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Despite the clinical benefits of androgen deprivation therapy, most patients with advanced androgen‐dependent prostate cancer (ADPC) eventually relapse and progress to lethal androgen‐independent prostate cancer (AIPC), also termed castration‐resistant prostate cancer (CRPC). MiRNAs can be packaged into exosomes (Exos) and shuttled between cells. However, the roles and mechanisms of exosomal miRNAs involved in CRPC progression have not yet been fully elucidated. Here, we find that miR‐222‐3p is elevated in AIPC cells, which results in remarkable enhancement of cell proliferation, migration, and invasion ability. Furthermore, Exos released by AIPC cells can be uptaken by ADPC cells, thus acclimating ADPC cells to progressing to more aggressive cell types in vitro and in vivo through exosomal transfer of miR‐222‐3p. Mechanistically, Exos‐miR‐222‐3p promoted ADPC cells transformed to AIPC‐like cells, at least in part, by activating mTOR signaling through targeting MIDN. Our results show that AIPC cells secrete Exos containing miRNA cargo. These cargos can be transferred to ADPC cells through paracrine mechanisms that have a strong impact on cellular functional remodeling. The current work underscores the great therapeutic potential of targeting Exo miRNAs, either as a single agent or combined with androgen receptor pathway inhibitors for CRPC treatment.

Prostate cancer (PCa) is the most common malignant tumor of the male urological system and poses a severe threat to the survival of middle-aged and elderly males worldwide. The development and progression of PCa are affected by a variety of biological processes, including proliferation, apoptosis, migration, invasion and the maintenance of membrane homeostasis of PCa cells. The present review summarizes recent research advances in lipid (fatty acid, cholesterol and phospholipid) metabolic pathways in PCa. In the first section, the metabolism of fatty acids is highlighted, from formation to catabolism and associated proteins. Subsequently, the role of cholesterol in the pathogenesis and evolution of PCa is described in detail. Finally, the different types of phospholipids and their association with PCa progression is also discussed. In addition to the impact of key proteins of lipid metabolism on PCa growth, metastasis and drug resistance, the present review also summarizes the clinical value of fatty acids, cholesterol and phospholipids, as diagnostic and prognostic indicators and therapeutic targets in PCa.

Despite achieving optimal initial responses to androgen deprivation therapy, most patients with prostate cancer eventually progress to a poor prognosis state known as castration-resistant prostate cancer (CRPC). Currently, there is a notable absence of reliable early warning biomarkers and effective treatment strategies for these patients. Although androgen receptor (AR)-independent pathways have been discovered and acknowledged in recent years, the AR signaling pathway continues to play a pivotal role in the progression of CRPC. The present review focuses on newly identified proteins within human CRPC tissues. These proteins encompass both those involved in AR-dependent and AR-independent pathways. Specifically, the present review provides an in-depth summary and analysis of the emerging proteins within AR bypass pathways. Furthermore, the significance of these proteins as potential biomarkers and therapeutic targets for treating CRPC is discussed. Therefore, the present review offers valuable theoretical insights and clinical perspectives to comprehensively enhance the understanding of CRPC.

Prostate cancer (PCa) remains among the most common genitourinary tumors in elderly men, as PCa diagnosis and treatment remain major challenges. Liquid biopsy is a minimally invasive method that causes minor harm to patients with cancer. Peripheral blood protein biomarkers provide real-time PCa information and are easily accessible. The present review summarizes recent progress in identifying candidate peripheral blood protein biomarkers of PCa, including pentraxin-3, soluble E-cadherin, serum T-cell immunoglobulin, serum B- and T-lymphocyte attenuator, myeloid differentiation factor-2, pleiotrophin, spondin 2, filamin A, soluble urokinase plasminogen activator receptor, laminin subunit [beta]-1, Golgi membrane protein 1, vitamin D-binding protein, tumor necrosis factor receptor superfamily member 9, activated leukocyte cell adhesion molecule and trophoblastic cell-surface antigen. Notably, the present review summarizes and discusses the clinical value of these proteins in PCa prediction, diagnosis, prognosis and drug resistance monitoring. These emerging peripheral blood protein biomarkers are promising for improving PCa stratification and management. Key words: prostate cancer, peripheral blood protein biomarkers, diagnostic value, predictive value, prognostic value, drug resistance monitoring value

