Analysis of factors associated with postoperative systemic inflammatory response syndrome in patients with urine culture-positive stone lithotripsy.

Introduction: Systemic inflammatory response syndrome (SIRS) is a significant postoperative complication following lithotripsy, particularly in patients with positive urine cultures. Understanding the factors that contribute to the development of SIRS in these patients is crucial for improving clinical outcomes and reducing morbidity. Materials and methods: From 2022 to 2023, patients with preoperative positive urine culture who underwent minimally invasive uroscopic lithotripsy in Wuhan Union Hospital were retrospectively analyzed. Results: A total of 393 patients with positive urine cultures underwent endoscopic lithotripsy, and 13.2% (52/393) were diagnosed with SIRS by relevant indicators after surgery. Multivariate logistic regression was used to study the risk factors for the occurrence of SIRS in patients postoperatively, which were preoperative positive WBC in urinalysis (OR = 5.685, p = 0.0051) and postoperative hemoglobin drop of greater than 5 g/L (OR = 2.180, p = 0.0145). Notably, preoperative upper urinary tract drainage was found to be a protective factor (OR = 0.4029, p = 0.0302), and postoperative C-reactive protein (CRP) value (OR = 1.025, p < 0.0001) and procalcitonin (PCT) value (OR = 1.066, p < 0.0001) were predictive factors. Besides, postoperative hemoglobin drop showed a weak correlation with surgical duration (r = 0.1589, p = 0.0016). Conclusions: In summary, our study identifies key factors affecting the occurrence of SIRS after lithotripsy for urine culture-positive stone: preoperative positive WBC in urinalysis, postoperative hemoglobin drop, and preoperative upper urinary tract drainage. And monitoring postoperative CRP and PCT levels helps to predict SIRS.

Non-contrasted computed tomography (NCCT) based chronic thromboembolic pulmonary hypertension (CTEPH) automatic diagnosis using cascaded network with multiple instance learning.

Objective.The diagnosis of chronic thromboembolic pulmonary hypertension (CTEPH) is challenging due to nonspecific early symptoms, complex diagnostic processes, and small lesion sizes. This study aims to develop an automatic diagnosis method for CTEPH using non-contrasted computed tomography (NCCT) scans, enabling automated diagnosis without precise lesion annotation.Approach.A novel cascade network (CN) with multiple instance learning (CNMIL) framework was developed to improve the diagnosis of CTEPH. This method uses a CN architecture combining two Resnet-18 CNN networks to progressively distinguish between normal and CTEPH cases. Multiple instance learning (MIL) is employed to treat each 3D CT case as a 'bag' of image slices, using attention scoring to identify the most important slices. An attention module helps the model focus on diagnostically relevant regions within each slice. The dataset comprised NCCT scans from 300 subjects, including 117 males and 183 females, with an average age of 52.5 ± 20.9 years, consisting of 132 normal cases and 168 cases of lung diseases, including 88 cases of CTEPH. The CNMIL framework was evaluated using sensitivity, specificity, and the area under the curve (AUC) metrics, and compared with common 3D supervised classification networks and existing CTEPH automatic diagnosis networks.Main results. The CNMIL framework demonstrated high diagnostic performance, achieving an AUC of 0.807, accuracy of 0.833, sensitivity of 0.795, and specificity of 0.849 in distinguishing CTEPH cases. Ablation studies revealed that integrating MIL and the CN significantly enhanced performance, with the model achieving an AUC of 0.978 and perfect sensitivity (1.000) in normal classification. Comparisons with other 3D network architectures confirmed that the integrated model outperformed others, achieving the highest AUC of 0.8419.Significance. The CNMIL network requires no additional scans or annotations, relying solely on NCCT. This approach can improve timely and accurate CTEPH detection, resulting in better patient outcomes.

Significantly Promoting the Thermal Conductivity and Machinability of Negative Thermal Expansion Alloy via In Situ Precipitation of Copper Networks.

