Trapping of deformable active particles by a periodic background potential.

The dynamic behaviors, specifically trapping and sorting, of active particles interacting with periodic substrates have garnered significant attention. This study investigates numerically the trapping of soft, deformable particles on a periodic potential substrate, which can be experimentally verified through optical tweezers. The research demonstrates that multiple factors, including the relative size of traps, self-propelled velocity, shape parameters, ratio of particles to traps, and translational diffusion, can influence the trapping effect. Within certain parameter boundaries, it is shown that all particles can be consistently trapped. The research reveals that stable trapping typically occurs at median values of the relative trap size. An increase in the self-propelled velocity, the shape parameter, and the translational diffusion coefficient tends to facilitate the escapement of the particles from the traps. It is noteworthy that particles with larger shape parameters can escape even when the restoring force exceeds the self-propelled force. In addition, as the ratio of particles to traps grows, the fraction of trapped particles steadily reduces. Notably, rigid particles are consistently divided and trapped by traps closely approximating an integer multiple of the particles' area, up until the ratio reaches the aforesaid integer value. These findings can potentially enhance the understanding of the interactive effects between active deformable particles and periodic substrates. Moreover, this work suggests a different experimental approach to sort active particles based on rigidity disparities.

Collective self-optimization of binary mixed heterogeneous populations.

To maximize the survival chances of society members, collective self-organization must balance individual interests with promoting the collective welfare. Although situations where group members have equal optimal values are clear, how varying optimal values impacts group dynamics remains unclear. To address this gap, we conducted a self-optimization study of a binary system incorporating communication-enabled active particles with distinct optimal values. We demonstrate that similar particles will spontaneously aggregate and separate from each other to maximize their individual benefits during the process of self-optimization. Our research shows that both types of particles can produce the optimal field values at low density. However, only one type of particle can achieve the optimal field values at medium density. At high densities, neither type of particle is effective in reaching the optimal field values. Interestingly, we observed that during the self-optimization process, the mixture demixed spontaneously under certain circumstances of mixed particles. Particles with higher optimal values developed into larger clusters, while particles with lower optimal values migrated outside of these clusters, resulting in the separation of the mixture. To achieve this separation, suitable noise intensity, particle density, and the significant difference in optimal values were necessary. Our results provide a more profound comprehension of the self-optimization of synthetic or biological agents' communication and provide valuable insight into separating binary species and mixtures.

Artificial intelligence aided precise detection of local recurrence on MRI for nasopharyngeal carcinoma: a multicenter cohort study.

Background: MRI is the routine examination to surveil the recurrence of nasopharyngeal carcinoma, but it has relatively lower sensitivity than PET/CT. We aimed to find if artificial intelligence (AI) could be competent pre-inspector for MRI radiologists and whether AI-aided MRI could perform better or even equal to PET/CT. Methods: This multicenter study enrolled 6916 patients from five hospitals between September 2009 and October 2020. A 2.5D convolutional neural network diagnostic model and a nnU-Net contouring model were developed in the training and test cohorts and used to independently predict and visualize the recurrence of patients in the internal and external validation cohorts. We evaluated the area under the ROC curve (AUC) of AI and compared AI with MRI and PET/CT in sensitivity and specificity using the McNemar test. The prospective cohort was randomized into the AI and non-AI groups, and their sensitivity and specificity were compared using the Chi-square test. Findings: The AI model achieved AUCs of 0.92 and 0.88 in the internal and external validation cohorts, corresponding to the sensitivity of 79.5% and 74.3% and specificity of 91.0% and 92.8%. It had comparable sensitivity to MRI (e.g., 74.3% vs. 74.7%, P = 0.89) but lower sensitivity than PET/CT (77.9% vs. 92.0%, P < 0.0001) at the same individual-specificities. The AI model achieved moderate precision with a median dice similarity coefficient of 0.67. AI-aided MRI improved specificity (92.5% vs. 85.0%, P = 0.034), equaled PET/CT in the internal validation subcohort, and increased sensitivity (81.9% vs. 70.8%, P = 0.021) in the external validation subcohort. In the prospective cohort of 1248 patients, the AI group had higher sensitivity than the non-AI group (78.6% vs. 67.3%, P = 0.23), albeit nonsignificant. In future randomized controlled trials, a sample size of 3943 patients in each arm would be required to demonstrate the statistically significant difference. Interpretation: The AI model equaled MRI by expert radiologists, and AI-aided MRI by expert radiologists equaled PET/CT. A larger randomized controlled trial is warranted to demonstrate the AI's benefit sufficiently. Funding: The Sun Yat-sen University Clinical Research 5010 Program (2015020), Guangdong Basic and Applied Basic Research Foundation (2022A1515110356), and Guangzhou Science and Technology Program (2023A04J1788).

