Acidity-targeting transition-aided universal chimeric antigen receptor T-cell (ATT-CAR-T) therapy for the treatment of solid tumors.

The use of CAR-T cells in treating solid tumors frequently faces significant challenges, mainly due to the heterogeneity of tumor antigens. This study assessed the efficacy of an acidity-targeting transition-aided universal chimeric antigen receptor T (ATT-CAR-T) cell strategy, which is facilitated by an acidity-targeted transition. Specifically, the EGFRvIII peptide was attached to the N-terminus of a pH-low insertion peptide. Triggered by the acidic conditions of the tumor microenvironment, this peptide alters its structure and selectively integrates into the membrane of solid tumor cells. The acidity-targeted transition component effectively relocated the EGFRvIII peptide across various tumor cell membranes; thus, allowing the direct destruction of these cells by EGFRvIII-specific CAR-T cells. This method was efficient even when endogenous antigens were absent. In vivo tests showed marked antigen modification within the acidic tumor microenvironment using this component. Integrating this component with CAR-T cell therapy showed high effectiveness in combating solid tumors. These results highlight the capability of ATT-CAR-T cell therapy to address the challenges presented by tumor heterogeneity and expand the utility of CAR-T cell therapy in the treatment of solid tumors.

Potent antitumor activity of a bispecific T-cell engager antibody targeting the intracellular antigen KRAS G12V.

Kirsten Rat Sarcoma viral oncogene homolog (KRAS) is one of the most frequent oncogenes. However, there are limited treatment options due to its intracellular expression. To address this, we developed a novel bispecific T-cell engager (BiTE) antibody targeting HLA-A2/KRAS G12V complex and CD3 (HLA-G12V/CD3 BiTE). We examined its specific binding to tumor cells and T cells, as well as its anti-tumor effects in vivo. HLA-G12V/CD3 BiTE was expressed in Escherichia coli and its binding affinities to CD3 and HLA-A2/KRAS G12V were measured by flow cytometry, along with T-cell activation. In a xenograft pancreatic tumor model, the HLA-G12V/CD3 BiTE's anti-tumor effects were assessed through tumor growth, survival time, and safety. Our results demonstrated specific binding of HLA-G12V/CD3 BiTE to tumor cells with an HLA-A2/KRAS G12V mutation and T cells. The HLA-G12V/CD3 BiTE also activated T-cells in the presence of tumor cells in vitro. HLA-G12V/CD3 BiTE in vivo testing showed delayed tumor growth without severe toxicity to major organs and prolonged mouse survival. This study highlights the potential of constructing BiTEs recognizing an HLA-peptide complex and providing a novel therapy for cancer treatment targeting the intracellular tumor antigen.

Cellulose-based materials in environmental protection: A scientometric and visual analysis review.

As sustainable materials, cellulose-based materials have attracted significant attention in the field of environmental protection, resulting in the publication of numerous academic papers. However, there is a scarcity of literature that involving scientometric analysis within this specific domain. This review aims to address this gap and highlight recent research in this field by utilizing scientometric analysis and a historical review. As a result, 21 highly cited articles and 10 mostly productive journals were selected out. The scientometric analysis reveals that recent studies were objectively clustered into five interconnected main themes: extraction of cellulose from raw materials and its degradation, adsorption of pollutants using cellulose-based materials, cellulose-acetate-based membrane materials, nanocellulose-based materials, and other cellulose-based materials such as carboxymethyl cellulose and bacterial cellulose for environmental protection. Analyzing the distribution of author keywords and thoroughly examining relevant literature, the research focuses within these five themes were summarized. In the future, the development of eco-friendly and cost-effective methods for extracting and preparing cellulose and its derivatives, particularly nanocellulose-based materials, remains an enduring pursuit. Additionally, machine learning techniques holds promise for the advancement and application of cellulose-based materials. Furthermore, there is potential to expand the research and application scope of cellulose-based materials for environmental protection.

Non-destructive detection of defective maize kernels using hyperspectral imaging and convolutional neural network with attention module.