Rapid advancements in electronic devices yield an urgent demand for high-performance electronic packaging materials with high thermal conductivity, low thermal expansion, and great mechanical properties. However, it is a great challenge for current design philosophies to fulfill all the requirements simultaneously. Here, an effective strategy is proposed for significantly promoting the thermal conductivity and machinability of negative thermal expansion alloy (Zr,Nb)Fe2 through eutectic precipitation of copper networks. The eutectic dual-phase alloy exhibits an isotropic chips-matched thermal expansion coefficient and a thermal conductivity enhancement exceeding 200% compared with (Zr,Nb)Fe2, along with an ultimate compressive strength of 550 MPa. The addition of copper reorganizes the composition of (Zr,Nb)Fe2, which smooths the magnetic transition and shifts it toward higher temperature, resulting in linear low thermal expansion in a wide temperature range. The highly fine eutectic copper lamellae construct high thermal conductivity networks within (Zr,Nb)Fe2, serving as highways for heat transfer electrons and phonons. The in situ forming of eutectic copper lamellae also facilitates the mechanical properties by enhancing interfacial bonding and bearing additional stress after yielding of (Zr,Nb)Fe2. This work provides a novel strategy for promoting thermal conductivity and mechanical properties of negative thermal expansion alloys via eutectic precipitation of copper networks.

RMCProfile7: reverse Monte Carlo for multiphase systems.

This work introduces a completely rewritten version of the program RMCProfile (version 7), big-box, reverse Monte Carlo modelling software for analysis of total scattering data. The major new feature of RMCProfile7 is the ability to refine multiple phases simultaneously, which is relevant for many current research areas such as energy materials, catalysis and engineering. Other new features include improved support for molecular potentials and rigid-body refinements, as well as multiple different data sets. An empirical resolution correction and calculation of the pair distribution function as a back-Fourier transform are now also available. RMCProfile7 is freely available for download at https://rmcprofile.ornl.gov/.

Predicting major adverse cardiac events using radiomics nomogram of pericoronary adipose tissue based on CCTA: A multi-center study.

BACKGROUND: The evolution of coronary atherosclerotic heart disease (CAD) is intricately linked to alterations in the pericoronary adipose tissue (PCAT). In recent epochs, characteristics of the PCAT have progressively ascended as focal points of research in CAD risk stratification and individualized clinical decision-making. Harnessing radiomic methodologies allows for the meticulous extraction of imaging features from these adipose deposits. Coupled with machine learning paradigms, we endeavor to establish predictive models for the onset of major adverse cardiovascular events (MACE). PURPOSE: To appraise the predictive utility of radiomic features of PCAT derived from coronary computed tomography angiography (CCTA) in forecasting MACE. METHODS: We retrospectively incorporated data from 314 suspected or confirmed CAD patients admitted to our institution from June 2019 to December 2022. An additional cohort of 242 patients from two external institutions was encompassed for external validation. The endpoint under consideration was the occurrence of MACE after a 1-year follow-up. MACE was delineated as cardiovascular mortality, newly diagnosed myocardial infarction, hospitalization (or re-hospitalization) for heart failure, and coronary target vessel revascularization occurring more than 30 days post-CCTA examination. All enrolled patients underwent CCTA scanning. Radiomic features were meticulously extracted from the optimal diastolic phase axial slices of CCTA images. Feature reduction was achieved through a composite feature selection algorithm, laying the groundwork for the radiomic signature model. Both univariate and multivariate analyses were employed to assess clinical variables. A multifaceted logistic regression analysis facilitated the crafting of a clinical-radiological-radiomic combined model (or nomogram). Receiver operating characteristic (ROC) curves, calibration, and decision curve analyses (DCA) were delineated, with the area under the ROC curve (AUCs) computed to gauge the predictive prowess of the clinical model, radiomic model, and the synthesized ensemble. RESULTS: A total of 12 radiomic features closely associated with MACE were identified to establish the radiomic model. Multivariate logistic regression results demonstrated that smoking, age, hypertension, and dyslipidemia were significantly correlated with MACE. In the integrated nomogram, which amalgamated clinical, imaging, and radiomic parameters, the diagnostic performance was as follows: 0.970 AUC, 0.949 accuracy (ACC), 0.833 sensitivity (SEN), 0.981 specificity (SPE), 0.926 positive predictive value (PPV), and 0.955 negative predictive value (NPV). The calibration curve indicated a commendable concordance of the nomogram, and the decision curve analysis underscored its superior clinical utility. CONCLUSIONS: The integration of radiomic signatures from PCAT based on CCTA, clinical indices, and imaging parameters into a nomogram stands as a promising instrument for prognosticating MACE events.

