FOXA1 regulates ribosomal RNA transcription in prostate cancer.

BACKGROUND: Ribosome biogenesis is excessively activated in tumor cells, yet it is little known whether oncogenic transcription factors (TFs) are involved in the ribosomal RNA (rRNA) transactivation. METHODS: Nucleolar proteomics data and large-scale immunofluorescence were re-analyzed to jointly identify the proteins localized at nucleolus. RNA-Seq data of five prostate cancer (PCa) cohorts were combined and integrated with multi-dimensional data to define the upregulated nucleolar TFs in PCa tissues. Then, ChIP-Seq data of PCa cell lines and two PCa clinical cohorts were re-analyzed to reveal the TF binding patterns at ribosomal DNA (rDNA) repeats. The TF binding at rDNA was validated by ChIP-qPCR. The effect of the TF on rRNA transcription was determined by rDNA luciferase reporter, nascent RNA synthesis, and global protein translation assays. RESULTS: In this study, we reveal the role of oncogenic TF FOXA1 in regulating rRNA transcription within nucleolar organization regions. By analyzing human TFs in prostate cancer clinical datasets and nucleolar proteomics data, we identified that FOXA1 is partially localized in the nucleolus and correlated with global protein translation. Our extensive FOXA1 ChIP-Seq analysis provides robust evidence of FOXA1 binding across rDNA repeats in prostate cancer cell lines, primary tumors, and castration-resistant variants. Notably, FOXA1 occupancy at rDNA repeats correlates with histone modifications associated with active transcription, namely H3K27ac and H3K4me3. Reducing FOXA1 expression results in decreased transactivation at rDNA, subsequently diminishing global protein synthesis. CONCLUSIONS: Our results suggest FOXA1 regulates aberrant ribosome biogenesis downstream of oncogenic signaling in prostate cancer.

The relationship between cumulative ecological risk and health risk behaviors among Chinese adolescents.

OBJECTIVES: To explore the relationship between cumulative ecological risk and individual risky behavior and multiple forms of aggregated behaviors among adolescents, and examine the gender differences. METHODS: A large-scale, nationally representative, and students-based investigation was conducted in rural and urban areas of eight provinces in China from October to December 2021. A total of 22 868 adolescents with an average age of 14.64 years completely standardized questionnaire in which the sociodemographic characteristics, socio-ecological risk factors and risky behaviors were used to analyze. RESULTS: Of included students, 48.4% encountered the high level of social-ecological risk. The prevalence of breakfast intake not daily, alcohol use (AU), smoking, physical inactivity, prolonged screen time (ST) on weekdays and weekends, suicidal ideation, suicidal plan, suicidal attempt, and non-suicidal self-injury (NSSI) was 41.0%, 11.9%, 3.4%, 61.9%, 15.1%, 51.1%, 27.7%, 13.9%, 6.5% and 27.0% respectively. 22.2% of participants engaged in high-risk behaviors. All were significantly influences of increased cumulative ecological risk on individual behavior and low-risk clustering behaviors separately. The odds ratio of breakfast intake not daily, AU, smoking, physical inactivity, prolonged ST in weekday and weekend, suicidal ideation, suicidal plan, suicidal attempt, and NSSI for the adjusted model in low versus high level of cumulative ecological risk was respectively significant in both boy and girls, and the ratio of odds ratios (ROR) was separately 0.95 (p = 0.228), 0.67 (p < 0.001), 0.44 (p < 0.001), 0.60 (p < 0.001), 0.78 (p = 0.001), 0.83 (p = 0.001), 0.80 (p = 0.001), 0.83 (p = 0.022), 0.71 (p = 0.005), 0.75 (p = 0.001). Girls encountering a high level of cumulative ecological risk were more likely to engage in multiple forms of clustering risky behaviors than boys (RORs: 0.77, p = 0.001). CONCLUSIONS: Research and effective inventions at the social-ecological environment, based on the view of cumulative risk, are needed to promote the healthy development of behaviors in adolescence, and pay more attention to decreasing the occurrence of risky behaviours in girls than boys.