Rapid, effective and non-destructive detection of the defective maize kernels is crucial for their high-quality storage in granary. Hyperspectral imaging (HSI) coupled with convolutional neural network (CNN) based on spectral and spatial attention (Spl-Spal-At) module was proposed for identifying the different types of maize kernels. The HSI data within 380-1000 nm of six classes of sprouted, heat-damaged, insect-damaged, moldy, broken and healthy kernels was collected. The CNN-Spl-At, CNN-Spal-At and CNN-Spl-Spal-At models were established based on the spectra, images and their fusion features as inputs for the recognition of different kernels. Further compared the performances of proposed models and conventional models were built by support vector machine (SVM) and extreme learning machine (ELM). The results indicated that the recognition ability of CNN with attention series models was significantly better than that of SVM and ELM models and fused features were more conducive to expressing the appearance of different kernels than single features. And the CNN-Spl-Spal-At model had an optimal recognition result with high average classification accuracy of 98.04 % and 94.56 % for the training and testing sets, respectively. The recognition results were visually presented on the surface image of kernels with different colors. The CNN-Spl-Spal-At model was built in this study could effectively detect defective maize kernels, and it also had great potential to provide the analysis approaches for the development of non-destructive testing equipment based on HSI technique for maize quality.

Strategies to Mitigate Age-Related Bias in Machine Learning: Scoping Review.

BACKGROUND: Research suggests that digital ageism, that is, age-related bias, is present in the development and deployment of machine learning (ML) models. Despite the recognition of the importance of this problem, there is a lack of research that specifically examines the strategies used to mitigate age-related bias in ML models and the effectiveness of these strategies. OBJECTIVE: To address this gap, we conducted a scoping review of mitigation strategies to reduce age-related bias in ML. METHODS: We followed a scoping review methodology framework developed by Arksey and O'Malley. The search was developed in conjunction with an information specialist and conducted in 6 electronic databases (IEEE Xplore, Scopus, Web of Science, CINAHL, EMBASE, and the ACM digital library), as well as 2 additional gray literature databases (OpenGrey and Grey Literature Report). RESULTS: We identified 8 publications that attempted to mitigate age-related bias in ML approaches. Age-related bias was introduced primarily due to a lack of representation of older adults in the data. Efforts to mitigate bias were categorized into one of three approaches: (1) creating a more balanced data set, (2) augmenting and supplementing their data, and (3) modifying the algorithm directly to achieve a more balanced result. CONCLUSIONS: Identifying and mitigating related biases in ML models is critical to fostering fairness, equity, inclusion, and social benefits. Our analysis underscores the ongoing need for rigorous research and the development of effective mitigation approaches to address digital ageism, ensuring that ML systems are used in a way that upholds the interests of all individuals. TRIAL REGISTRATION: Open Science Framework AMG5P; https://osf.io/amg5p.

Predicting the role of the human gut microbiome in type 1 diabetes using machine-learning methods.

Gut microbes is a crucial factor in the pathogenesis of type 1 diabetes (T1D). However, it is still unclear which gut microbiota are the key factors affecting T1D and their influence on the development and progression of the disease. To fill these knowledge gaps, we constructed a model to find biomarker from gut microbiota in patients with T1D. We first identified microbial markers using Linear discriminant analysis Effect Size (LEfSe) and random forest (RF) methods. Furthermore, by constructing co-occurrence networks for gut microbes in T1D, we aimed to reveal all gut microbial interactions as well as major beneficial and pathogenic bacteria in healthy populations and type 1 diabetic patients. Finally, PICRUST2 was used to predict Kyoto Encyclopedia of Genes and Genomes (KEGG) functional pathways and KO gene levels of microbial markers to investigate the biological role. Our study revealed that 21 identified microbial genera are important biomarker for T1D. Their AUC values are 0.962 and 0.745 on discovery set and validation set. Functional analysis showed that 10 microbial genera were significantly positively associated with D-arginine and D-ornithine metabolism, spliceosome in transcription, steroid hormone biosynthesis and glycosaminoglycan degradation. These genera were significantly negatively correlated with steroid biosynthesis, cyanoamino acid metabolism and drug metabolism. The other 11 genera displayed an inverse correlation. In summary, our research identified a comprehensive set of T1D gut biomarkers with universal applicability and have revealed the biological consequences of alterations in gut microbiota and their interplay. These findings offer significant prospects for individualized management and treatment of T1D.

Controlled atmosphere storage drive proteomic change in Chinese Daohuaxiang.