Transient Covalency in Molten Uranium(III) Chloride.

Uranium is arguably the most essential element in the actinide series, serving as a crucial component of nuclear fuels. While U is recognized for engaging the 5f orbitals in chemical bonds under normal conditions, little is known about its coordination chemistry and the nature of bonding interactions at extreme conditions of high temperature. Here we report experimental and computational evidence for the shrinkage of the average U-ligand distance in UCl3 upon the solid-to-molten phase transition, leading to the formation of a significant fraction of short, transient U-Cl bonds with the enhanced involvement of U 5f valence orbitals. These findings reveal that extreme temperatures create an unusual heterogeneous bonding environment around U(III) with distinct inner- and outer-coordination subshells.

Artificial intelligence in perinatal mental health research: A scoping review.

The intersection of Artificial Intelligence (AI) and perinatal mental health research presents promising avenues, yet uncovers significant challenges for innovation. This review explicitly focuses on this multidisciplinary field and undertakes a comprehensive exploration of existing research therein. Through a scoping review guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, we searched relevant literature spanning a decade (2013-2023) and selected fourteen studies for our analysis. We first provide an overview of the main AI techniques and their development, including traditional methods across different categories, as well as recent emerging methods in the field. Then, through our analysis of the literature, we summarize the predominant AI and ML techniques adopted and their applications in perinatal mental health studies, such as identifying risk factors, predicting perinatal mental health disorders, voice assistants, and Q&A chatbots. We also discuss existing limitations and potential challenges that hinder AI technologies from improving perinatal mental health outcomes, and suggest several promising directions for future research to meet real needs in the field and facilitate the translation of research into clinical settings.

TransAC4C-a novel interpretable architecture for multi-species identification of N4-acetylcytidine sites in RNA with single-base resolution.

N4-acetylcytidine (ac4C) is a modification found in ribonucleic acid (RNA) related to diseases. Expensive and labor-intensive methods hindered the exploration of ac4C mechanisms and the development of specific anti-ac4C drugs. Therefore, an advanced prediction model for ac4C in RNA is urgently needed. Despite the construction of various prediction models, several limitations exist: (1) insufficient resolution at base level for ac4C sites; (2) lack of information on species other than Homo sapiens; (3) lack of information on RNA other than mRNA; and (4) lack of interpretation for each prediction. In light of these limitations, we have reconstructed the previous benchmark dataset and introduced a new dataset including balanced RNA sequences from multiple species and RNA types, while also providing base-level resolution for ac4C sites. Additionally, we have proposed a novel transformer-based architecture and pipeline for predicting ac4C sites, allowing for highly accurate predictions, visually interpretable results and no restrictions on the length of input RNA sequences. Statistically, our work has improved the accuracy of predicting specific ac4C sites in multiple species from less than 40% to around 85%, achieving a high AUC > 0.9. These results significantly surpass the performance of all existing models.

Strong Local Polarization Fluctuations Enabled High Electrostatic Energy Storage in Pb-Free Relaxors.