Parameterized Gompertz-Guided Morphological AutoEncoder for Predicting Pulmonary Nodule Growth.

The growth rate of pulmonary nodules is a critical clue to the cancerous diagnosis. It is essential to monitor their dynamic progressions during pulmonary nodule management. To facilitate the prosperity of research on nodule growth prediction, we organized and published a temporal dataset called NLSTt with consecutive computed tomography (CT) scans. Based on the self-built dataset, we develop a visual learner to predict the growth for the following CT scan qualitatively and further propose a model to predict the growth rate of pulmonary nodules quantitatively, so that better diagnosis can be achieved with the help of our predicted results. To this end, in this work, we propose a parameterized Gempertz-guided morphological autoencoder (GM-AE) to generate any future-time-span high-quality visual appearances of pulmonary nodules from the baseline CT scan. Specifically, we parameterize a popular mathematical model for tumor growth kinetics, Gompertz, to predict future masses and volumes of pulmonary nodules. Then, we exploit the expected growth rate on the mass and volume to guide decoders generating future shape and texture of pulmonary nodules. We introduce two branches in an autoencoder to encourage shape-aware and textural-aware representation learning and integrate the generated shape into the textural-aware branch to simulate the future morphology of pulmonary nodules. We conduct extensive experiments on the self-built NLSTt dataset to demonstrate the superiority of our GM-AE to its competitive counterparts. Experiment results also reveal the learnable Gompertz function enjoys promising descriptive power in accounting for inter-subject variability of the growth rate for pulmonary nodules. Besides, we evaluate our GM-AE model on an in-house dataset to validate its generalizability and practicality. We make its code publicly available along with the published NLSTt dataset.

Solid-liquid transition induced by rigidity disparity in a binary mixture of cell tissues.

The two-dimensional melting of a binary mixture of cell tissues is numerically investigated in the presence of rigidity disparity. We present the full melting phase diagrams of the system by using the Voronoi-based cellular model. It is found that the enhancement of rigidity disparity can induce a solid-liquid transition at both zero temperature and finite temperature. (i) In the case of zero temperature, the system undergoes a continuous solid-hexatic transition followed by a continuous hexatic-liquid transition for zero rigidity disparity, but a discontinuous hexatic-liquid transition for finite rigidity disparity. Remarkably, the solid-hexatic transitions always arise when the soft cells reach the rigidity transition point of monodisperse systems. (ii) In the case of finite temperature, the melting occurs via a continuous solid-hexatic transition followed by a discontinuous hexatic-liquid transition. Our study may contribute to the understanding of solid-liquid transitions in binary mixture systems with rigidity disparity.

Mortality of myasthenia gravis: a national population-based study in China.

OBJECTIVE: As a potentially life-threatening condition, myasthenia gravis (MG) has limited epidemiological studies on mortality. We aim to provide demographic distribution, geographical variation, and temporal trend of MG-related mortality in China. METHODS: The national population-based analysis was conducted based on records derived from the National Mortality Surveillance System of China. All deaths related to MG were identified from 2013 to 2020, and MG-related mortality was evaluated by sex, age, location, and year. RESULTS: A total of 4224 deaths were related to MG during 2013-2020, and the median age at death of MG was 59.45 years, significantly lower than that in the general population (75.47 years, P < 0.05). In 2020, the age-standardized mortality rate of MG was 1.86 per million people and markedly higher in males than in females (2.37 vs. 1.31 per million). The mortality rate per million was lower than 1 in young children, peaking at 2.83 only in males (vs. 0.36 in females) aged 10-19 years, and substantially increased with age, reaching the highest rate of 13.31 for males and 10.58 for females aged 80 years and older. Geographical disparity across China was observed with the highest age-standardized mortality rate in Southwest (2.53 per million). From 2013 to 2020, MG-related mortality rate showed an increasing trend with the average annual percentage change of 3.5% (95% CI, 1.4-5.6). The notable increases occurred in age 10-19 years and over 70 years. INTERPRETATION: In China, MG-related mortality was notably high among adolescent males and the elderly. The increasing death burden due to MG highlight challenges to disease management.