Although the use of a controlled atmosphere is one of the most successful storage techniques, the mechanism thereof in rice storage remains unclear. We stored aromatic rice cultivar Daohuaxiang in a package filled with 98 % N2 and 35 % CO2 for 3 months. We investigated 2-acetyl-1-pyrroline loss, enzyme activities, and proteomics changes of rice during storage. The results showed that the content of 2-acetyl-1-pyrroline was reduced by 37.40 %, 25.65 %, and 43.89 % during storage using 98 % N2, 35 % CO2 controlled atmosphere storage, and conventional storage. Controlled atmosphere storage slowed down the increase of malondialdehyde content in Daohuaxiang. The results showed that 26S proteasome regulatory particle triple-A ATPase subunit 6, superoxide dismutase, glutathione transferase, and other key proteins were upregulated during 35 % CO2 regulation. This study provided a meaningful basis for exploring the regulation strategy of aromatic rice quality and strengthening the quality control of aromatic rice industry.

Genetic Factors Associated With Adverse Pregnancy Outcomes in Chronic Pancreatitis.

INTRODUCTION: The effects of genetic factors on pregnancy outcomes in chronic pancreatitis (CP) patients remain unclear. We evaluated the impacts of clinical features and mutations in main CP-susceptibility genes ( SPINK1 , PRSS1 , CTRC , and CFTR ) on pregnancy outcomes in Chinese CP patients. METHODS: This was a prospective cohort study with 14-year follow-up. The sample comprised female CP patients with documented pregnancy and known genetic backgrounds. Adverse pregnancy outcomes were compared between patients with and without gene mutations. Univariate and multivariate analyses were performed to determine the impact factors for adverse pregnancy outcomes. RESULTS: Totally, 160 female CP patients with a pregnancy history were enrolled; 59.4% of patients carried pathogenic mutations in CP-susceptibility genes. Adverse pregnancy outcomes occurred in 38 patients (23.8%); the prevalence of adverse outcomes was significantly higher in those harboring gene mutations than those without (30.5% vs 13.8%, P = 0.015). Notably, the rates of preterm delivery (12.6% vs 3.1%, P = 0.036) and abortion (17.9% vs 4.6%, P = 0.013) were remarkably higher in patients with gene mutations (especially SPINK1 mutations) than those without. In multivariate analyses, both CP-susceptibility gene mutations (odds ratio, 2.52; P = 0.033) and SPINK1 mutations (odds ratio, 2.60; P = 0.037) significantly increased the risk of adverse pregnancy outcomes. Acute pain attack during pregnancy was another risk factor for adverse pregnancy outcomes. DISCUSSION: Pathogenic mutations in CP-susceptibility genes, especially SPINK1 , were independently related to adverse pregnancy outcomes in CP patients. Significant attention should be paid to pregnant females harboring CP-susceptibility gene mutations (ClinicalTrials.gov: NCT06055595).

Indole-3-acetic acid improves periphyton's resistance to ultraviolet-B: From physiological-biochemical properties and bacteria community to livestock-polluted water purification.

Livestock-polluted water is a pressing water environmental issue in plateau pastoral regions, necessitating the adoption of eco-friendly solutions. Despite periphyton being a promising alternative, its efficacy is limited by the prevalence of intense ultraviolet radiation, particularly ultraviolet-B (UVB), in these regions. Therefore, this study employs molecular tools and small-scale trials to explore the crucial role of indole-3-acetic acid (IAA) in modulating periphyton characteristics and mediating nutrient removal from livestock-polluted water under UVB exposure. The results revealed that IAA augments periphyton's resilience to UVB stress through several pathways, including increasing periphyton's biomass, producing more extracellular polymeric substances (EPS), and enhancing antioxidant enzyme activities and photosynthetic activity of periphyton. Moreover, IAA addition increased periphyton's bacterial diversity, reshaped bacterial community structure, enhanced community stability, and elevated the R2 value of neutral processes in bacterial assembly from 0.257 to 0.651 under UVB. Practically, an IAA concentration of 50 mg/L was recommended. Small-scale trials confirmed the effectiveness of IAA in assisting UVB-stressed periphyton to remove nitrogen and phosphorus from livestock-polluted water, without the risk of nitrogen accumulation. These findings offer valuable insights into the protection of aquatic ecosystems in plateau pastoral regions based on periphyton property in an eco-friendly manner.