Electrostatic energy-storage ceramic capacitors are essential components of modern electrified power systems. However, improving their energy-storage density while maintaining high efficiency to facilitate cutting-edge miniaturized and integrated applications remains an ongoing challenge. Herein, we report a record-high energy-storage density of 20.3 J cm-3 together with a high efficiency of 89.3% achieved by constructing a relaxor ferroelectric state with strongly enhanced local polarization fluctuations. This is realized by incorporating highly polarizable, heterovalent, and large-sized Zn and Nb ions into a Bi0.5Na0.5TiO3-BaTiO3 ferroelectric matrix with very strong tetragonal distortion. Element-specific local structure analysis revealed that the foreign ions strengthen the magnitude of the unit-cell polarization vectors while simultaneously reducing their orientation anisotropy and forming strong fluctuations in both magnitude and orientation within 1-3 nm polar clusters. This leads to a particularly high polarization variation (ΔP) of 72 µC cm-2, low hysteresis, and a high effective polarization coefficient at a high breakdown strength of 80 kV mm-1. This work has surpassed the current energy density limit of 20 J cm-3 in bulk Pb-free ceramics and has demonstrated that controlling the local structure via the chemical composition design can open up new possibilities for exploring relaxors with high energy-storage performance.

Role of dietary nutrients and metabolism in colorectal cancer.

Colorectal cancer (CRC) is one of the most common malignancies and the leading causes of cancer related deaths worldwide. The development of CRC is driven by a combination of genetic and environmental factors. There is growing evidence that changes in dietary nutrition may modulate the CRC risk, and protective effects on the risk of developing CRC have been advocated for specific nutrients such as glucose, amino acids, lipid, vitamins, micronutrients and prebiotics. Metabolic crosstalk between tumor cells, tumor microenvironment components and intestinal flora further promote proliferation, invasion and metastasis of CRC cells and leads to treatment resistance. This review summarizes the research progress on CRC prevention, pathogenesis, and treatment by dietary supplementation or deficiency of glucose, amino acids, lipids, vitamins, micronutri-ents, and prebiotics, respectively. The roles played by different nutrients and dietary crosstalk in the tumor microenvironment and metabolism are discussed, and nutritional modulation is inspired to be beneficial in the prevention and treatment of CRC.

IGCNSDA: unraveling disease-associated snoRNAs with an interpretable graph convolutional network.

Accurately delineating the connection between short nucleolar RNA (snoRNA) and disease is crucial for advancing disease detection and treatment. While traditional biological experimental methods are effective, they are labor-intensive, costly and lack scalability. With the ongoing progress in computer technology, an increasing number of deep learning techniques are being employed to predict snoRNA-disease associations. Nevertheless, the majority of these methods are black-box models, lacking interpretability and the capability to elucidate the snoRNA-disease association mechanism. In this study, we introduce IGCNSDA, an innovative and interpretable graph convolutional network (GCN) approach tailored for the efficient inference of snoRNA-disease associations. IGCNSDA leverages the GCN framework to extract node feature representations of snoRNAs and diseases from the bipartite snoRNA-disease graph. SnoRNAs with high similarity are more likely to be linked to analogous diseases, and vice versa. To facilitate this process, we introduce a subgraph generation algorithm that effectively groups similar snoRNAs and their associated diseases into cohesive subgraphs. Subsequently, we aggregate information from neighboring nodes within these subgraphs, iteratively updating the embeddings of snoRNAs and diseases. The experimental results demonstrate that IGCNSDA outperforms the most recent, highly relevant methods. Additionally, our interpretability analysis provides compelling evidence that IGCNSDA adeptly captures the underlying similarity between snoRNAs and diseases, thus affording researchers enhanced insights into the snoRNA-disease association mechanism. Furthermore, we present illustrative case studies that demonstrate the utility of IGCNSDA as a valuable tool for efficiently predicting potential snoRNA-disease associations. The dataset and source code for IGCNSDA are openly accessible at: https://github.com/altriavin/IGCNSDA.

A Study on the Field Emission Characteristics of High-Quality Wrinkled Multilayer Graphene Cathodes.