Deep learning-based precise prediction and early detection of radiation-induced temporal lobe injury for nasopharyngeal carcinoma.

Background: Radiotherapy is the mainstay of treatment for nasopharyngeal carcinoma. Radiation-induced temporal lobe injury (TLI) can regress or resolve in the early phase, but it is irreversible at a later stage. However, no study has proposed a risk-based follow-up schedule for its early detection. Planning evaluation is difficult when dose-volume histogram (DVH) parameters are similar and optimization is terminated. Methods: This multicenter retrospective study included 6065 patients between 2014 and 2018. A 3D ResNet-based deep learning model was developed in training and validation cohorts and independently tested using concordance index in internal and external test cohorts. Accordingly, the patients were stratified into risk groups, and the model-predicted risks were used to develop risk-based follow-up schedules. The schedule was compared with the Radiation Therapy Oncology Group (RTOG) recommendation (every 3 months during the first 2 years and every 6 months in 3-5 years). Additionally, the model was used to evaluate plans with similar DVH parameters. Findings: Our model achieved concordance indexes of 0.831, 0.818, and 0.804, respectively, which outperformed conventional prediction models (all P < 0.001). The temporal lobes in all the cohorts were stratified into three groups with discrepant TLI-free survival. Personalized follow-up schedules developed for each risk group could detect TLI 1.9 months earlier than the RTOG recommendation. According to a higher median predicted 3-year TLI-free survival (99.25% vs. 99.15%, P < 0.001), the model identified a better plan than previous models. Interpretation: The deep learning model predicted TLI more precisely. The model-determined risk-based follow-up schedule detected the TLI earlier. The planning evaluation was refined because the model identified a better plan with a lower risk of TLI. Funding: The Sun Yat-sen University Clinical Research 5010 Program (2015020), Guangdong Basic and Applied Basic Research Foundation (2022A1515110356), Medical Scientific Research Foundation of Guangdong Province (A2022367), and Guangzhou Science and Technology Program (2023A04J1788).

Benefit of [18F] FDG PET/CT in the diagnosis and salvage treatment of recurrent nasopharyngeal carcinoma.

PURPOSE: To compare PET/CT, MRI and ultrasonography in detecting recurrence of nasopharyngeal carcinoma and identify their benefit in staging, contouring and overall survival (OS). METHODS: Cohort A included 1453 patients with or without histopathology-confirmed local recurrence, while cohort B consisted of 316 patients with 606 histopathology-confirmed lymph nodes to compare the sensitivities and specificities of PET/CT, MRI and ultrasonography using McNemar test. Cohorts C and D consisted of 273 patients from cohort A and 267 patients from cohort B, respectively, to compare the distribution of PET/CT-based and MRI-based rT-stage and rN-stage and the accuracy of rN-stage using McNemar test. Cohort E included 30 random patients from cohort A to evaluate the changes in contouring with or without PET/CT by related-samples T test or Wilcoxon rank test. The OS of 61 rT3-4N0M0 patients staged by PET/CT plus MRI (cohort F) and 67 MRI-staged rT3-4N0M0 patients (cohort G) who underwent similar salvage treatment were compared by log-rank test and Cox regression. RESULTS: PET/CT had similar specificity to MRI but higher sensitivity (93.9% vs. 79.3%, P < 0.001) in detecting local recurrence. PET/CT, MRI and ultrasonography had comparable specificities, but PET/CT had greater sensitivity than MRI (90.9% vs. 67.6%, P < 0.001) and similar sensitivity to ultrasonography in diagnosing lymph nodes. According to PET/CT, more patients were staged rT3-4 (82.8% vs. 68.1%, P < 0.001) or rN + (89.9% vs. 69.3%, P < 0.001), and the rN-stage was more accurate (90.6% vs. 73.8%, P < 0.001). Accordingly, the contours of local recurrence were more precise (median Dice similarity coefficient 0.41 vs. 0.62, P < 0.001) when aided by PET/CT plus MRI. Patients staged by PET/CT plus MRI had a higher 3-year OS than patients staged by MRI alone (85.5% vs. 60.4%, P = 0.006; adjusted HR = 0.34, P = 0.005). CONCLUSION: PET/CT more accurately detected and staged recurrence of nasopharyngeal carcinoma and accordingly complemented MRI, providing benefit in contouring and OS.