Mechanism of effects of electroacupuncture on memory and cognitive impairment in offspring rats with perinatal nicotine exposure. / ç”µé’ˆå¯¹å›´äº§æœŸå°¼å¤ä¸æš´éœ²å­ä»£å¤§é¼ è®°å¿†å’Œè®¤çŸ¥æŸä¼¤å½±å“çš„æœºåˆ¶æŽ¢è®¨.

OBJECTIVES: To observe the effects of electroacupuncture(EA) on memory, cognitive impairment, and the brain-derived neurotrophic factor(BDNF)/N-methyl-D-aspartate receptor subtype 1(NMDAR1) pathway in the brains of offspring rat with intrauterine growth restriction(IUGR) induced by perinatal nicotine exposure(PNE), so as to explore the underlying mechanism. METHODS: SD rats were randomly divided into normal, model, and EA groups, with 4 mothers and 10 offspring rats of each mother in each group. The IUGR model was established by subcutaneous injection of nicotine during pregnancy and lactation. From the 6th day of pregnancy in the mothers until the 21st day after birth of the offspring rats, EA (2 Hz/15 Hz, 1 mA) was administered bilaterally at the "Zusanli"(ST36) of mothers, once daily for 20 min. The brain organ coefficient was used to evaluate the brain development of the offspring rats. The Y-maze test and novel object recognition experiments were performed to assess memory and cognitive function. HE staining was used to observe the development and cellular morphology of the hippocampus and prefrontal cortex in the offspring rats. UV spectrophotometry was used to measure the glutamate(Glu) content in the hippocampus. ELISA was used to detect the BDNF content in the hippocampus. Western blot was performed to measure the protein expression of NMDAR1 in the hippocampus. Immunohistochemistry was used to count the number of BDNF-positive cells in the hippocampus and prefrontal cortex. RESULTS: Compared with the normal group, the brain organ coefficient, exploration time of the novel arm, spontaneous alternation rate, and novel object recognition index, contents of BDNF and expression of NMDAR1 proteins in the hippocampus, the number of BDNF-positive cells in the CA1 and CA3 regions of the hippocampus and prefrontal cortex were significantly reduced(P<0.01), while the Glu content in the hippocampus was significantly increased(P<0.01) in the model group of offspring rats；decreased cell number, scattered arrangement, and disrupted cellular structure were observed in the hippocampus and prefrontal cortex of offspring rats in the model group. Compared with the model group, the brain organ coefficient, exploration time of the novel arm, spontaneous alternation rate, and novel object recognition index, the BDNF contents and NMDAR1 protein expression in the hippocampus, the number of BDNF-positive cells in the hippocampal CA1 and CA3 regions and prefrontal cortex significantly increased(P<0.01, P<0.05), while the Glu content in the hippocampus was significantly decreased (P<0.01) in offspring rats of the EA group；increased cell number, neat arrangement, and reduced cellular damage were observed in the hippocampus and prefrontal cortex in the EA group. CONCLUSIONS: EA has an improving effect on memory and cognitive function impairment in offspring rats with IUGR induced by PNE, and this mechanism may be associated with the regulation of BDNF/NMDAR1 pathway, thereby improving the neuronal quantity and structure of the hippocampus and prefrontal cortex in offspring rats.

Acupuncture Relieves Cervical Spondylosis Radiculopathy by Regulating Spinal Microglia Activation Through MAPK Signaling Pathway in Rats.