Field emission (FE) necessitates cathode materials with low work function and high thermal and electrical conductivity and stability. To meet these requirements, we developed FE cathodes based on high-quality wrinkled multilayer graphene (MLG) prepared using the bubble-assisted chemical vapor deposition (B-CVD) method and investigated their emission characteristics. The result showed that MLG cathodes prepared using the spin-coating method exhibited a high field emission current density (~7.9 mA/cm2), indicating the excellent intrinsic emission performance of the MLG. However, the weak adhesion between the MLG and the substrate led to the poor stability of the cathode. Screen printing was employed to prepare the cathode to improve stability, and the influence of a silver buffer layer was explored on the cathode's performance. The results demonstrated that these cathodes exhibited better emission stability, and the silver buffer layer further enhanced the comprehensive field emission performance. The optimized cathode possesses low turn-on field strength (~1.5 V/µm), low threshold field strength (~2.65 V/µm), high current density (~10.5 mA/cm2), and good emission uniformity. Moreover, the cathode also exhibits excellent emission stability, with a current fluctuation of only 6.28% during a 4-h test at 1530 V.

Conventional dendritic cells 2, the targeted antigen-presenting-cell, induces enhanced type 1 immune responses in mice immunized with CVC1302 in oil formulation.

Multifunctional CD4+ T helper 1 (Th1) cells, producing IFN-γ, TNF-α and IL-2, define a correlate of vaccine-mediated protection against intracellular infection. In our previous study, we found that CVC1302 in oil formulation promoted the differentiation of IFN-γ+/TNF-α+/IL-2+Th1 cells. In order to extend the application of CVC1302 in oil formulation, this study aimed to elucidate the mechanism of action in improving the Th1 immune response. Considering the signals required for the differentiation of CD4+ T cells to Th1 cells, we detected the distribution of innate immune cells and the model antigen OVA-FITC in lymph node (LN), as well as the quantity of cytokines produced by the innate immune cells. The results of these experiments show that, cDC2 and OVA-FITC localized to interfollicular region (IFR) of the draining lymph nodes, inflammatory monocytes localized to both IFR and T cell zone, which mainly infiltrate from the blood. In this inflammatory niche within LN, CD4+ T cells were attracted into IFR by CXCL10, secreted by inflammatory monocytes, then activated by cDC2, secreting IL-12. Above all, CVC1302 in oil formulation, on the one hand, targeted antigen and inflammatory monocytes into the LN IFR in order to attract CD4+ T cells, on the other hand, targeted cDC2 to produce IL-12 in order to promote optimal Th1 differentiation. The new finding will provide a blueprint for application of immunopotentiators in optimal formulations.

Electroencephalography (EEG) based epilepsy diagnosis via multiple feature space fusion using shared hidden space-driven multi-view learning.

Epilepsy is a chronic, non-communicable disease caused by paroxysmal abnormal synchronized electrical activity of brain neurons, and is one of the most common neurological diseases worldwide. Electroencephalography (EEG) is currently a crucial tool for epilepsy diagnosis. With the development of artificial intelligence, multi-view learning-based EEG analysis has become an important method for automatic epilepsy recognition because EEG contains difficult types of features such as time-frequency features, frequency-domain features and time-domain features. However, current multi-view learning still faces some challenges, such as the difference between samples of the same class from different views is greater than the difference between samples of different classes from the same view. In view of this, in this study, we propose a shared hidden space-driven multi-view learning algorithm. The algorithm uses kernel density estimation to construct a shared hidden space and combines the shared hidden space with the original space to obtain an expanded space for multi-view learning. By constructing the expanded space and utilizing the information of both the shared hidden space and the original space for learning, the relevant information of samples within and across views can thereby be fully utilized. Experimental results on a dataset of epilepsy provided by the University of Bonn show that the proposed algorithm has promising performance, with an average classification accuracy value of 0.9787, which achieves at least 4% improvement compared to single-view methods.

Chemical Framework to Design Linear-like Relaxors toward Capacitive Energy Storage.