Artificial intelligence-based graded training of pulmonary nodules for junior radiology residents and medical imaging students.

BACKGROUND: To evaluate the efficiency of artificial intelligence (AI)-assisted diagnosis system in the pulmonary nodule detection and diagnosis training of junior radiology residents and medical imaging students. METHODS: The participants were divided into three groups. Medical imaging students of Grade 2020 in the Jinzhou Medical University were randomly divided into Groups 1 and 2; Group 3 comprised junior radiology residents. Group 1 used the traditional case-based teaching mode; Groups 2 and 3 used the 'AI intelligent assisted diagnosis system' teaching mode. All participants performed localisation, grading and qualitative diagnosed of 1,057 lung nodules in 420 cases for seven rounds of testing after training. The sensitivity and number of false positive nodules in different densities (solid, pure ground glass, mixed ground glass and calcification), sizes (less than 5 mm, 5-10 mm and over 10 mm) and positions (subpleural, peripheral and central) of the pulmonary nodules in the three groups were detected. The pathological results and diagnostic opinions of radiologists formed the criteria. The detection rate, diagnostic compliance rate, false positive number/case, and kappa scores of the three groups were compared. RESULTS: There was no statistical difference in baseline test scores between Groups 1 and 2, and there were statistical differences with Group 3 (P = 0.036 and 0.011). The detection rate of solid, pure ground glass and calcified nodules; small-, medium-, and large-diameter nodules; and peripheral nodules were significantly different among the three groups (P<0.05). After seven rounds of training, the diagnostic compliance rate increased in all three groups, with the largest increase in Group 2. The average kappa score increased from 0.508 to 0.704. The average kappa score for Rounds 1-4 and 5-7 were 0.595 and 0.714, respectively. The average kappa scores of Groups 1,2 and 3 increased from 0.478 to 0.658, 0.417 to 0.757, and 0.638 to 0.791, respectively. CONCLUSION: The AI assisted diagnosis system is a valuable tool for training junior radiology residents and medical imaging students to perform pulmonary nodules detection and diagnosis.

Distributed State Estimation for Flapping-Wing Micro Air Vehicles with Information Fusion Correction.

In this paper, we explore a nonlinear interactive network system comprising nodalized flapping-wing micro air vehicles (FMAVs) to address the distributed H∞ state estimation problem associated with FMAVs. We enhance the model by introducing an information fusion function, leading to an information-fusionized estimator model. This model ensures both estimation accuracy and the completeness of FMAV topological information within a unified framework. To facilitate the analysis, each FMAV's received signal is individually sampled using independent and time-varying samplers. Transforming the received signals into equivalent bounded time-varying delays through the input delay method yields a more manageable and analyzable time-varying nonlinear network error system. Subsequently, we construct a Lyapunov-Krasovskii functional (LKF) and integrate it with the refined Wirtinger and relaxed integral inequalities to derive design conditions for the FMAVs' distributed H∞ state estimator, minimizing conservatism. Finally, we validate the effectiveness and superiority of the designed estimator through simulations.

Does digital infrastructure improve public Health? A quasi-natural experiment based on China's Broadband policy.

This study proposed a scheme for improving people's health from the perspective of digital infrastructure construction. We used the China Family Panel Studies conducted between 2010 and 2020 and the digital infrastructure construction marked by the Broadband China policy between 2014 and 2016 as a quasi-natural experiment. We adopted the multi-time difference-in-differences method to identify the causal relationship between digital infrastructure and people's health. We found that digital infrastructure construction significantly improved people's health, and the effect was more prominent among young and middle-aged residents and those with less than a university education. Moreover, digital infrastructure construction improved the utilization of medical services, helped residents develop healthy lifestyles, and increased people's health investments. Additionally, digital infrastructure reduced health inequality among people and promoted health equity. The findings could guide future policies to improve people's health and well-being.