Purpose: Local acupuncture has been found to have a good analgesic effect in rats with cervical spondylosis radiculopathy (CSR), but it lacks a regulatory effect on traditional Chinese medicine syndrome types of CSR. We proposed "Invigorating Qi and activating Blood" (IQAB) acupuncture, compared with Fenbid, and local electroacupuncture (LEA), to observe whether it has advantages in the protection of the CSR rat model and to elucidate its mechanism through the MAPK (mitogen-activated protein kinase) signaling pathway. Materials and Methods: Male Sprague-Dawley rats were randomly divided into six groups: control, sham, model, Fenbid, LEA, and IQAB. The CSR model was induced by inserting nylon sutures to compress the C4-T1 nerve root. The Fenbid group was treated with ibuprofen sustained-release capsules (15 mg/kg·d, ig). The LEA group received electroacupuncture at both C5 and C7 EX-B2 once a day. The IQAB group received acupuncture at both ST36 and BL17 based on the LEA group's intervention. Mechanical allodynia and gait, morphological changes in the spinal cord, IL-6 and TNF-α levels, MAPKs phosphorylation ratio, monocyte chemoattractant protein-1 (MCP-1) levels in the spinal cord, and the expression of p-p38 in the spinal cord and its colocalization with neurons and glial cell activation markers were detected. Results: Mechanical allodynia, gait disorder, edema, reduced Nissl-positive cell numbers, and increased IL-6 and TNF-α levels in the spinal cord were observed in CSR rats. IQAB significantly alleviated these changes, and the effects were generally comparable to those of Fenbid. Meanwhile, the phosphorylation ratios of p38 and extracellular regulated protein kinase (ERK), co-expression of p-p38 with neuron/microglia, and MCP-1 levels in the spinal cord were markedly down-regulated by IQAB compared with those in CSR model rats. Conclusion: IQAB reduced p38-activation-related microglia activation and MCP-1 levels, thus alleviating pathological changes, inflammation levels in the local spinal cord, and pain behavior of CSR.

P450Rdb: a manually curated database of reactions catalyzed by cytochrome P450 enzymes.

INTRODUCTION: Cytochrome P450 enzymes (P450s) are recognized as the most versatile catalysts worldwide, playing vital roles in numerous biological metabolism and biosynthesis processes across all kingdoms of life. Despite the vast number of P450 genes available in databases (over 300,000), only a small fraction of them (less than 0.2%) have undergone functional characterization. OBJECTIVES: To provide a convenient platform with abundant information on P450s and their corresponding reactions, we introduce the P450Rdb database, a manually curated resource compiles literature-supported reactions catalyzed by P450s. METHODS: All the P450s and Reactions were manually curated from the literature and known databases. Subsequently, the P450 reactions organized and categorized according to their chemical reaction type and site. The website was developed using HTML and PHP languages, with the MySQL server utilized for data storage. RESULTS: The current version of P450Rdb catalogs over 1,600 reactions, involving more than 590 P450s across a diverse range of over 200 species. Additionally, it offers a user-friendly interface with comprehensive information, enabling easy querying, browsing, and analysis of P450s and their corresponding reactions. P450Rdb is free available at http://www.cellknowledge.com.cn/p450rdb/. CONCLUSIONS: We believe that this database will significantly promote structural and functional research on P450s, thereby fostering advancements in the fields of natural product synthesis, pharmaceutical engineering, biotechnological applications, agricultural and crop improvement, and the chemical industry.

A Lightweight Network for Contextual and Morphological Awareness for Hepatic Vein Segmentation.

Accurate segmentation of the hepatic vein can improve the precision of liver disease diagnosis and treatment. Since the hepatic venous system is a small target and sparsely distributed, with various and diverse morphology, data labeling is difficult. Therefore, automatic hepatic vein segmentation is extremely challenging. We propose a lightweight contextual and morphological awareness network and design a novel morphology aware module based on attention mechanism and a 3D reconstruction module. The morphology aware module can obtain the slice similarity awareness mapping, which can enhance the continuous area of the hepatic veins in two adjacent slices through attention weighting. The 3D reconstruction module connects the 2D encoder and the 3D decoder to obtain the learning ability of 3D context with a very small amount of parameters. Compared with other SOTA methods, using the proposed method demonstrates an enhancement in the dice coefficient with few parameters on the two datasets. A small number of parameters can reduce hardware requirements and potentially have stronger generalization, which is an advantage in clinical deployment.

iPADD: A Computational Tool for Predicting Potential Antidiabetic Drugs Using Machine Learning Algorithms.

Diabetes mellitus is a chronic metabolic disease, which causes an imbalance in blood glucose homeostasis and further leads to severe complications. With the increasing population of diabetes, there is an urgent need to develop drugs to treat diabetes. The development of artificial intelligence provides a powerful tool for accelerating the discovery of antidiabetic drugs. This work aims to establish a predictor called iPADD for discovering potential antidiabetic drugs. In the predictor, we used four kinds of molecular fingerprints and their combinations to encode the drugs and then adopted minimum-redundancy-maximum-relevance (mRMR) combined with an incremental feature selection strategy to screen optimal features. Based on the optimal feature subset, eight machine learning algorithms were applied to train models by using 5-fold cross-validation. The best model could produce an accuracy (Acc) of 0.983 with the area under the receiver operating characteristic curve (auROC) value of 0.989 on an independent test set. To further validate the performance of iPADD, we selected 65 natural products for case analysis, including 13 natural products in clinical trials as positive samples and 52 natural products as negative samples. Except for abscisic acid, our model can give correct prediction results. Molecular docking illustrated that quercetin and resveratrol stably bound with the diabetes target NR1I2. These results are consistent with the model prediction results of iPADD, indicating that the machine learning model has a strong generalization ability. The source code of iPADD is available at https://github.com/llllxw/iPADD.

