TRIM21/USP15 balances ACSL4 stability and the imatinib resistance of gastrointestinal stromal tumors.

BACKGROUND: Imatinib has become an exceptionally effective targeted drug for treating gastrointestinal stromal tumors (GISTs). Despite its efficacy, the resistance to imatinib is common in GIST patients, posing a significant challenge to the effective treatment. METHODS: The expression profiling of TRIM21, USP15, and ACSL4 in GIST patients was evaluated using Western blot and immunohistochemistry. To silence gene expression, shRNA was utilized. Biological function of TRIM21, USP15, and ACSL4 was examined through various methods, including resistance index calculation, colony formation, shRNA interference, and xenograft mouse model. The molecular mechanism of TRIM21 and USP15 in GIST was determined by conducting Western blot, co-immunoprecipitation, and quantitative real-time PCR (qPCR) analyses. RESULTS: Here we demonstrated that downregulation of ACSL4 is associated with imatinib (IM) resistance in GIST. Moreover, clinical data showed that higher levels of ACSL4 expression are positively correlated with favorable clinical outcomes. Mechanistic investigations further indicated that the reduced expression of ACSL4 in GIST is attributed to excessive protein degradation mediated by the E3 ligase TRIM21 and the deubiquitinase USP15. CONCLUSION: These findings demonstrate that the TRIM21 and USP15 control ACSL4 stability to maintain the IM sensitive/resistant status of GIST.

Tumor size, but not consolidation-to-tumor ratio, is an independent prognostic factor for part-solid clinical T1 non-small cell lung cancer.

BACKGROUND: Tumor size and consolidation-to-tumor ratio (CTR) are crucial for non-small cell lung cancer (NSCLC) prognosis. However, the optimal CTR cutoff remains unclear. Whether tumor size and CTR are independent prognostic factors for part-solid NSCLC is under debate. Here, we aimed to evaluate the prognostic impacts of CTR and tumor size on NSCLC, especially on part-solid NSCLC. METHODS: We reviewed 1366 clinical T1 NSCLC patients who underwent surgical treatment. Log-rank test and Cox regression analyses were adopted for prognostic evaluation. The "surv_cutpoint" function was used to identify the optimal CTR and tumor size cutoff values. RESULTS: There were 416, 510, and 440 subjects with pure ground-glass opacity (pGGO), part-solid, and pure solid nodules. The 5-year overall survival (disease-free survival) for patients with pGGO, part-solid, and pure solid nodules were 99.5% (99.5%), 97.3% (95.8%), and 90.4% (78.9%), respectively. Multivariate Cox regression analysis indicated that CTR was an independent prognostic factor for the whole patients, and the optimal CTR cutoff was 0.99. However, for part-solid NSCLC, CTR was not independently associated with survival, even if categorized by the optimal cutoffs. The predicted optimal cutoffs of total tumor size and solid component size were 2.4 and 1.4 cm for part-solid NSCLC. Total tumor size (HR = 6.21, 95% CI: 1.58-24.34, p = 0.009) and solid component size (HR = 2.27, 95% CI: 1.04-5.92, p = 0.045) grouped by the cutoffs were significantly associated with part-solid NSCLC prognosis. CONCLUSIONS: CTR was an independent prognostic factor for the whole NSCLC, but not for the part-solid NSCLC. Tumor size was still meaningful for part-solid NSCLC.

Metagenome-assembled genome extraction and analysis from microbiomes using KBase.

Uncultivated Bacteria and Archaea account for the vast majority of species on Earth, but obtaining their genomes directly from the environment, using shotgun sequencing, has only become possible recently. To realize the hope of capturing Earth's microbial genetic complement and to facilitate the investigation of the functional roles of specific lineages in a given ecosystem, technologies that accelerate the recovery of high-quality genomes are necessary. We present a series of analysis steps and data products for the extraction of high-quality metagenome-assembled genomes (MAGs) from microbiomes using the U.S. Department of Energy Systems Biology Knowledgebase (KBase) platform ( http://www.kbase.us/ ). Overall, these steps take about a day to obtain extracted genomes when starting from smaller environmental shotgun read libraries, or up to about a week from larger libraries. In KBase, the process is end-to-end, allowing a user to go from the initial sequencing reads all the way through to MAGs, which can then be analyzed with other KBase capabilities such as phylogenetic placement, functional assignment, metabolic modeling, pangenome functional profiling, RNA-Seq and others. While portions of such capabilities are available individually from other resources, the combination of the intuitive usability, data interoperability and integration of tools in a freely available computational resource makes KBase a powerful platform for obtaining MAGs from microbiomes. While this workflow offers tools for each of the key steps in the genome extraction process, it also provides a scaffold that can be easily extended with additional MAG recovery and analysis tools, via the KBase software development kit (SDK).

Frame-Level Teacher-Student Learning With Data Privacy for EEG Emotion Recognition.