Multi-scale contextual semantic enhancement network for 3D medical image segmentation.

Objective. Accurate and automatic segmentation of medical images is crucial for improving the efficiency of disease diagnosis and making treatment plans. Although methods based on convolutional neural networks have achieved excellent results in numerous segmentation tasks of medical images, they still suffer from challenges including drastic scale variations of lesions, blurred boundaries of lesions and class imbalance. Our objective is to design a segmentation framework named multi-scale contextual semantic enhancement network (3D MCSE-Net) to address the above problems.Approach. The 3D MCSE-Net mainly consists of a multi-scale context pyramid fusion module (MCPFM), a triple feature adaptive enhancement module (TFAEM), and an asymmetric class correction loss (ACCL) function. Specifically, the MCPFM resolves the problem of unreliable predictions due to variable morphology and drastic scale variations of lesions by capturing the multi-scale global context of feature maps. Subsequently, the TFAEM overcomes the problem of blurred boundaries of lesions caused by the infiltrating growth and complex context of lesions by adaptively recalibrating and enhancing the multi-dimensional feature representation of suspicious regions. Moreover, the ACCL alleviates class imbalances by adjusting asy mmetric correction coefficient and weighting factor.Main results. Our method is evaluated on the nasopharyngeal cancer tumor segmentation (NPCTS) dataset, the public dataset of the MICCAI 2017 liver tumor segmentation (LiTS) challenge and the 3D image reconstruction for comparison of algorithm and DataBase (3Dircadb) dataset to verify its effectiveness and generalizability. The experimental results show the proposed components all have unique strengths and exhibit mutually reinforcing properties. More importantly, the proposed 3D MCSE-Net outperforms previous state-of-the-art methods for tumor segmentation on the NPCTS, LiTS and 3Dircadb dataset.Significance. Our method addresses the effects of drastic scale variations of lesions, blurred boundaries of lesions and class imbalance, and improves tumors segmentation accuracy, which facilitates clinical medical diagnosis and treatment planning.

A new classification network for diagnosing Alzheimer's disease in class-imbalance MRI datasets.

Automatic identification of Alzheimer's Disease (AD) through magnetic resonance imaging (MRI) data can effectively assist to doctors diagnose and treat Alzheimer's. Current methods improve the accuracy of AD recognition, but they are insufficient to address the challenge of small interclass and large intraclass differences. Some studies attempt to embed patch-level structure in neural networks which enhance pathologic details, but the enormous size and time complexity render these methods unfavorable. Furthermore, several self-attention mechanisms fail to provide contextual information to represent discriminative regions, which limits the performance of these classifiers. In addition, the current loss function is adversely affected by outliers of class imbalance and may fall into local optimal values. Therefore, we propose a 3D Residual RepVGG Attention network (ResRepANet) stacked with several lightweight blocks to identify the MRI of brain disease, which can also trade off accuracy and flexibility. Specifically, we propose a Non-local Context Spatial Attention block (NCSA) and embed it in our proposed ResRepANet, which aggregates global contextual information in spatial features to improve semantic relevance in discriminative regions. In addition, in order to reduce the influence of outliers, we propose a Gradient Density Multiple-weighting Mechanism (GDMM) to automatically adjust the weights of each MRI image via a normalizing gradient norm. Experiments are conducted on datasets from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Australian Imaging, Biomarker and Lifestyle Flagship Study of Aging (AIBL). Experiments on both datasets show that the accuracy, sensitivity, specificity, and Area Under the Curve are consistently better than for state-of-the-art methods.