Abnormal expression of long non­coding RNA LINC01270 in glioma and its correlation with X­ray computed tomography signs.

This study focused on the association of LINC01270 and computed tomography (CT) signs with glioma development, to evaluate their potential in the early detection of glioma. Serum LINC01270 was evaluated in glioma patients and healthy individuals using PCR. The involvement of LINC01270 in glioma onset and development was evaluated by ROC and chi­square test. The association of LINC01270 with the CT signs and their combined effects in the diagnosis in glioma were also estimated. Serum LINC01270 was significantly elevated in glioma patients, which was closely associated with patients' advanced WHO grades and lower KPS. Both LINC01270 upregulation and CT findings showed significant potential in diagnosing glioma, and LINC01270 correlated significantly with the invasion risk and metastasis indicated on CT. The combination of LINC01270 expression and CT findings significantly improved the sensitivity and specificity of glioma diagnosis. Upregulated LINC01270 in glioma is associated with malignant and severe disease development and has significant diagnostic value. Combined detection of LINC01270 and CT findings could improve the diagnostic efficacy in glioma cases, thus optimizing clinical diagnosis.

A fully validated LC­MS/MS method for quantifying bevacizumab in plasma samples from patients with NSCLC and its implications in therapeutic drug monitoring.

Given the increasing use of bevacizumab in combinatorial drug therapy for a multitude of different cancer types, there is a need for therapeutic drug monitoring to analyze the possible correlation between drug trough concentration, and therapeutic effect and adverse reactions. An ultra-performance liquid chromatography tandem-mass spectrometry method was then developed and validated to determine bevacizumab levels in human plasma samples. Chromatographic separation was achieved on a Shimadzu InertSustainBio C18 HP column, whereas subsequent mass spectrometric analysis was performed using a Shimadzu 8050CL triple quadrupole mass spectrometer equipped with an electro-spray ionization source in the positive ion mode. In total, three multiple reaction monitoring transitions of each of the surrogate peptides were chosen with 'FTFSLDTSK' applied as the quantification peptide whereas 'VLIYFTSSLHSGVPSR' and 'STAYLQMNSLR' were designated as the verification peptides using the Skyline software. This analytical method was then fully validated, with specificity, linearity, lower limit of quantitation, accuracy, precision, stability, matrix effect and recovery calculated. The linearity of this method was developed to be within the concentration range 5-400 µg/ml for bevacizumab in human plasma. Subsequently, eight patients with non-small cell lung cancer (NSCLC) were recruited and injected with bevacizumab over three periods of treatment to analyze their steady-state trough concentration and differences. To conclude, the results of the present study suggest that bevacizumab can be monitored in a therapeutic setting in patients with NSCLC.

Majorbio Cloud 2024: Update single-cell and multiomics workflows.

Majorbio Cloud (https://cloud.majorbio.com/) is a one-stop online analytic platform aiming at promoting the development of bioinformatics services, narrowing the gap between wet and dry experiments, and accelerating the discoveries for the life sciences community. In 2024, three single-omics workflows, two multiomics workflows, and extensions were newly released to facilitate omics data mining and interpretation.

Majorbio Cloud: A one-stop, comprehensive bioinformatic platform for multiomics analyses.

The platform consists of three modules, which are pre-configured bioinformatic pipelines, cloud toolsets, and online omics' courses. The pre-configured bioinformatic pipelines not only combine analytic tools for metagenomics, genomes, transcriptome, proteomics and metabolomics, but also provide users with powerful and convenient interactive analysis reports, which allow them to analyze and mine data independently. As a useful supplement to the bioinformatics pipelines, a wide range of cloud toolsets can further meet the needs of users for daily biological data processing, statistics, and visualization. The rich online courses of multi-omics also provide a state-of-art platform to researchers in interactive communication and knowledge sharing.