Abstract Cancer is a complex disease composing systemic alterations in multiple scales. In this study, we develop the Tumor Multi-Omics pre-trained Network (TMO-Net) that integrates multi-omics ...pan-cancer datasets for model pre-training, facilitating cross-omics interactions and enabling joint representation learning and incomplete omics inference. This model enhances multi-omics sample representation and empowers various downstream oncology tasks with incomplete multi-omics datasets. By employing interpretable learning, we characterize the contributions of distinct omics features to clinical outcomes. The TMO-Net model serves as a versatile framework for cross-modal multi-omics learning in oncology, paving the way for tumor omics-specific foundation models.
The technology of automatic text generation by machine has always been an important task in natural language processing, but the low-quality text generated by the machine seriously affects the user ...experience due to poor readability and fuzzy effective information. The machine-generated text detection method based on traditional machine learning relies on a large number of artificial features with detection rules. The general method of text classification based on deep learning tends to the orientation of text topics, but logical information between texts sequences is not well utilized. For this problem, we propose an end-to-end model which uses the text sequences self-information to compensate for the information loss in the modeling process, to learn the logical information between the text sequences for machine-generated text detection. This is a text classification task. We experiment on a Chinese question and answer the dataset collected from a biomedical social media, which includes human-written text and machine-generated text. The result shows that our method is effective and exceeds most baseline models.
The objectives of the survey were to identify the level of influenza vaccination coverage in China in three influenza seasons 2009/10 to 2011/12, and to find out potential predictors for seasonal ...influenza vaccination.
In September and October 2011, representative urban household telephone surveys were conducted in five provinces in China with a response rate of 6%. Four target groups were defined for analysis: 1) children ≤ 5 years old; 2) elderly persons aged ≥ 60 years old; 3) health care workers (persons working in the medical field) and 4) chronically ill persons.
The overall mean vaccination rate was 9.0%. Among the four target groups, the rate of vaccination of children aged ≤ 5 years old (mean = 26%) was highest and the rate of elderly people aged ≥ 60 years old (mean = 7.4%) was the lowest, while the rates of persons who suffer from a chronic illness (mean = 9.4%) and health care workers (9.5%) were similar. A subsidy for influenza vaccination, age group, health care workers, suffering from a chronic illness and living in Eastern China were independent significant predictors for influenza vaccination.
The seasonal influenza vaccination coverage rates among urban populations in selected cities and provinces in China were far below previously reported rates in developed countries. Influenza vaccination coverage rates differed widely between different target groups and provinces in China. Subsidy policy might have a positive effect on influenza vaccination rate, but further cost-effectiveness studies, as well as the vaccination rate associated factors studies are still needed to inform strategies to increase coverage.
Limited permeability in solid tumors significantly restricts the anticancer efficacy of nanomedicines. Light‐driven nanomotors powered by photothermal converting engines are appealing carriers for ...directional drug delivery and simultaneous phototherapy. Nowadays, it is still a great challenge to construct metal‐free photothermal nanomotors for a programmable anticancer treatment. Herein, one kind of photoactivated organic nanomachines is reported with asymmetric geometry assembled by light‐to‐heat converting semiconducting polymer engine and macromolecular anticancer payload through a straightforward nanoprecipitation process. The NIR‐fueled polymer engine can be remotely controlled to power the nanomachines for light‐driven thermophoresis in the liquid media and simultaneously thermal ablating the cancer cells. The great manipulability of the nanomachines allows for programming of their self‐propulsion in the tumor microenvironment for effectively improving cellular uptake and tumor penetration of the anticancer payload. Taking the benefit from this behavior, a programmed treatment process is established at a low drug dose and a low photothermal temperature for significantly enhancing the antitumor efficacy.
Photoactivated organic nanomachines are powered by an NIR‐fueled organic semiconducting polymer engine for light‐driven traversing of physiological barriers in the tumor microenvironment. The great manipulability of the organic nanomachines allows precise programming of the antitumor treatment for significantly enhanced efficacy at a low drug dose and a low photothermal temperature.
Nanomedicines confront various complicated physiological barriers limiting the accumulation and deep penetration in the tumor microenvironment, which seriously restricts the efficacy of antitumor ...therapy. Self‐propelled nanocarriers assembled with kinetic engines can translate external energy into orientated motion for tumor penetration. However, achieving a stable ultrafast permeability at the tumor site remains challenging. Here, sub‐200 nm photoactivated completely organic nanorockets (NRs), with asymmetric geometry conveniently assembled from photothermal semiconducting polymer payload and thermo‐driven macromolecular propulsion through a straightforward nanoprecipitation process, are presented. The artificial NRs can be remotely manipulated by 808 nm near‐infrared light to trigger the photothermal conversion and Curtius rearrangement reaction within the particles for robustly pushing nitrogen out into the solution. Such a two‐stage light‐to‐heat‐to‐chemical energy transition effectively powers the NRs for an ultrafast (≈300 µm s−1) and chemical medium‐independent self‐propulsion in the liquid media. That endows the NRs with high permeability against physiological barriers in the tumor microenvironment to directionally deliver therapeutic agents to target lesions for elevating tumor accumulation, deep penetration, and cellular uptake, resulting in a significant enhancement of antitumor efficacy. This work will inspire the design of advanced kinetic systems for powering intelligent nanomachines in biomedical applications.