Shift-channel attention and weighted-region loss function for liver and dense tumor segmentation.

PURPOSE: To assist physicians in the diagnosis and treatment planning of tumor, a robust and automatic liver and tumor segmentation method is highly demanded in the clinical practice. Recently, numerous researchers have improved the segmentation accuracy of liver and tumor by introducing multiscale contextual information and attention mechanism. However, this tends to introduce more training parameters and suffer from a heavier computational burden. In addition, the tumor has various sizes, shapes, locations, and numbers, which is the main reason for the poor accuracy of automatic segmentation. Although current loss functions can improve the learning ability of the model for hard samples to a certain extent, these loss functions are difficult to optimize the segmentation effect of small tumor regions when the large tumor regions in the sample are in the majority. METHODS: We propose a Liver and Tumor Segmentation Network (LiTS-Net) framework. First, the Shift-Channel Attention Module (S-CAM) is designed to model the feature interdependencies in adjacent channels and does not require additional training parameters. Second, the Weighted-Region (WR) loss function is proposed to emphasize the weight of small tumors in dense tumor regions and reduce the weight of easily segmented samples. Moreover, the Multiple 3D Inception Encoder Units (MEU) is adopted to capture the multiscale contextual information for better segmentation of liver and tumor. RESULTS: Efficacy of the LiTS-Net is demonstrated through the public dataset of MICCAI 2017 Liver Tumor Segmentation (LiTS) challenge, with Dice per case of 96.9 % ${\bf \%}$ and 75.1 % ${\bf \%}$ , respectively. For the 3D Image Reconstruction for Comparison of Algorithm and DataBase (3Dircadb), Dices are 96.47 % ${\bf \%}$ for the liver and 74.54 % ${\bf \%}$ for tumor segmentation. The proposed LiTS-Net outperforms existing state-of-the-art networks. CONCLUSIONS: We demonstrated the effectiveness of LiTS-Net and its core components for liver and tumor segmentation. The S-CAM is designed to model the feature interdependencies in the adjacent channels, which is characterized by no need to add additional training parameters. Meanwhile, we conduct an in-depth study of the feature shift proportion of adjacent channels to determine the optimal shift proportion. In addition, the WR loss function can implicitly learn the weights among regions without the need to manually specify the weights. In dense tumor segmentation tasks, WR aims to enhance the weights of small tumor regions and alleviate the problem that small tumor segmentation is difficult to optimize further when large tumor regions occupy the majority. Last but not least, our proposed method outperforms other state-of-the-art methods on both the LiTS dataset and the 3Dircadb dataset.

Relationships among inferiority feelings, fear of negative evaluation, and social anxiety in Chinese junior high school students.

Introduction: This study aimed to explore the relationship between feelings of inferiority and social anxiety in Chinese junior high school students. In addition, it examined the potential mediating effect of fear of negative evaluation in this relationship. Methods: A survey was administered to a sample of 734 Chinese junior high school students. The Feelings of Inadequacy Scale, Brief Fear of Negative Evaluation Scale, and Social Avoidance Distress Scale were used. Results: First, there were significant positive correlations between all subscales for the inferiority feelings, social anxiety, and fear of negative evaluation. Furthermore, fear of negative evaluation mediated the predictive effects of four inferiority subscales (i.e., self-esteem, academic ability, appearance, and physical ability) for social anxiety. However, the total score for the sense of inferiority and social confidence subscale lacked this mediating effect. Conclusion: The inferiority feelings of self-esteem, academic ability, appearance, and physical ability may directly and indirectly predict social anxiety through fear of negative evaluation.

Biological roles of l-carnitine in oocyte and early embryo development.

