Discovery of novel ocotillol derivatives modulating glucocorticoid receptor/NF-κB signaling for the treatment of sepsis.

Glucocorticoids (GCs) have been used in the treatment of sepsis because of their potent anti-inflammatory effects. However, their clinical efficacy against sepsis remains controversial because of glucocorticoid receptor (GR) downregulation and side effects. Herein, we designed and synthesized 30 ocotillol derivatives and evaluated their anti-inflammatory activities. Ocotillol 24(R/S) differential isomers were stereoselective in their pharmacological action. Specifically, 24(S) derivatives had better anti-inflammatory activity than their corresponding 24(R) derivatives. Compound 20 most effectively inhibited NO release (85.97% reduction), and it exerted dose-dependent inhibitory effects on interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α) levels. Mechanistic studies revealed that compound 20 reduces the degradation of GR mRNA and GR protein. Meanwhile, compound 20 inhibited the activation of nuclear factor-κB (NF-κB) signaling, thereby inhibiting the nuclear translocation of p65 and attenuating the inflammatory response. In vivo studies revealed that compound 20 attenuated hepatic, pulmonary, and renal pathology damage in mice with sepsis and suppressed the production of inflammatory mediators. These results indicated that compound 20 is a promising lead compound for designing and developing anti-sepsis drugs.

Personalized differential expression analysis in triple-negative breast cancer.

Identification of individual-level differentially expressed genes (DEGs) is a pre-step for the analysis of disease-specific biological mechanisms and precision medicine. Previous algorithms cannot balance accuracy and sufficient statistical power. Herein, RankCompV2, designed for identifying population-level DEGs based on relative expression orderings, was adjusted to identify individual-level DEGs. Furthermore, an optimized version of individual-level RankCompV2, named as RankCompV2.1, was designed based on the assumption that the rank positions of genes and relative rank differences of gene pairs would influence the identification of individual-level DEGs. In comparison to other individualized analysis algorithms, RankCompV2.1 performed better on statistical power, computational efficiency, and acquired coequal accuracy in both simulation and real paired cancer-normal data from ten cancer types. Besides, single sample GSEA and Gene Set Variation Analysis analysis showed that pathways enriched with up-regulated and down-regulated genes presented higher and lower enrichment scores, respectively. Furthermore, we identified 16 genes that were universally deregulated in 966 triple-negative breast cancer (TNBC) samples and interacted with Food and Drug Administration (FDA)-approved drugs or antineoplastic agents, indicating notable therapeutic targets for TNBC. In addition, we also identified genes with highly variable deregulation status and used these genes to cluster TNBC samples into three subgroups with different prognoses. The subgroup with the poorest outcome was characterized by down-regulated immune-regulated pathways, signal transduction pathways, and apoptosis-related pathways. Protein-protein interaction network analysis revealed that OAS family genes may be promising drug targets to activate tumor immunity in this subgroup. In conclusion, RankCompV2.1 is capable of identifying individual-level DEGs with high accuracy and statistical power, analyzing mechanisms of carcinogenesis and exploring therapeutic strategy.

Therapeutic potential of targeting polo-like kinase 4.

Polo-like kinase 4 (PLK4), a highly conserved serine/threonine kinase, masterfully regulates centriole duplication in a spatiotemporal manner to ensure the fidelity of centrosome duplication and proper mitosis. Abnormal expression of PLK4 contributes to genomic instability and associates with a poor prognosis in cancer. Inhibition of PLK4 is demonstrated to exhibit significant efficacy against various types of human cancers, further highlighting its potential as a promising therapeutic target for cancer treatment. As such, numerous small-molecule inhibitors with distinct chemical scaffolds targeting PLK4 have been extensively investigated for the treatment of different human cancers, with several undergoing clinical evaluation (e.g., CFI-400945). Here, we review the structure, distribution, and biological functions of PLK4, encapsulate its intricate regulatory mechanisms of expression, and highlighting its multifaceted roles in cancer development and metastasis. Moreover, the recent advancements of PLK4 inhibitors in patent or literature are summarized, and their therapeutic potential as monotherapies or combination therapies with other anticancer agents are also discussed.