Multi-Lane Differential Variable Speed Limit Control via Deep Neural Networks Optimized by an Adaptive Evolutionary Strategy.

In advanced transportation-management systems, variable speed limits are a crucial application. Deep reinforcement learning methods have been shown to have superior performance in many applications, as they are an effective approach to learning environment dynamics for decision-making and control. However, they face two significant difficulties in traffic-control applications: reward engineering with delayed reward and brittle convergence properties with gradient descent. To address these challenges, evolutionary strategies are well suited as a class of black-box optimization techniques inspired by natural evolution. Additionally, the traditional deep reinforcement learning framework struggles to handle the delayed reward setting. This paper proposes a novel approach using covariance matrix adaptation evolution strategy (CMA-ES), a gradient-free global optimization method, to handle the task of multi-lane differential variable speed limit control. The proposed method uses a deep-learning-based method to dynamically learn optimal and distinct speed limits among lanes. The parameters of the neural network are sampled using a multivariate normal distribution, and the dependencies between the variables are represented by a covariance matrix that is optimized dynamically by CMA-ES based on the freeway's throughput. The proposed approach is tested on a freeway with simulated recurrent bottlenecks, and the experimental results show that it outperforms deep reinforcement learning-based approaches, traditional evolutionary search methods, and the no-control scenario. Our proposed method demonstrates a 23% improvement in average travel time and an average of a 4% improvement in CO, HC, and NOx emission.Furthermore, the proposed method produces explainable speed limits and has desirable generalization power.

MSU-Net: Multi-scale Sensitive U-Net based on pixel-edge-region level collaborative loss for nasopharyngeal MRI segmentation.

Radiotherapy is the traditional treatment of early nasopharyngeal carcinoma (NPC). Automatic accurate segmentation of risky lesions in the nasopharynx is crucial in radiotherapy. U-Net has been proved its effective medical image segmentation ability. However, the great difference in the structure and size of nasopharynx among different patients requires a network that pays more attention to multi-scale information. In this paper, we propose a multi-scale sensitive U-Net (MSU-Net) based on pixel-edge-region level collaborative loss (LCo-PER) for NPC segmentation task. A series of novel feature fusion modules based on spatial continuity and multi-scale semantic are proposed for extracting multi-level features while efficiently searching for all size lesions. A spatial continuity information extraction module (SCIEM) is proposed for effectively using the spatial continuity information of context slices to search small lesions. And a multi-scale semantic feature extraction module (MSFEM) is proposed for extracting features of different receptive fields. LCo-PER is proposed for the network training which makes network model could take into account the size of different lesions. The global Dice, Precision, Recall and IOU of the testing set are 84.50%, 97.48%, 84.33% and 82.41%, respectively. The results show that our method is better than the other state-of-the-art methods for NPC segmentation which obtain higher accuracy and effective segmentation performance.

Metabolic Anomaly Appearance Aware U-Net for Automatic Lymphoma Segmentation in Whole-Body PET/CT Scans.

Positron emission tomography-computed tomography (PET/CT) is an essential imaging instrument for lymphoma diagnosis and prognosis. PET/CT image based automatic lymphoma segmentation is increasingly used in the clinical community. U-Net-like deep learning methods have been widely used for PET/CT in this task. However, their performance is limited by the lack of sufficient annotated data, due to the existence of tumor heterogeneity. To address this issue, we propose an unsupervised image generation scheme to improve the performance of another independent supervised U-Net for lymphoma segmentation by capturing metabolic anomaly appearance (MAA). Firstly, we propose an anatomical-metabolic consistency generative adversarial network (AMC-GAN) as an auxiliary branch of U-Net. Specifically, AMC-GAN learns normal anatomical and metabolic information representations using co-aligned whole-body PET/CT scans. In the generator of AMC-GAN, we propose a complementary attention block to enhance the feature representation of low-intensity areas. Then, the trained AMC-GAN is used to reconstruct the corresponding pseudo-normal PET scans to capture MAAs. Finally, combined with the original PET/CT images, MAAs are used as the prior information for improving the performance of lymphoma segmentation. Experiments are conducted on a clinical dataset containing 191 normal subjects and 53 patients with lymphomas. The results demonstrate that the anatomical-metabolic consistency representations obtained from unlabeled paired PET/CT scans can be helpful for more accurate lymphoma segmentation, which suggest the potential of our approach to support physician diagnosis in practical clinical applications.

