Search

Objective The cortical bone trajectory (CBT) technology is an effective substitute for traditional pedicle screw (PS) technology. However, there is still controversy about the CBT screw technology placement strategy. The objective of this study was to simulate cortical screw placement with the help of three‐dimensional (3D) software, to discuss the differences in screws between genders and vertebral segments, and to explore a safer and more efficient strategy for cortical screw placement. Methods Mimics Medical software was used to construct a 3D model of the lumbar spine, and the placement of CBT screws was simulated. The volume of each vertebral body from L1 to L5, the pedicle isthmus height (IH), the pedicle isthmus width (IW), and the sagittal vertebral distance (SAVD) were measured. The transverse distance (TD) and the longitudinal distance (LD) between the ideal starting point (SP) and the clinical SP (the intersection Q of the midline of the superior articular process and the horizontal line 1 mm below the transverse process) were measured. The cephalad angle (CA), lateral angle (LA), maximum screw diameter (MSD), maximum screw length (MSL) of each trajectory of the L1 to L5 vertebral bodies, and the percentage of the screw insertion depth (PSID) into the vertebral body were measured. Data were statistically analyzed using Student's t‐test, one‐way analysis of variance (ANOVA), and Tukey's test. Results Vertebral anatomical parameters and CBT screw parameters differed between males and females. Female patients had lower IH, IW, SAVD, CA, LA, MSD, and MSL than males. IH was greatest in L1 (male, 17.81 mm; female, 16.12 mm) and the smallest in L5 (male, 14.11 mm; female, 13.05 mm). IW was smallest in L1 (male, 8.89 mm; female, 7.37 mm) and greatest in L5 (male, 16.59 mm; female, 15.43 mm). The MSD of males was smallest in L1 (6.05 mm) and greatest in L3 (7.06 mm); the MSD of females was smallest in L1 (5.13 mm) and greatest in L4 (6.64 mm). MSL was greatest at L3 (male, 33.63 mm; female, 32.28 mm) and smallest at L5 (male, 31.25 mm; female, 29.97 mm). CA was smallest in L1 (male, 22.80°; female, 21.92°) and greatest in L3 (male, 25.29°; female, 24.33°). LA was smallest in L1 (male 12.37°, female 11.27°) and greatest in L5 (male 13.56°, female 12.96°). Among the males, TD was smallest at L1 (−0.51 mm) and greatest at L5 (1.37 mm), while LD was greatest at L2 (3.46 mm) and smallest at L5 (2.40 mm). In females, TD was greatest at L1 (0.12 mm) and smallest at L3 (−0.51 mm), while LD was greatest at L1 (3.69 mm) and smallest at L5 (2.08 mm). In the overall sample, the incidence of SAVD and PSID gradually increased from L1 to L5. Conclusion The optimal screw placement strategy for CBT screws varies significantly according to sex and vertebral body segments, particularly noting the specificity of screw placement at L5. The optimal screw placement strategy should be selected based on the patient's sex and segment characteristics before surgery to maximize the safety and accuracy of CBT screw placement.

Learning trajectory describes the student’s learning progress or steps in one area through which teaching and learning can be linked. The cognitive diagnostic model (CDM) is an emerging evaluation theory in education measures. Researchers can determine students’ mastery of fine-grained knowledge points by describing the learning trajectory based on CDM. The present study is based on the Trends in International Mathematics and Science Study (TIMSS) 2019, particularly, 21 mathematical items in the fourth grade. It analyzes the response data of 2,854 students from 17 countries from 10 attributes based on CDM. This study explores students’ different learning trajectories in the content field by analyzing the relationships between knowledge states and attribute patterns. The study found that the 17 countries differ in learning trajectories but have commonalities. The learning starting points of 17 countries can be roughly divided into two categories and geometry attribute and fractions and decimals attribute are the last two attributes to master.

ObjectiveTo assess the difference between the posterior condylar angle (PCA) and the mechanical lateral distal femoral angle (mLDFA) in the osseous and cartilaginous contours in a non-arthritic Chinese population.MethodsComputed tomography (CT) and magnetic resonance imaging (MRI) were obtained from 83 patients with knee injuries before arthroscopy, and femur and distal femoral cartilage three-dimensional (3D) models were constructed. The 3D cartilage model was arranged to share physical space with the 3D femoral model, and then PCA and mLDFA were measured on the osseous and cartilaginous contours, respectively. The differences between the measurements with and without cartilage were evaluated.ResultsThe average PCA with cartilage was 2.88 ± 1.35° and without was 2.73 ± 1.34°. The difference was significant in all patients and females but not in males. The average mLDFA with cartilage was 84.73 ± 2.15° and without cartilage was 84.83 ± 2.26°, but the difference was statistically insignificant in all groups.ConclusionPCA on the osseous and cartilaginous contours significantly differed with and without cartilage in the female group, suggesting that cartilage thickness should be considered during preoperative femoral rotational resection planning.