Balanced influence maximization in social networks based on deep reinforcement learning.

Balanced influence maximization aims to balance the influence maximization of multiple different entities in social networks and avoid the emergence of filter bubbles and echo chambers. Recently, an increasing number of studies have drawn attention to the study of balanced influence maximization in social networks and achieves success to some extent. However, most of them still have two major shortcomings. First, the previous works mainly focus on spreading the influence of multiple target entities to more users, ignoring the potential influence of the correlation between the target entities and other entities on information propagation in real social networks. Second, the existing methods require a large amount of diffusion sampling for influence estimation, making it difficult to apply to large social networks. To this end, we propose a Balanced Influence Maximization framework based on Deep Reinforcement Learning named BIM-DRL, which consists of two core components: an entity correlation evaluation module and a balanced seed node selection module. Specifically, in the entity correlation evaluation module, an entity correlation evaluation model based on the users' historical behavior sequences is proposed, which can accurately evaluate the impact of entity correlation on information propagation. In the balanced seed node selection module, a balanced influence maximization model based on deep reinforcement learning is designed to train the parameters in the objective function, and then a set of seed nodes that maximize the balanced influence is found. Extensive experiments on six real-life network datasets demonstrate the superiority of the BIM-DRL over state-of-the-art methods on the metrics of balanced influence spread and balanced propagation accuracy.

A lightweight intrusion detection method for IoT based on deep learning and dynamic quantization.

Intrusion detection ensures that IoT can protect itself against malicious intrusions in extensive and intricate network traffic data. In recent years, deep learning has been extensively and effectively employed in IoT intrusion detection. However, the limited computing power and storage space of IoT devices restrict the feasibility of deploying resource-intensive intrusion detection systems on them. This article introduces the DL-BiLSTM lightweight IoT intrusion detection model. By combining deep neural networks (DNNs) and bidirectional long short-term memory networks (BiLSTMs), the model enables nonlinear and bidirectional long-distance feature extraction of complex network information. This capability allows the system to capture complex patterns and behaviors related to cyber-attacks, thus enhancing detection performance. To address the resource constraints of IoT devices, the model utilizes the incremental principal component analysis (IPCA) algorithm for feature dimensionality reduction. Additionally, dynamic quantization is employed to trim the specified cell structure of the model, thereby reducing the computational burden on IoT devices while preserving accurate detection capability. The experimental results on the benchmark datasets CIC IDS2017, N-BaIoT, and CICIoT2023 demonstrate that DL-BiLSTM surpasses traditional deep learning models and cutting-edge detection techniques in terms of detection performance, while maintaining a lower model complexity.

Using DeepContact with Amira graphical user interface.

DeepContact is a deep learning software for high-throughput quantification of membrane contact site (MCS) in 2D electron microscopy images. This protocol will guide users through incorporating available DeepContact models in Amira's artificial intelligence module, thereby allowing invoking of DeepContact functions in organelle segmentation and quantifying of MCS with a user-friendly graphical user interface of Amira software. For complete details on the use and execution of this protocol, please refer to Liu et al. (2022).1.

Radiology report generation with a learned knowledge base and multi-modal alignment.

In clinics, a radiology report is crucial for guiding a patient's treatment. However, writing radiology reports is a heavy burden for radiologists. To this end, we present an automatic, multi-modal approach for report generation from a chest x-ray. Our approach, motivated by the observation that the descriptions in radiology reports are highly correlated with specific information of the x-ray images, features two distinct modules: (i) Learned knowledge base: To absorb the knowledge embedded in the radiology reports, we build a knowledge base that can automatically distill and restore medical knowledge from textual embedding without manual labor; (ii) Multi-modal alignment: to promote the semantic alignment among reports, disease labels, and images, we explicitly utilize textual embedding to guide the learning of the visual feature space. We evaluate the performance of the proposed model using metrics from both natural language generation and clinic efficacy on the public IU-Xray and MIMIC-CXR datasets. Our ablation study shows that each module contributes to improving the quality of generated reports. Furthermore, the assistance of both modules, our approach outperforms state-of-the-art methods over almost all the metrics. Code is available at https://github.com/LX-doctorAI1/M2KT.

