10 cm proximal to the harvest start point). Femoral width, notch width, notch height, maximum patellar length, and patellar tendon length were determined on preoperative radiographs. Intraoperative measurements of PLT were made, including all fiber lengths of PLT and diameters of 2PLT using sizing tubes calibrated to 0.5 mm. RESULTS: CSA at 1 cm proximal to the harvest site had the highest correlation with the diameter of 2PLT (r = 0.84, P < 0.001). Calf length had the highest correlation with PLT length (r = 0.65, P < 0.001). The diameter of the 2PLT autografts could be predicted by the following formula: 4.6 + 0.2 × [sonographic CSA of PLT at 1 cm level]; the length of PLT could be predicted by the following formula: 5.6 + 0.5 × Calf length. CONCLUSION: The diameter of 2PLT and length of PLT autografts can be accurately predicted by preoperative US and calf length measurements, respectively. Accurate preoperative prediction of the diameter and length of autologous grafts can provide the most suitable and individualized graft for patients. LEVEL OF EVIDENCE: IV.

Accumulation of blood chromium and cobalt in the participants with metal objects: findings from the 2015 to 2018 National Health and Nutrition Examination Survey (NHANES).

BACKGROUND: Chromium (Cr) and cobalt (Co) are the essential elements for producing metal implants, but might have potential health issues. The research on the correlation between metal implants and blood Cr and Co on a large population is still limited. METHODS: National Health and Nutrition Examination Survey (NHANES) is a program of studies designed to assess the health status of Americans began in the early 1960s. The study was based on the NHANES database from two data collection years (2015-2016 and 2017-2018). The exposure variable of this study was whether the participants had metal objects in the body or not. The outcome variables were blood concentrations of Cr and Co. Age, body mass index, sex, race/ethnicity, income to poverty ratio, tap water behavior, shellfish/fish/tuna/salmon eating habits, level of education, smoking behavior, marital status, blood hemoglobin, and data collection years were included as confounding variables. RESULTS: A total of 4412 participants, aged 40 years or older, were included in this analysis, consisting of the without metal objects group (n = 3150) and the metal objects group (n = 1262). Metal objects was positively correlated to the accumulation of blood Cr (ß = 0.072, 95% CI: 0.043-0.102, p < 0.001) and blood Co (ß = 0.079, 95% CI: 0.049-0.109, p < 0.001). However, the positive correlation of metal objects with blood Cr was only presented in women (ß = 0.112, 95% CI: 0.074-0.151, p < 0.001), but not in men. Meanwhile, the positive relationship between metal objects and blood Cr/Co was not observed in the Asian subgroup. CONCLUSIONS: Blood Cr and Co concentrations were statistically higher in people with metal objects, but with race and sex differences. LEVEL OF EVIDENCE: Level IV, cross-sectional study.

A cell phone app for facial acne severity assessment.

Acne vulgaris, the most common skin disease, can cause substantial economic and psychological impacts to the people it affects, and its accurate grading plays a crucial role in the treatment of patients. In this paper, we firstly proposed an acne grading criterion that considers lesion classifications and a metric for producing accurate severity ratings. Due to similar appearance of acne lesions with comparable severities and difficult-to-count lesions, severity assessment is a challenging task. We cropped facial skin images of several lesion patches and then addressed the acne lesion with a lightweight acne regular network (Acne-RegNet). Acne-RegNet was built by using a median filter and histogram equalization to improve image quality, a channel attention mechanism to boost the representational power of network, a region-based focal loss to handle classification imbalances and a model pruning and feature-based knowledge distillation to reduce model size. After the application of Acne-RegNet, the severity score is calculated, and the acne grading is further optimized by the metadata of the patients. The entire acne assessment procedure was deployed to a mobile device, and a phone app was designed. Compared with state-of-the-art lightweight models, the proposed Acne-RegNet significantly improves the accuracy of lesion classifications. The acne app demonstrated promising results in severity assessments (accuracy: 94.56%) and showed a dermatologist-level diagnosis on the internal clinical dataset.The proposed acne app could be a useful adjunct to assess acne severity in clinical practice and it enables anyone with a smartphone to immediately assess acne, anywhere and anytime.

Osteoarthritis is positively associated with self-reported sleep trouble in older adults.

BACKGROUND: Osteoarthritis (OA) is a leading cause of disability in older adults. Most research has focused on minimizing pain and maximizing physical function so as to maintain patient mobility preceding joint arthroplasty. However, few studies have formally studied the relationship between OA and sleep trouble, although it is clinically recognized that OA may affect sleep. METHODS: The study was based on the National Health and Nutrition Examination Survey (NHANES) database from 2011-2018. Participants were defined as adults aged 60 years or older with diagnoses of OA and self-reported sleep trouble. Multivariable regression analyses were applied to assess the association between OA and sleep trouble, adjusting for age, sex, body mass index, race/ethnicity, education level, marital status, income, depression level, etc. RESULTS: This study included 4154 participants, consisting of the control group (n = 2966) and the OA group (n = 1188). OA individuals were 2.11 (95% CI 1.79-2.47, p < 0.001) times more likely to have sleep trouble compared with those without OA. On subgroup analyses, there was lower odds ratio value of sleep trouble in men compared with women, and in the highest income group compared with the other income groups. CONCLUSIONS: OA was positively associated with sleep trouble in older adults, with different odds ratio values among different subgroups. Our results suggest that older adults with OA should be aggressively screened for sleep problems.