The cognitive diagnosis model is an emerging evaluation theory. The mastery of fine-grained knowledge points of students can be obtained via the cognitive diagnostic model (CDM), which can subsequently describe the learning trajectory. The latter is a description of the learning progress of students in a specific area, through which teaching and learning can be linked. This research is based on nine statistical items in the Program for International Student Assessment (PISA) 2012 and an analysis of the response data of 30,092 students from 14 countries from four attributes based on CDM. Then, it obtains the learning trajectory of students in statistical knowledge. The study found that Bulgaria, Costa Rica, Peru, Mexico, and Serbia have the same learning trajectories. The learning trajectories of almost 14 countries are as follows: (1) uncertainty, (2) data handling, (3) statistical chart, and (4) average.

BackgroundWhile in vitro wear simulation of unicompartmental knee arthroplasty (UKA) showed outstanding long-term wear performance, studies reported that polyethylene (PE) wear was responsible for 12% fixed-bearing (FB) UKA failure. This paper aimed to quantify the in vivo 6-degrees-of-freedom (6-DOF) knee kinematics and contact positions of FB UKA during daily activities and compare with the previous results of in vitro wear simulator.MethodsFourteen patients following unilateral medial FB UKA received a CT scan and dual fluoroscopic imaging during level walking, single-leg deep lunge, and sit-to-stand motion for evaluating in vivo 6-DOF FB UKA kinematics. The closest point between surface models of the femoral condyle and PE insert was determined to locate the medial compartmental articular contact positions, which were normalized relative to the PE insert length. The in vivo contact area was compared with the in vitro wear region in previous simulator studies.ResultsThe in vivo contact positions during daily activities were more anterior than those in the previous in vitro wear simulator studies (p < 0.001). Significant differences in the femoral anteroposterior translation and tibial internal rotation during the stance phase were observed and compared with those in lunge and sit-to-stand motions (p < 0.05). The in vivo contact position located anteriorly and medially by 5.2 ± 2.7 and 1.8 ± 1.6 mm on average for the stance phase, 1.0 ± 2.4 and 0.9 ± 1.5 mm for the lunge, and 2.1 ± 3.3 and 1.4 ± 1.4 mm for sit-to-stand motion. The in vivo contact position was in the more anterior part during the stance phase (p < 0.05).ConclusionThe current study revealed that the contact position of FB UKA was located anteriorly and medially on the PE insert during in vivo weight-bearing activities and different from previous findings of the in vitro wear simulator. We should take in vivo 6-DOF knee kinematics and contact patterns of FB UKA into account to reproduce realistic wear performance for in vitro wear simulator and to improve implant design.

In this paper, a novel human respiration detection method based on angular velocity is proposed for continuous acquisition and analysis of respiratory signals. Using patient-specific information derived from a wearable sensor, our proposed system is capable of monitoring human respiration and assists in identifying potential respiratory disorders by relying on pre-defined simple procedures. Specifically, our signal verification platform is equipped with a carbon dioxide concentration detection device in order to acquire a synchronous signal. In addition, a median filter method is used to extract the respiratory waveform from the original signal. The obtained angular velocity waveform pertaining to human respiration is then compared to the respiratory carbon dioxide concentration waveform, and the validity of our designated parameters is verified. The test results demonstrate that our implemented system is appropriate for unobtrusive respiratory signals acquisition and analysis; therefore, it can be considered a potential alternative for physiological monitoring and respiratory disorders screening.

Item response models often cannot calculate true individual response probabilities because of the existence of response disturbances (such as guessing and cheating). Many studies on aberrant responses under item response theory (IRT) framework had been conducted. Some of them focused on how to reduce the effect of aberrant responses, and others focused on how to detect aberrant examinees, such as person fit analysis. The purpose of this research was to derive a generalized formula of bias with/without aberrant responses, that showed the effect of both non-aberrant and aberrant response data on the bias of capability estimation mathematically. A new evaluation criterion, named aberrant absolute bias (|ABIAS|), was proposed to detect aberrant examinees. Simulation studies and application to a real dataset were conducted to demonstrate the efficiency and the utility of |ABIAS|.