Hsa_circ_0063329 inhibits prostate cancer growth and metastasis by modulating the miR-605-5p/tgif2 axis.

Circular RNAs play crucial regulatory roles in the progression of various cancers. Nevertheless, the underlying mechanisms of circRNAs in prostate cancer (PCa) proliferation and metastasis remain largely uncertain. Here, we performed circRNA microarray analyses to identify differentially expressed circRNAs in a normal prostate epithelial cell line and PCa cell lines. We found that hsa_circ_0063329 was significantly downregulated in PCa. A series of in vitro and in vivo functional assays showed that overexpression of hsa_circ_0063329 inhibits PCa cell progression, while silencing of hsa_circ_0063329 achieved the opposite effects. Mechanistically, bioinformatics analysis, RNA pulldown, RNA-seq and dual-luciferase reporter assays demonstrated that hsa_circ_0063329 exerts its effect by sponging miR-605-5p to derepress TG-interacting factor 2 (TGIF2) and inactivate the TGF-ß pathway. In conclusion, hsa_circ_0063329 inhibits the proliferation and metastasis of PCa via modulation of the miR-605-5p/TGIF2 axis, and targeting hsa_circ_0063329 may provide a promising treatment strategy for aggressive PCa.

An immuno-score signature of tumor immune microenvironment predicts clinical outcomes in locally advanced rectal cancer.

Background and purpose: Accumulating evidence indicates that neoadjuvant chemoradiotherapy(nCRT) success has an immune-associated constituent in locally advanced rectal cancer (LARC). The immune-associated configuration of the tumor microenvironment associated with responses to treatment was explored in LARC in this study. Material and methods: A novel analytic framework was developed based on within-sample relative expression orderings for identifying tumor immune-associated gene pairs and identified an immuno-score signature from bulk transcriptome profiling analysis of 200 LARC patients. And sequencing and microarray analysis of gene expression was conducted to investigate the association between the signature and response to nCRT, immunotherapy, and cell function of CD4 and CD8. The results were validated using 111 pretreated samples from publicly available datasets in multiple aspects and survival analyses. Results: The immuno-score signature of 18 immune-related gene pairs (referred to as IPS) was validated on bulk microarray and RNA-Seq data. According to the model's immune score, LARC patients were divided into high- and low-score groups. The patients with high-score were greater sensitivity to nCRT and immunotherapy, gaining a significantly improved prognosis. In addition, the immune-score gene pair signature was associated with type I anti-tumor T cell responses, positive regulators of T cell functions, and chromosomal instability while reflecting differences between CD8+ T cell subtypes. Conclusion: The immuno-score signature underlines a key role of tumor immune components in nCRT response, and predicts the prognosis of LARC patients as well.

Flexible joint parameters identification method based on improved tracking differentiator and adaptive differential evolution.

The assembly error of flexible joints and the change in joint stiffness during movement make the actual value of joint parameters inconsistent with the given value, which affects the joint control accuracy. In order to suppress the influence of parameters error, a parameters identification method for flexible joint combined offline identification and online compensation is proposed. First, the offline identification model of inertia, mass, and damping and the online identification model of joint stiffness are established, respectively. Then, a hybrid tracking differentiator based on an improved Sigmoid function is designed to track the differential signals of joint motion parameters, and the Lyapunov function is designed to prove its convergence. The adaptive differential evolution is used as the identification algorithm, and the improved adaptive crossover, mutation factor, and Metropolis acceptance criterion are designed to improve the convergence speed. Finally, a feedforward control structure based on identification is designed to compensate for the model deviation. Simulation and experimental results show that the improved differentiator can effectively improve the tracking speed and derivation accuracy of the signals. Compared with other algorithms, the proposed identification method has a faster convergence speed and higher identification accuracy, and feedforward compensation control can effectively correct model parameters and improve control accuracy.