Recently, electroencephalogram (EEG) emotion recognition has gradually attracted a lot of attention. This brief designs a novel frame-level teacher-student framework with data privacy (FLTSDP) for EEG emotion recognition. The framework first proposes a teacher-student network without prior professional information for automated filtering of useful frame-level features by a gated mechanism and extracting high-level features by using knowledge distillation to capture the results of EEG emotion recognition from a teacher network and student networks. Then, the results from subnetworks are integrated by using the novel decision module, which, motivated by the voting mechanism, adjusts the composition of feature vectors and improves the weight of accurate prediction to optimize the integration effect. During training, an innovative data privacy protection mechanism is applied for avoiding data sharing, where each student network only inherits weights from all trained networks and does not inherit the training dataset. Here, the framework can be repeatedly optimized and improved by only training the next student subnetwork on new EEG signals. Experimental results show that our framework improves the accuracy of EEG emotion recognition by more than 5% and gets state-of-the-art performance for EEG emotion recognition in the subject-independent mode.

Multilabel Convolutional Network With Feature Denoising and Details Supplement.

In multilabel images, the changeable size, posture, and position of objects in the image will increase the difficulty of classification. Moreover, a large amount of irrelevant information interferes with the recognition of objects. Therefore, how to remove irrelevant information from the image to improve the performance of label recognition is an important problem. In this article, we propose a convolutional network based on feature denoising and details supplement (FDDS) to address this issue. In FDDS, we first design a cascade convolution module (CCM) to collect spatial details of upper features, in order to enhance the information expression of features. Second, the feature denoising module (FDM) is further put forward to reallocate the weight of the feature semantic area, in order to enrich the effective semantic information of the current feature and perform denoising operations on object-irrelevant information. Experimental results show that the proposed FDDS outperforms the existing state-of-the-art models on several benchmark datasets, especially for complex scenes.

Clinical effect of Xuebijing combined with thymosinα1 on patients with severe pneumonia complicated with sepsis and its effect on serum inflammatory factors.

This study aimed to research the clinical effect of Xuebijing combined with thymosin α1 on patients with severe pneumonia complicated with sepsis, and its effect on serum inflammatory factors. For this purpose, 81 cases of severe pneumonia complicated with sepsis were collected. All patients were given early treatments. 41 cases who received Xuebijing injection by intravenous drip were selected as the control group. 40 cases who were treated through subcutaneous injection of thymosin α1 based on Xuebijing injection by intravenous drip were selected as the study group. The body temperature, respiration, heart rate, leukocytes, other general conditions, blood gas indexes, serum IL-6, TNF-α and CRP levels, bacterial clearance rate and therapy effect were recorded and compared before and after treatment. Results showed that after treatment, the body temperature, respiration, heart rate, leukocytes and other general conditions of the study group were lower than those in the control group (all p<0.05). The blood gas indexes pH and PaCO2 levels of the study group were lower than those of the control group. The levels of serum interleukin-6 (IL-6), serum tumor necrosis factor α (TNF-α) and C-reactive protein (CRP) in the study group were lower than those in the control group (all p<0.05). The bacterial clearance rate of the study group was lower than that of the control group (all p<0.05). The total effective rate of treatment of patients in the study group was higher than that of patients in the control group (all p<0.05). In general, Thymosin α1 and Xuebijing injection can improve the therapy effect of severe pneumonia complicated with sepsis, improve the hemorheology condition of patients, effectively remove bacteria and reduce the expression level of serum CRP, TNF-α, IL-6, IL-8 and other inflammatory factors in patients, which is worthy of clinical promotion.

FLDNet: Frame-Level Distilling Neural Network for EEG Emotion Recognition.

Based on the current research on EEG emotion recognition, there are some limitations, such as hand-engineered features, redundant and meaningless signal frames and the loss of frame-to-frame correlation. In this paper, a novel deep learning framework is proposed, named the frame-level distilling neural network (FLDNet), for learning distilled features from the correlations of different frames. A layer named the frame gate is designed to integrate weighted semantic information on multiple frames to remove redundant and meaningless signal frames. A triple-net structure is introduced to distill the learned features net by net to replace the hand-engineered features with professional knowledge. Specifically, one neural network is normally trained for several epochs. Then, a second network of the same structure will be initialized again to learn the extracted features from the frame gate of the first neural network based on the output of the first net. Similarly, the third net improves the features based on the frame gate of the second network. To utilize the representation ability of the triple neural network, an ensemble layer is conducted to integrate the discriminative ability of the proposed framework for final decisions. Consequently, the proposed FLDNet provides an effective method for capturing the correlation between different frames and automatically learn distilled high-level features for emotion recognition. The experiments are carried out in a subject-independent emotion recognition task on public emotion datasets of DEAP and DREAMER benchmarks, which have demonstrated the effectiveness and robustness of the proposed FLDNet.