Abstract Melanoma is a highly malignant form of skin cancer that typically originates from abnormal melanocytes. Despite significant advances in treating metastatic melanoma with immune checkpoint blockade (ICB) therapy, a substantial number of patients do not respond to this treatment and face risks of recurrence and metastasis. This study collected data from multiple datasets, including cohorts from Riaz et al., Gide et al., MGH, and Abril-Rodriguez et al., focusing on on-treatment samples during ICB therapy. We used the single-sample gene set enrichment analysis (ssGSEA) method to calculate immunogenic cell death scores (ICDS) and employed an elastic network algorithm to construct a model predicting ICB efficacy. By analyzing 18 ICD gene signatures, we identified 9 key ICD gene signatures that effectively predict ICB treatment response for on-treatment metastatic melanoma specimens. Results showed that patients with high ICD scores had significantly higher response rates to ICB therapy compared to those with low ICD scores. ROC analysis demonstrated that the AUC values for both the training and validation sets were around 0.8, indicating good predictive performance. Additionally, survival analysis revealed that patients with high ICD scores had longer progression-free survival (PFS). This study used an elastic network algorithm to identify 9 ICD gene signatures related to the immune response in metastatic melanoma. These gene features can not only predict the efficacy of ICB therapy but also provide references for clinical decision-making. The results indicate that ICD plays an important role in metastatic melanoma immunotherapy and that expressing ICD signatures can more accurately predict ICB treatment response and prognosis for on-treatment metastatic melanoma specimens, thus providing a basis for personalized treatment.

IntroductionUltrasound-guided needle biopsy is a commonly employed technique in modern medicine for obtaining tissue samples, such as those from breast tumors, for pathological analysis. However, it is limited by the low signal-to-noise ratio and the complex background of breast ultrasound imaging. In order to assist physicians in accurately performing needle biopsies on pathological tissues, minimize complications, and avoid damage to surrounding tissues, computer-aided needle segmentation and tracking has garnered increasing attention, with notable progress made in recent years. Nevertheless, challenges remain, including poor ultrasound image quality, high computational resource requirements, and various needle shape.MethodsThis study introduces a novel Spatio-Temporal Memory Network designed for ultrasound-guided breast tumor biopsy. The proposed network integrates a hybrid encoder that employs CNN-Transformer architectures, along with an optical flow estimation method. From the Ultrasound Imaging Department at the First Affiliated Hospital of Shantou University, we developed a real-time segmentation dataset specifically designed for ultrasound-guided needle puncture procedures in breast tumors, which includes ultrasound biopsy video data collected from 11 patients.ResultsExperimental results demonstrate that this model significantly outperforms existing methods in improving the positioning accuracy of needle and enhancing the tracking stability. Specifically, the performance metrics of the proposed model is as follows: IoU is 0.731, Dice is 0.817, Precision is 0.863, Recall is 0.803, and F1 score is 0.832. By advancing the precision of needle localization, this model contributes to enhanced reliability in ultrasound-guided breast tumor biopsy, ultimately supporting safer and more effective clinical outcomes.DiscussionThe model proposed in this paper demonstrates robust performance in the computer-aided tracking and segmentation of biopsy needles in ultrasound imaging, specifically for ultrasound-guided breast tumor biopsy, offering dependable technical support for clinical procedures.

While breast cancer treatments have advanced significantly nowadays, yet metastatic, especially triple-negative breast cancer (TNBC), remains challenging with low survival. Cancer immunotherapy, a promising approach for HER2-positive and TNBC, still faces resistance hurdles. Recently, numerous studies have set their sights on the resistance of immunotherapy for breast cancer. Our study provides a thorough comprehension of the current research landscape, hotspots, and emerging breakthroughs in this critical area through a meticulous bibliometric analysis. As of March 26, 2024, a total of 1341 articles on immunology resistance in breast cancer have been gathered from Web of Science Core Collection, including 765 articles and 576 reviews. Bibliometrix, CiteSpace and VOSviewer software were utilized to examine publications and citations per year, prolific countries, contributive institutions, high-level journals and scholars, as well as highly cited articles, references and keywords. The research of immunotherapy resistance in breast cancer has witnessed a remarkable surge over the past seven years. The United States and China have made significant contributions, with Harvard Medical School being the most prolific institution and actively engaging in collaborations. The most contributive author is Curigliano, G from the European Institute of Oncology in Italy, while Wucherpfennig, K. W. from the Dana-Farber Cancer Institute in the USA, had the highest citations. Journals highly productive primarily focus on clinical, immunology and oncology research. Common keywords include “resistance”, “expression”, “tumor microenvironment”, “cancer”, “T cell”, “therapy”, “chemotherapy” and “cell”. Current research endeavors to unravel the mechanisms of immune resistance in breast cancer through the integration of bioinformatics, basic experiments, and clinical trials. Efforts are underway to develop strategies that improve the effectiveness of immunotherapy, including the exploration of combination therapies and advancements in drug delivery systems. Additionally, there is a strong focus on identifying novel biomarkers that can predict patient response to immunology. This study will provide researchers with an up-to-date overview of the present knowledge in drug resistance of immunology for breast cancer, serving as a valuable resource for informed decision-making and further research on innovative approaches to address immunotherapy resistance.