DeepContact: High-throughput quantification of membrane contact sites based on electron microscopy imaging.

Membrane contact site (MCS)-mediated organelle interactions play essential roles in the cell. Quantitative analysis of MCSs reveals vital clues for cellular responses under various physiological and pathological conditions. However, an efficient tool is lacking. Here, we developed DeepContact, a deep-learning protocol for optimizing organelle segmentation and contact analysis based on label-free EM. DeepContact presents high efficiency and flexibility in interactive visualizations, accommodating new morphologies of organelles and recognizing contacts in versatile width ranges, which enables statistical analysis of various types of MCSs in multiple systems. DeepContact profiled previously unidentified coordinative rearrangements of MCS types in cultured cells with combined nutritional conditions. DeepContact also unveiled a subtle wave of ER-mitochondrial entanglement in Sertoli cells during the seminiferous epithelial cycle, indicating its potential in bridging MCS dynamics to physiological and pathological processes.

Knowledge matters: Chest radiology report generation with general and specific knowledge.

Automatic chest radiology report generation is critical in clinics which can relieve experienced radiologists from the heavy workload and remind inexperienced radiologists of misdiagnosis or missed diagnose. Existing approaches mainly formulate chest radiology report generation as an image captioning task and adopt the encoder-decoder framework. However, in the medical domain, such pure data-driven approaches suffer from the following problems: 1) visual and textual bias problem; 2) lack of expert knowledge. In this paper, we propose a knowledge-enhanced radiology report generation approach introduces two types of medical knowledge: 1) General knowledge, which is input independent and provides the broad knowledge for report generation; 2) Specific knowledge, which is input dependent and provides the fine-grained knowledge for chest X-ray report generation. To fully utilize both the general and specific knowledge, we also propose a knowledge-enhanced multi-head attention mechanism. By merging the visual features of the radiology image with general knowledge and specific knowledge, the proposed model can improve the quality of generated reports. The experimental results on the publicly available IU-Xray dataset show that the proposed knowledge-enhanced approach outperforms state-of-the-art methods in almost all metrics. And the results of MIMIC-CXR dataset show that the proposed knowledge-enhanced approach is on par with state-of-the-art methods. Ablation studies also demonstrate that both general and specific knowledge can help to improve the performance of chest radiology report generation.

COVID-19 inhibits spermatogenesis in the testes by inducing cellular senescence.

Introduction: COVID-19 (SARS-CoV-2) has been linked to organ damage in humans since its worldwide outbreak. It can also induce severe sperm damage, according to research conducted at numerous clinical institutions. However, the exact mechanism of damage is still unknown. Methods: In this study, testicular bulk-RNA-seq Data were downloaded from three COVID-19 patients and three uninfected controls from GEO to evaluate the effect of COVID-19 infection on spermatogenesis. Relative expression of each pathway and the correlation between genes or pathways were analyzed by bioinformatic methods. Results: By detecting the relative expression of each pathway and the correlation between genes or pathways, we found that COVID-19 could induce testicular cell senescence through MAPK signaling pathway. Cellular senescence was synergistic with MAPK pathway, which further affected the normal synthesis of cholesterol and androgen, inhibited the normal synthesis of lactate and pyruvate, and ultimately affected spermatogenesis. The medications targeting MAPK signaling pathway, especially MAPK1 and MAPK14, are expected to be effective therapeutic medications for reducing COVID-19 damage to spermatogenesis. Conclusion: These results give us a new understanding of how COVID-19 inhibits spermatogenesis and provide a possible solution to alleviate this damage.

Photoaffinity-Based Chemical Proteomics Reveals 7-Oxocallitrisic Acid Targets CPT1A to Trigger Lipogenesis Inhibition.