Janus Z-scheme heterostructure of ZnIn2S4/MoSe2/In2Se3 for efficient photocatalytic hydrogen evolution.

Artificial manipulation of charge separation and transfer are central issues dominating hydrogen evolution reaction triggered via photocatalysis. Herein, through elaborate designing on the architecture, band alignment, and interface bonding mode, a sulfur vacancy-rich ZnIn2S4-based (Vs-ZIS) multivariate heterostructure ZnIn2S4/MoSe2/In2Se3 (Vs-ZIS/MoSe2/In2Se3) with specific Janus Z-scheme charge transfer mechanism is constructed through a two-step hydrothermal process. Steering by the Janus Z-scheme charge transfer mechanism, photogenerated electrons in the conduction band of MoSe2 transfer synchronously to the valence band of Vs-ZIS and In2Se3, resulting in abundant highly-active photogenerated electrons reserved in the conduction band of Vs-ZIS and In2Se3, therefore significantly enhancing the photocatalytic activity of hydrogen evolution. Under visible light irradiation, the optimized Vs-ZIS/MoSe2/In2Se3 with the mass ratio of MoSe2 and In2Se3 to ZnIn2S4 at 3 % and 30 %, respectively, performs a high hydrogen evolution rate of 124.42 mmol·g-1·h-1, about 43.5-folds of the original ZIS photocatalyst. Besides, an apparent quantum efficiency (AQE) of 22.5 % at 420 nm and favorable durability are also achieved over Vs-ZIS/MoSe2/In2Se3 photocatalyst. This work represents an important development in efficient photocatalysts and donates a sound foundation for the design of regulating charge transfer pathways.

Self-assembled nanoparticles: A new platform for revolutionizing therapeutic cancer vaccines.

Cancer vaccines have had some success in the past decade. Based on in-depth analysis of tumor antigen genomics, many therapeutic vaccines have already entered clinical trials for multiple cancers, including melanoma, lung cancer, and head and neck squamous cell carcinoma, which have demonstrated impressive tumor immunogenicity and antitumor activity. Recently, vaccines based on self-assembled nanoparticles are being actively developed as cancer treatment, and their feasibility has been confirmed in both mice and humans. In this review, we summarize recent therapeutic cancer vaccines based on self-assembled nanoparticles. We describe the basic ingredients for self-assembled nanoparticles, and how they enhance vaccine immunogenicity. We also discuss the novel design method for self-assembled nanoparticles that pose as a promising delivery platform for cancer vaccines, and the potential in combination with multiple therapeutic approaches.

A multi-feature fusion decoding study for unilateral upper-limb fine motor imagery.

To address the fact that the classical motor imagination paradigm has no noticeable effect on the rehabilitation training of upper limbs in patients after stroke and the corresponding feature extraction algorithm is limited to a single domain, this paper describes the design of a unilateral upper-limb fine motor imagination paradigm and the collection of data from 20 healthy people. It presents a feature extraction algorithm for multi-domain fusion and compares the common spatial pattern (CSP), improved multiscale permutation entropy (IMPE) and multi-domain fusion features of all participants through the use of decision tree, linear discriminant analysis, naive Bayes, a support vector machine, k-nearest neighbor and ensemble classification precision algorithms in the ensemble classifier. For the same subject, the average classification accuracy improvement of the same classifier for multi-domain feature extraction relative to CSP feature results went up by 1.52%. The average classification accuracy improvement of the same classifier went up by 32.87% relative to the IMPE feature classification results. This study's unilateral fine motor imagery paradigm and multi-domain feature fusion algorithm provide new ideas for upper limb rehabilitation after stroke.