The recent surge in diverse 3D datasets spanning various scales and applications marks a significant advancement in the field. However, the comprehensive process of data acquisition, refinement, and annotation at a large scale poses a formidable challenge, particularly for individual researchers and small teams. To this end, we present a novel synthetic 3D point cloud generation framework that can produce detailed outdoor aerial photogrammetric 3D datasets with accurate ground truth annotations without the labor-intensive and time-consuming data collection/annotation processes. Our pipeline procedurally generates synthetic environments, mirroring real-world data collection and 3D reconstruction processes. A key feature of our framework is its ability to replicate consistent quality, noise patterns, and diversity similar to real-world datasets. This is achieved by adopting UAV flight patterns that resemble those used in real-world data collection processes (e.g., the cross-hatch flight pattern) across various synthetic terrains that are procedurally generated, thereby ensuring data consistency akin to real-world scenarios. Moreover, the generated datasets are enriched with precise semantic and instance annotations, eliminating the need for manual labeling. Our approach has led to the development and release of the Semantic Terrain Points Labeling—Synthetic 3D (STPLS3D) benchmark, an extensive outdoor 3D dataset encompassing over 16 km2, featuring up to 19 semantic labels. We also collected, reconstructed, and annotated four real-world datasets for validation purposes. Extensive experiments on these datasets demonstrate our synthetic datasets’ effectiveness, superior quality, and their value as a benchmark dataset for further point cloud research.

Accurate ice flow velocity data are essential for studying the mass balance of the Antarctic ice sheet. However, there is a lack of ice velocity maps of 1960s–80s in basin-wide regions or the entire ice sheet. In this study, an enhanced hierarchical network densification approach is developed for basin-wide Antarctic velocity mapping using historical ARGON and Landsat images. The produced multiple historical velocity maps from 1963 to 1989 in the region of the Fimbul and Jelbart ice shelves, East Antarctica, achieved an accuracy better than 29 m a−1. They revealed that the ice flow velocity had no significant changes over the period. Combining the surface mass balance estimate with the ice discharge estimated from our historical velocity maps and recently published velocity maps, we estimated a positive mass balance of 8.6 ± 3.9 Gt a−1 in the study area from 1963 and 2015. Our results indicate that the region's positive mass balance, as estimated in recently published studies, has been maintained since the 1960s. It is also in concordance with the low level of mass balance from 1992 to 2017 in East Antarctica. This suggests that the study area has been stable since the 1960s.

Background Patients with acute exacerbations of chronic obstructive pulmonary disease (AECOPD) face a high risk of readmission following discharge. Accurate identification of high-risk individuals is crucial for optimising clinical management. However, clinical prediction models frequently encounter challenges such as limited sample sizes, data missingness, and category imbalance, which compromise their generalisability and clinical utility. Methods This retrospective study included patients first hospitalised for AECOPD at a tertiary hospital between December 2018 and July 2023. The primary outcome was unplanned all-cause readmission within one year post-discharge. Missing data were addressed using Multiple Imputation by Chained Equations (MICE). To enhance model robustness, conditional generative adversarial networks (CTGAN) were applied to 80% of the derivation cohort for data augmentation (generating 150% of the original sample size). Logistic regression, decision trees, random forests, XGBoost, and LightGBM models were constructed on the augmented data. Hyperparameters were optimised using grid search and 5-fold cross-validation, with performance evaluated on the reserved 20% test set. The predictive mechanisms of the optimal model were interpreted using the SHAP framework. Results A total of 1,960 patients were included, of whom 783 (39.9%) experienced readmission. Data augmentation effectively mitigated overfitting and significantly improved model generalisation on the test set. The XGBoost model demonstrated optimal performance, achieving an AUC of 0.696 on the test set alongside favourable calibration and clinical net benefit. SHAP analysis revealed that eosinophil count (EOS, negatively correlated), ICU admission status (positively correlated), red cell distribution width (RDW-SD, positively correlated), Prognostic Nutritional Index (PNI, negatively correlated), and platelet-lymphocyte ratio (PLR, positively correlated) were the most critical features driving model predictions. Conclusion This study successfully developed and validated a readmission risk prediction model for AECOPD patients based on routine clinical variables. The integration of CTGAN data augmentation strategies effectively enhanced model performance. The optimal XGBoost model not only demonstrated strong discriminative capability but also exhibited interpretable predictive logic consistent with clinical pathophysiological mechanisms, as revealed by SHAP analysis. This model holds potential for clinical translation, aiding in the identification of high-risk individuals for readmission and enabling early intervention. Clinical trial Not applicable.