One of the natural terpenoids isolated from Resina Commiphora, 7-oxocallitrisic acid (7-OCA), has lipid metabolism regulatory activity. To uncover its lipogenesis inhibition mechanism, we developed a photoaffinity and clickable probe based on the 7-OCA scaffold and performed chemical proteomics to profile its potential cellular targets. It was found that 7-OCA could directly interact with carnitine palmitoyl transferase 1A (CPT1A) to promote its activity to reduce lipid accumulation. The present work reveals our understanding of the mode of lipid mebabolism regulation by abietic acids and provides new clues for antiobesity drug development with CPT1A as a main target.

A 41-Gene Pair Signature for Predicting the Pathological Response of Locally Advanced Rectal Cancer to Neoadjuvant Chemoradiation.

Background and Purpose: Pathological response status is a standard reference for the early evaluation of the effect of neoadjuvant chemoradiation (nCRT) on locally advanced rectal cancer (LARC) patients. Various patients respond differently to nCRT, but identifying the pathological response of LARC to nCRT remains a challenge. Therefore, we aimed to identify a signature that can predict the response of LARC to nCRT. Material and Methods: The gene expression profiles of 111 LARC patients receiving fluorouracil-based nCRT were used to obtain gene pairs with within-sample relative expression orderings related to pathological response. These reversal gene pairs were ranked according to the mean decrease Gini index provided by the random forest algorithm to obtain the signature. This signature was verified in two public cohorts of 46 and 42 samples, and a cohort of 33 samples measured at our laboratory. In addition, the signature was used to predict disease-free survival benefits in a series of colorectal cancer datasets. Results: A 41-gene pair signature (41-GPS) was identified in the training cohort with an accuracy of 84.68% and an area under the receiver operating characteristic curve (AUC) of 0.94. In the two public test cohorts, the accuracy was 93.37 and 73.81%, with AUCs of 0.97 and 0.86, respectively. In our dataset, the AUC was 0.80. The results of the survival analysis show that 41-GPS plays an effective role in identifying patients who will respond to nCRT and have a better prognosis. Conclusion: The signature consisting of 41 gene pairs can robustly predict the clinical pathological response of LARC patients to nCRT.

Tumor Temporal Proteome Profiling Reveals the Immunological Triple Offensive Induced by Synthetic Anti-Cancer Salmonella.

The engineered "obligate" anaerobic Salmonella typhimurium strain YB1 shows a prominent ability to repress tumor growth and metastasis, which has great potential as a novel cancer immunotherapy. However, the antitumor mechanism of YB1 remains unelucidated. To resolve the proteome dynamics induced by the engineered bacteria, we applied tumor temporal proteome profiling on murine bladder tumors after intravenous injection of either YB1 or PBS as a negative control. Our data suggests that during the two weeks treatment of YB1 injections, the cured tumors experienced three distinct phases of the immune response. Two days after injection, the innate immune response was activated, particularly the complement and blood coagulation pathways. In the meantime, the phagocytosis was initiated. The professional phagocytes such as macrophages and neutrophils were recruited, especially the infiltration of iNOS+ and CD68+ cells was enhanced. Seven days after injection, substantial amount of T cells was observed at the invasion margin of the tumor. As a result, the tumor shrunk significantly. Overall, the temporal proteome profiling can systematically reveal the YB1 induced immune responses in tumor, showing great promise for elucidating the mechanism of bacteria-mediated cancer immunotherapy.

A novel immune-related gene-based prognostic signature to predict biochemical recurrence in patients with prostate cancer after radical prostatectomy.

Accumulating evidences indicates that the immune landscape signature dramatically correlates with tumorigenesis and prognosis of prostate cancer (PCa). Here, we identified a novel immune-related gene-based prognostic signature (IRGPS) to predict biochemical recurrence (BCR) after radical prostatectomy. We also explored the correlation between IRGPS and tumor microenvironment. We identified an IRGPS consisting of seven immune-related genes (PPARGC1A, AKR1C2, COMP, EEF1A2, IRF5, NTM, and TPX2) that were related to the BCR-free survival of PCa patients. The high-risk patients exhibited a higher fraction of regulatory T cells and M2 macrophages than the low-risk BCR patients (P < 0.05) as well as a lower fraction of resting memory CD4 T cells and resting mast cells. These high-risk patients also had higher expression levels of CTLA4, TIGIT, PDCD1, LAG3, and TIM3. Finally, a strong correlation was detected between IRGPS and specific clinicopathological features, including Gleason scores and tumor stage. In conclusion, our study reveals the clinical significance and potential functions of the IRGPS, provides more data for predicting outcomes, and suggests more effective immunotherapeutic target strategies for PCa.