Engineered organic nanorockets are powered by photoactivated organic kinetic systems through a two‐stage light‐to‐heat‐to‐chemical energy transition for a stable ultrafast (≈300 µm s−1) self‐propulsion in the liquid media. The programmable navigation allows a high permeability against physiological barriers for elevating accumulation and deep penetration at the tumor site, thereby significantly enhancing the antitumor efficacy of the nanomedicines.
In the field of computational oncology, patient status is often assessed using radiology-genomics, which includes two key technologies and data, such as radiology and genomics. Recent advances in ...deep learning have facilitated the integration of radiology-genomics data, and even new omics data, significantly improving the robustness and accuracy of clinical predictions. These factors are driving artificial intelligence (AI) closer to practical clinical applications. In particular, deep learning models are crucial in identifying new radiology-genomics biomarkers and therapeutic targets, supported by explainable AI (xAI) methods. This review focuses on recent developments in deep learning for radiology-genomics integration, highlights current challenges, and outlines some research directions for multimodal integration and biomarker discovery of radiology-genomics or radiology-omics that are urgently needed in computational oncology.
Nb 3 Sn is now recognized as the practical material for the high-field applications from 10 to 16 T, for particle accelerators, fusion and other related fields. However, the obtaining of high J c in ...Nb 3 Sn presently contradicts one of the most important stability criteria: to keep a small filament size, which makes the stability issue particularly prominent. Large filament sizes frequently cause flux jumps in the conductor, making quench detection and protection more challenging for Nb 3 Sn magnets. In this work, we try to numerically simulate the flux jumps more accurately down to a filamentary scale. Based on the theory of superconducting dynamics, we have established a theoretical model to describe the magnetothermal instability of Nb 3 Sn wires by considering the superconductor flux dynamics, heat diffusion and temperature response. The preliminary analysis of the test results for LPF3 which is a 16-T hybrid common coil dipole magnet fabricated by Institute of High Energy Physics is conducted to study the causes of its quenching and the impact of flux jumps on the magnet.
Autonomous exploration and mapping in unknown environments is a critical capability for robots. Existing exploration techniques (e.g., heuristic-based and learning-based methods) do not consider the ...regional legacy issues, i.e., the great impact of smaller unexplored regions on the whole exploration process, which results in a dramatic reduction in their later exploration efficiency. To this end, this paper proposes a Local-and-Global Strategy (LAGS) algorithm that combines a local exploration strategy with a global perception strategy, which considers and solves the regional legacy issues in the autonomous exploration process to improve exploration efficiency. Additionally, we further integrate Gaussian process regression (GPR), Bayesian optimization (BO) sampling, and deep reinforcement learning (DRL) models to efficiently explore unknown environments while ensuring the robot's safety. Extensive experiments show that the proposed method could explore unknown environments with shorter paths, higher efficiencies, and stronger adaptability on different unknown maps with different layouts and sizes.
Abstract
Background
Because the patients undergoing medial patellofemoral ligament reconstruction (MPFLr) combined with medial tibial tubercle transfer (TTT) procedure are usually young and active, ...the quality of life (QoL) is also an important prognostic factor for patients with recurrent patellar dislocation. Assessing QoL can provide more useful and accurate evidence for the effects of this procedure. This study aimed to evaluate QoL following MPFLr combined with TTT, compared with isolated MPFLr (iMPFLr).
Methods
Fifty-one patients who underwent iMPFLr + TTT and 48 patients who underwent iMPFLr were included. Clinical evaluation included QoL (EQ-5D-5L and EQ-5D VAS), functional outcomes (Kujala, Lysholm and Tegner activity scores), physical examinations (patellar apprehension test and range of motion) and redislocation rates. Radiological evaluation included patellar tilt angle and bisect offset. These preoperative and postoperative results were compared between groups at baseline and the final follow-up. The paired and independent
t
tests were used for the data following a normal distribution. Otherwise, the Wilcoxon and Mann–Whitney
U
tests were used to analyze the differences. Categorical variables were compared by chi-square or Fisher’s exact test.
Results
All of the QoL (EQ-5D-5L and EQ-5D VAS), clinical results and radiological outcomes significantly improved in both groups at the final follow-up, with no significant differences between groups. There was no significant difference in five dimensions of EQ-5D at the final follow-up, although percentages of people with problems of mobility and pain/discomfort were higher in the MPFLr + TTT group. Female patients had lower EQ-5D index and EQ-5D VAS compared with male patients in both groups at the final follow-up, but there was only a significant difference in the EQ-5D VAS.
Conclusions
Both MPFLr + TTT and iMPFLr groups obtained similar and satisfactory improvements in the QoL, clinical results and radiological outcomes, indicating that MPFLr combined with TTT is a safe and effective procedure, which can significantly improve the QoL for patients with recurrent patellar dislocation in cases of pathologically lateralized TT. However, female patients obtained lower QoL than males.
Wide attention has been paid to named entity recognition (NER) in specific fields. Among the representative tasks are the aspect term extraction (ATE) in user online comments and the biomedical named ...entity recognition (BioNER) in medical documents. Existing methods only perform well in a particular field, and it is difficult to maintain an advantage in other fields. In this article, we propose a supervised learning method that can be used for much special domain NER tasks. The model consists of two parts, a multidimensional self-attention (MDSA) network and a CNN-based model. The multidimensional self-attention mechanism can calculate the importance of the context to the current word, select the relevance according to the importance, and complete the update of the word vector. This update mechanism allows the subsequent CNN model to have variable-length memory of sentence context. We conduct experiments on benchmark datasets of ATE and BioNER tasks. The results show that our model surpasses most baseline methods.
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