ABO3-type perovskite relaxor ferroelectrics (RFEs) have emerged as the preferred option for dielectric capacitive energy storage. However, the compositional design of RFEs with high energy density and efficiency poses significant challenges owing to the vast compositional space and the absence of general rules. Here, we present an atomic-level chemical framework that captures inherent characteristics in terms of radius and ferroelectric activity of ions. By categorizing A/B-site ions as host framework, rattling, ferroelectrically active, and blocking ions and assembling these four types of ions with specific criteria, linear-like relaxors with weak locally correlated and highly extendable unit-cell polarization vectors can be constructed. As example, we demonstrate two new compositions of Bi0.5K0.5TiO3-based and BaTiO3-based relaxors, showing extremely high recoverable energy densities of 17.3 and 12.1 J cm-3, respectively, both with a high efficiency of about 90%. Further, the role of different types of ions in forming heterogeneous polar structures is identified through element-specific local structure analysis using neutron total scattering combined with reverse Monte Carlo modeling. Our work not only opens up new avenues toward rational compositional design of high energy storage performance lead-free RFEs but also sheds light on atomic-level manipulation of functional properties in compositionally complex ferroelectrics.

Amorphous Oxyhalide Matters for Achieving Lithium Superionic Conduction.

The recently surged halide-based solid electrolytes (SEs) are great candidates for high-performance all-solid-state batteries (ASSBs), due to their decent ionic conductivity, wide electrochemical stability window, and good compatibility with high-voltage oxide cathodes. In contrast to the crystalline phases in halide SEs, amorphous components are rarely understood but play an important role in Li-ion conduction. Here, we reveal that the presence of amorphous component is common in halide-based SEs that are prepared via mechanochemical method. The fast Li-ion migration is found to be associated with the local chemistry of the amorphous proportion. Taking Zr-based halide SEs as an example, the amorphization process can be regulated by incorporating O, resulting in the formation of corner-sharing Zr-O/Cl polyhedrons. This structural configuration has been confirmed through X-ray absorption spectroscopy, pair distribution function analyses, and Reverse Monte Carlo modeling. The unique structure significantly reduces the energy barriers for Li-ion transport. As a result, an enhanced ionic conductivity of (1.35 ± 0.07) × 10-3 S cm-1 at 25 °C can be achieved for amorphous Li3ZrCl4O1.5. In addition to the improved ionic conductivity, amorphization of Zr-based halide SEs via incorporation of O leads to good mechanical deformability and promising electrochemical performance. These findings provide deep insights into the rational design of desirable halide SEs for high-performance ASSBs.

Multi-omics and Multi-VOIs to predict esophageal fistula in esophageal cancer patients treated with radiotherapy.

OBJECTIVE: This study aimed to develop a prediction model for esophageal fistula (EF) in esophageal cancer (EC) patients treated with intensity-modulated radiation therapy (IMRT), by integrating multi-omics features from multiple volumes of interest (VOIs). METHODS: We retrospectively analyzed pretreatment planning computed tomographic (CT) images, three-dimensional dose distributions, and clinical factors of 287 EC patients. Nine groups of features from different combination of omics [Radiomics (R), Dosiomics (D), and RD (the combination of R and D)], and VOIs [esophagus (ESO), gross tumor volume (GTV), and EG (the combination of ESO and GTV)] were extracted and separately selected by unsupervised (analysis of variance (ANOVA) and Pearson correlation test) and supervised (Student T test) approaches. The final model performance was evaluated using five metrics: average area under the receiver-operator-characteristics curve (AUC), accuracy, precision, recall, and F1 score. RESULTS: For multi-omics using RD features, the model performance in EG model shows: AUC, 0.817 ± 0.031; 95% CI 0.805, 0.825; p < 0.001, which is better than single VOI (ESO or GTV). CONCLUSION: Integrating multi-omics features from multi-VOIs enables better prediction of EF in EC patients treated with IMRT. The incorporation of dosiomics features can enhance the model performance of the prediction.

Evaluating the Field Emission Properties of N-Type Black Silicon Cold Cathodes Based on a Three-Dimensional Model.