Functional Assessment of Four Novel Immune-Related Biomarkers in the Pathogenesis of Clear Cell Renal Cell Carcinoma.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma whose pathogenesis is not well understood. We aimed at identifying novel immune-related biomarkers that could be valuable in the diagnosis and prognosis of ccRCC. METHODS: The Robust Rank Aggregation (RRA) method was used to integrate differently expressed genes (DEGs) of 7 Gene Expression Omnibus (GEO) datasets and obtain robust DEGs. Weighted gene co-expression network analyses (WGCNA) were performed to identify hub genes associated with clinical traits in The Cancer Genome Atlas (TCGA) database. Comprehensive bioinformatic analyses were used to explore the role of hub genes in ccRCC. RESULTS: Four hub genes IFI16, LMNB1, RHBDF2 and TACC3 were screened by the RRA method and WGCNA. These genes were found to be up-regulated in ccRCC, an upregulation that could be due to their associations with late TNM stages and tumor grades. The Receiver Operating Characteristic (ROC) curve and Kaplan-Meier survival analysis showed that the four hub genes had great diagnostic and prognostic values for ccRCC, while Gene Set Enrichment Analysis (GSEA) showed that they were involved in immune signaling pathways. They were also found to be closely associated with multiple tumor-infiltrating lymphocytes and critical immune checkpoint expressions. The results of Quantitative Real-time PCR (qRT-PCR) and immunohistochemical staining (IHC) analysis were consistent with bioinformatics analysis results. CONCLUSION: The four hub genes were shown to have great diagnostic and prognostic values and played key roles in the tumor microenvironment of ccRCC.

Carbon nanotubes promote alveolar macrophages toward M2 polarization mediated epithelial-mesenchymal transition and fibroblast-to-myofibroblast transdifferentiation.

With substantial progress of nanotechnology, carbon nanotubes (CNTs) are widely used in a variety of industrial and commercial applications. There is rising concern about potential adverse health effects, such as pulmonary fibrosis, related to inhalation of CNTs. The detailed cellular and molecular mechanisms of pulmonary fibrosis induced by CNTs are still not clear. Epithelial-mesenchymal transition (EMT) and fibroblast-to-myofibroblast transdifferentiation (FMT) are considered as critical events in pathogenesis of pulmonary fibrosis. Alveolar macrophages (AMs) polarization plays a key role of regulating EMT and FMT in pulmonary fibrosis. In this study, we applied CNTs to stimulate primary mouse AMs under M1 or M2 polarization conditions, then analyzed the proportion of F4/80+CD11c+ or F4/80+CD206+ AMs, mRNA expression and activities of iNOS or Arg-1, as well as mRNA expression and content of TNF-α and IL-6 or TGF-ß and IL-10 to evaluate dynamic phenotypic and functional changes of AMs. Single-walled CNT (SWCNT), short-type multi-walled CNT (MWCNT), and long-type MWCNT exposure at dose of 50 µg/ml promote AMs polarization toward M1 phenotype at early stage, while promote AMs polarization toward M2 phenotype at late stage. The roles of AMs polarization during development of EMT and FMT were further investigated by conditioned medium (CM) experiments. CNTs-activated M2 AMs promote progression of EMT and FMT via secreting TGF-ß. Furthermore, up-regulating IRF4 may be involved in CNTs-induced M2 AMs polarization. In conclusion, this study demonstrates a new insight that CNTs exposure promotes AMs polarization toward M2 phenotype which facilitate EMT and FMT through secreting TGF-ß.