Poor oocyte quality is responsible for female infertility. Multiple studies have been carried out to find supplements to enhance oocyte quality and mitigate infertility problems. l-carnitine and its derivatives have diverse roles in developing oocytes and early embryos. This review focuses on the in vitro and in vivo studies that using l-carnitine alone or in combination with other supplements for oocyte quality enhancement. The key roles of l-carnitine in oocyte quality and embryo growth were summarized, and the underlying mechanism was also elucidated. l-carnitine helps in the lipid metabolism process by controlling the transfer of fatty acids to mitochondria for ß-oxidation. l-carnitine modulates glucose metabolism and enhances respiratory chain enzyme activity. Furthermore, it acts as an antioxidant to prevent oxidative damage and inhibit apoptosis, a signal in response to oxidative stress. Results show the potential of l-carnitine as a potential agent in assisted reproductive technology to improve oocyte quality and the subsequent embryonic development.

Rectification of chiral active particles driven by transversal temperature difference.

Rectification of chiral active particles driven by transversal temperature difference is investigated in a two-dimensional periodic channel. Chiral active particles can be rectified by transversal temperature difference. Transport behaviors are qualitatively different for different wall boundary conditions. For the sliding boundary condition, the direction of transport completely depends on the chirality of particles. The average velocity is a peaked function of angular velocity or temperature difference. The average velocity increases linearly with the self-propulsion speed, while it decreases monotonically with the increase in the packing fraction. For randomized boundary condition, the transport behaviors become complex. When self-propulsion speed is small, in contrast with the sliding boundary condition, particles move in the opposite direction. However, for large self-propulsion speed, current reversals can occur by continuously changing the system parameters (angular velocity, temperature difference, packing fraction, and width of the channel).

[Serving for the longevity and health of people, being the teacher with righteousness and charity: professor XIA Shouren, the acupuncture master in modern times].

Professor XIA Shouren is a famous acupuncture master in China and has devoted his life to clinical practice, teaching and scientific study of TCM. In his 50 years of medical career, he has studied Chinese and western medicine, innovated ancient masters' experiences, adhered to the theory of "less but highly-effective needling", specified at genjie points and deeply explored the specificity of acupoints. Additionally, the acupuncture stfudy has been firstly conducted in the diagnosis and treatment of trigeminal neuralgia in China and his own unique academic thought has been formed. Professor XIA Shouren makes the contribution to the theory, practice and inheritance of acupuncture and moxibustion.

[WU Xiao-ren: an outstanding physician of acupuncture and moxibustion].

WU Xiao-ren devoted his whole life into acupuncture practice and education. During his 50 years clinical practice, teaching and researching, he focused on standardization and application of acupuncture manipulations. Through the integration of western and Chinese medicine as well as technique innovation, he developed new therapies for hypertension, stroke and various pain syndromes with the combination of acupuncture and materia medica and various acupoint prescription. He was against parochial prejudice by advocating absorption of others successful experiences and integration of different schools. Moreover, being conscientious and meticulous, WU Xiao-ren was always strict with his followers. He set up examples for his students with both precept and practice, and made great contribution to the inheritance of both acupuncture theory and practice.

[Autobiography of modern acupuncturist Yu Shu-zhuang].

Professor YU Shu-zhuang is a distinguished acupuncturist in China. He has practiced the TCM acupuncture-moxibustion clinical, educational and scientific research for 60 years in his life. In clinic, he summarized the experiences "five-ming first"; in treatment, he insisted "dredging" and "regulating", protecting the function of spleen and stomach, and needles should be less but specific. In the meanwhile, he made a deep study on the function and clinical effects of specific acupoints, and used the research results of propagated sensation along channel to guide clinical treatment, forming his special academic points. Professor YU has educated a great number of acupuncture-moxibustion talents in China and foreign countries, making great contribution to the popularization of acupuncture-moxibustion in the worldwide.

Factors contributing to de qi in acupuncture randomized clinical trials.

De qi is a core concept of acupuncture and is necessary to produce therapeutic effect. In 2010, de qi has been received as a term in the official extension of the CONSORT Statement. However, there are few articles that discuss which factors have influences on obtaining de qi in clinical trials. This paper aims to explore these factors and give advice on trial design in order to optimize de qi in acupuncture RCTs.