Abstract The retinal impulse signal is the basic carrier of visual information. It records the distribution of light on the retina. However, its direct conversion to a scene image is difficult due to the nonlinear characteristics of its distribution. Therefore, the use of artificial neural network to reconstruct the scene from retinal spikes has become an important research area. This paper proposes the architecture of a neural network based on vector quantization, where the feature vectors of spike trains are extracted, compressed, and stored using a feature extraction and compression network. During the decoding process, the nearest neighbour search method is used to find the nearest feature vector corresponding to each feature vector in the feature map. Finally, a reconstruction network is used to decode a new feature map composed of matching feature vectors to obtain a visual scene. This paper also verifies the impact of vector quantization on the characteristics of pulse signals by comparing experiments and visualizing the characteristics before and after vector quantization. The network delivers promising performance when evaluated on different datasets, demonstrating that this research is of great significance for improving relevant applications in the fields of retinal image processing and artificial intelligence.

Introduction: Early predictive pathological complete response (pCR) is beneficial for optimizing neoadjuvant chemotherapy (NAC) strategies for breast cancer. The hematoxylin and eosin (HE)-stained slices of biopsy tissues contain a large amount of information on tumor epithelial cells and stromal. The fusion of pathological image features and clinicopathological features is expected to build a model to predict pCR of NAC in breast cancer.Methods: We retrospectively collected a total of 440 breast cancer patients from three hospitals who underwent NAC. HE-stained slices of biopsy tissues were scanned to form whole-slide images (WSIs), and pathological images of representative regions of interest (ROI) of each WSI were selected at different magnifications. Based on several different deep learning models, we propose a novel feature extraction method on pathological images with different magnifications. Further, fused with clinicopathological features, a multimodal breast cancer NAC pCR prediction model based on a support vector machine (SVM) classifier was developed and validated with two additional validation cohorts (VCs).Results: Through experimental validation of several different deep learning models, we found that the breast cancer pCR prediction model based on the SVM classifier, which uses the VGG16 model for feature extraction of pathological images at ×20 magnification, has the best prediction efficacy. The area under the curve (AUC) of deep learning pathological model (DPM) were 0.79, 0.73, and 0.71 for TC, VC1, and VC2, respectively, all of which exceeded 0.70. The AUCs of clinical model (CM), a clinical prediction model established by using clinicopathological features, were 0.79 for TC, 0.73 for VC1, and 0.71 for VC2, respectively. The multimodal deep learning clinicopathological model (DPCM) established by fusing pathological images and clinicopathological features improved the AUC of TC from 0.79 to 0.84. The AUC of VC2 improved from 0.71 to 0.78.Conclusion: Our study reveals that pathological images of HE-stained slices of pre-NAC biopsy tissues can be used to build a pCR prediction model. Combining pathological images and clinicopathological features can further enhance the predictive efficacy of the model.

Convolutional neural networks are given extensive attention towards noise removal due to their good performance over traditional denoising algorithms. With shallow conventional neural networks, the feature extraction ability is not profound. While employing deeper networks, network performance improves with the cost of additional computational requirements. In this paper, we propose an attention-guided twofold denoising network to remove the noise present in the image. The proposed network incorporates dilation convolution to enlarge the receptive fields and improves the feature extraction ability. Also, the presence of attention mechanism strengthens the extracted features and restores the image details during the noise removal. To demonstrate the superiority of the twofold structure, the proposed network is compared with the state-of-the-art denoising models. The experimental results prove that the proposed deep network achieves good peak signal to noise ratio and structural similarity index for different noise levels.