Black silicon (BS), a nanostructured silicon surface containing highly roughened surface morphology, has recently emerged as a promising candidate for field emission (FE) cathodes in novel electron sources due to its huge number of sharp tips with ease of large-scale fabrication and controllable geometrical shapes. However, evaluating the FE performance of BS-based nanostructures with high accuracy is still a challenge due to the increasing complexity in the surface morphology. Here, we demonstrate a 3D modeling methodology to fully characterize highly disordered BS-based field emitters randomly distributed on a roughened nonflat surface. We fabricated BS cathode samples with different morphological features to demonstrate the validity of this method. We utilize parametrized scanning electron microscopy images that provide high-precision morphology details, successfully describing the electric field distribution in field emitters and linking the theoretical analysis with the measured FE property of the complex nanostructures with high precision. The 3D model developed here reveals a relationship between the field emission performance and the density of the cones, successfully reproducing the classical relationship between current density J and electric field E (J-E curve). The proposed modeling approach is expected to offer a powerful tool to accurately describe the field emission properties of large-scale, disordered nano cold cathodes, thus serving as a guide for the design and application of BS as a field electron emission material.

Analysis of CVC1302-Mediated Enhancement of Monocyte Recruitment in Inducing Immune Responses.

Monocytes (Mos) are believed to play important roles during the generation of immune response. In our previous study, CVC1302, a complex of PRRs agonists, was demonstrated to recruit Mo into lymph nodes (LNs) in order to present antigen and secret chemokines (CXCL9 and CXCL10), which attracted antigen-specific CD4+ T cells. As it is known that Mos in mice are divided into two main Mo subsets (Ly6C+ Mo and Ly6C- Mo), we aimed to clarify the CVC1302-recruiting Mo subset and functions in the establishment of immunity. In this study, we found that CVC1302 attracted both Ly6C+ Mo and Ly6C- Mo into draining LNs, which infiltrated from different origins, injection muscles and high endothelial venule (HEV), respectively. We also found that the numbers of OVA+ Ly6C+ Mo in the draining LNs were significantly higher compared with OVA+ Ly6C- Mo. However, the levels of CXCL9 and CXCL10 produced by Ly6C- Mo were significantly higher than Ly6C+ Mo, which plays important roles in attracting antigen-specific CD4+ T cells. Under the analysis of their functions in initiating immune responses, we found that the ability of the Ly6C+ monocyte was mainly capturing and presenting antigens, otherwise; the ability of the Ly6C- monocyte was mainly secreting CXCL9 and CXCL10, which attracted antigen-specific CD4+ T cells through CXCR3. These results will provide new insights into the development of new immunopotentiators and vaccines.

Multi-omics fusion with soft labeling for enhanced prediction of distant metastasis in nasopharyngeal carcinoma patients after radiotherapy.

Omics fusion has emerged as a crucial preprocessing approach in medical image processing, significantly assisting several studies. One of the challenges encountered in integrating omics data is the unpredictability arising from disparities in data sources and medical imaging equipment. Due to these differences, the distribution of omics futures exhibits spatial heterogeneity, diminishing their capacity to enhance subsequent tasks. To overcome this challenge and facilitate the integration of their joint application to specific medical objectives, this study aims to develop a fusion methodology for nasopharyngeal carcinoma (NPC) distant metastasis prediction to mitigate the disparities inherent in omics data. The multi-kernel late-fusion method can reduce the impact of these differences by mapping the features using the most suiTable single-kernel function and then combining them in a high-dimensional space that can effectively represent the data. The proposed approach in this study employs a distinctive framework incorporating a label-softening technique alongside a multi-kernel-based Radial basis function (RBF) neural network to address these limitations. An efficient representation of the data may be achieved by utilizing the multi-kernel to map the inherent features and then merging them in a space with many dimensions. However, the inflexibility of label fitting poses a constraint on using multi-kernel late-fusion methods in complex NPC datasets, hence affecting the efficacy of general classifiers in dealing with high-dimensional characteristics. The label softening increases the disparity between the two cohorts, providing a more flexible structure for allocating labels. The proposed model is evaluated on multi-omics datasets, and the results demonstrate its strength and effectiveness in predicting distant metastasis of NPC patients.