IntroductionAI-assisted ultrasound diagnosis is considered a fast and accurate new method that can reduce the subjective and experience-dependent nature of handheld ultrasound. In order to meet clinical diagnostic needs better, we first proposed a breast lesions AI classification model based on ultrasound dynamic videos and ACR BI-RADS characteristics (hereafter, Auto BI-RADS). In this study, we prospectively verify its performance.MethodsIn this study, the model development was based on retrospective data including 480 ultrasound dynamic videos equivalent to 18122 static images of pathologically proven breast lesions from 420 patients. A total of 292 breast lesions ultrasound dynamic videos from the internal and external hospital were prospectively tested by Auto BI-RADS. The performance of Auto BI-RADS was compared with both experienced and junior radiologists using the DeLong method, Kappa test, and McNemar test.ResultsThe Auto BI-RADS achieved an accuracy, sensitivity, and specificity of 0.87, 0.93, and 0.81, respectively. The consistency of the BI-RADS category between Auto BI-RADS and the experienced group (Kappa:0.82) was higher than that of the juniors (Kappa:0.60). The consistency rates between Auto BI-RADS and the experienced group were higher than those between Auto BI-RADS and the junior group for shape (93% vs. 80%; P = .01), orientation (90% vs. 84%; P = .02), margin (84% vs. 71%; P = .01), echo pattern (69% vs. 56%; P = .001) and posterior features (76% vs. 71%; P = .0046), While the difference of calcification was not significantly different.DiscussionIn this study, we aimed to prospectively verify a novel AI tool based on ultrasound dynamic videos and ACR BI-RADS characteristics. The prospective assessment suggested that the AI tool not only meets the clinical needs better but also reaches the diagnostic efficiency of experienced radiologists.

BackgroundStrain analysis provides more thorough spatiotemporal signatures for myocardial contraction, which is helpful for early detection of cardiac insufficiency. The use of deep learning (DL) to automatically measure myocardial strain from echocardiogram videos has garnered recent attention. However, the development of key techniques including segmentation and motion estimation remains a challenge. In this work, we developed a novel DL-based framework for myocardial segmentation and motion estimation to generate strain measures from echocardiogram videos.MethodsThree-dimensional (3D) Convolutional Neural Network (CNN) was developed for myocardial segmentation and optical flow network for motion estimation. The segmentation network was used to define the region of interest (ROI), and the optical flow network was used to estimate the pixel motion in the ROI. We performed a model architecture search to identify the optimal base architecture for motion estimation. The final workflow design and associated hyperparameters are the result of a careful implementation. In addition, we compared the DL model with a traditional speck tracking algorithm on an independent, external clinical data. Each video was double-blind measured by an ultrasound expert and a DL expert using speck tracking echocardiography (STE) and DL method, respectively.ResultsThe DL method successfully performed automatic segmentation, motion estimation, and global longitudinal strain (GLS) measurements in all examinations. The 3D segmentation has better spatio-temporal smoothness, average dice correlation reaches 0.82, and the effect of target frame is better than that of previous 2D networks. The best motion estimation network achieved an average end-point error of 0.05 ± 0.03 mm per frame, better than previously reported state-of-the-art. The DL method showed no significant difference relative to the traditional method in GLS measurement, Spearman correlation of 0.90 (p < 0.001) and mean bias −1.2 ± 1.5%.ConclusionIn conclusion, our method exhibits better segmentation and motion estimation performance and demonstrates the feasibility of DL method for automatic strain analysis. The DL approach helps reduce time consumption and human effort, which holds great promise for translational research and precision medicine efforts.

Product reviews provide crucial information for both consumers and businesses, offering insights needed before purchasing a product or service. However, existing sentiment analysis methods, especially for Chinese language, struggle to effectively capture contextual information due to the complex semantics, multiple sentiment polarities, and long-term dependencies between words. In this paper, we propose a sentiment classification method based on the BiLSTM algorithm to address these challenges in natural language processing. Self-Attention-CNN BiLSTM (SAC-BiLSTM) leverages dual channels to extract features from both character-level embeddings and word-level embeddings. It combines BiLSTM and Self-Attention mechanisms for feature extraction and weight allocation, aiming to overcome the limitations in mining contextual information. Experiments were conducted on the onlineshopping10cats dataset, which is a standard corpus of e-commerce shopping reviews available in the ChineseNlpCorpus 2018. The experimental results demonstrate the effectiveness of our proposed algorithm, with Recall, Precision, and F1 scores reaching 0.9409, 0.9369, and 0.9404, respectively.