Acquiring acoustic modes with high quality data in large-scale nacelles is quite challenging in the engine industry because of the complex configuration, high flow speed, tremendous number of acoustic modes, and some other extraordinary interference. A complete procedure for mode detection in the engine industry that is applicable to full-size situations is proposed. Two different array patterns are adopted: a circular array for azimuthal modes in both the intake and bypass ducts, and a rotating linear array for radial modes only in the bypass duct. The azimuthal locations of sensors in the circumferential array are non-uniformly distributed to get more modes than the Nyquist limit. For each individual channel signal, an adaptive resampling method is adopted to reduce the components incoherent with source rotation and frequency shifts caused by shaft speed variation. At high flow speeds, boundary turbulence contaminates acoustic signals of wall-flush mounted sensors. A wavenumber decomposition method is used to separate the acoustic part and the dynamic pressure part in the bypass duct during radial mode detection. Finally, both the azimuthal and radial acoustic modes in bypass and intake ducts are acquired successfully.

Coxiella burnetii ( C. burnetii ) is the causative agent of Q fever, a type of zoonoses withwidespread distribution. In 2019, a case of Q fever was diagnosed by metagenomic next-generation sequencing (mNGS) method in Xuyi County (Jiangsu province, China). The seroprevalence of previous fever patients and the molecular epidemiology of Coxiella in wild hedgehogs and harbouring ticks around the confirmed patient were detected to reveal the genetic characteristics and pathogenicity of the Coxiella strains. Four of the 90 serum samples (4.44%) were positive for specific C. burnetii IgM antibody, suggesting that local humans are at risk of Q fever. The positive rates of C. burnetii in hedgehogs and ticks were 21.9% (7/32) and 70.5% (122/173), respectively. At least 3 strains of Coxiella were found prevalent in the investigated area, including one new genotype of pathogenic C. burnetii (XYHT29) and two non-pathogenic Coxiella-like organisms (XYHT19 and XYHT3). XYHT29 carried by ticks and wild hedgehogs successfully infected mice, imposing a potential threat to local humans. XYHT19, a novel Coxiella-like microorganism, was first discovered in the world to co-infect with C. burnetii in Haemaphysalis flava . The study provided significant epidemic information that could be used for prevention and control strategies against Q fever for local public health departments and medical institutions.

Auxiliary activity family 9 (AA9) lytic polysaccharide monooxygenases (LPMOs) show significant synergism with cellulase in cellulose degradation. In recent years, there have been many reports on AA9 LPMOs; however, the identification of efficient and thermostable AA9 LPMOs with broad potential for industrial applications remains necessary. In this study, a new AA9 LPMO from Talaromyces cellulolyticus , named Tc AA9A, was identified. An analysis of the oxidation products of phosphoric acid-swollen cellulose categorized Tc AA9A as a type 3 AA9 LPMO, and Tc AA9A exhibited a better synergistic effect with cellulase from Trichoderma reesei than what is seen with Ta AA9A, a well-studied AA9 LPMO from Thermoascus aurantiacus . Two AA9 LPMOs were successfully expressed in T. reesei , and the transformants were named Tr Tc AA9A and Tr Ta AA9A . The activities and thermostabilities of the AA9 LPMOs in Tr Tc AA9A were higher than those of the AA9 LPMOs in Tr Ta AA9A or the parent. The enzyme solution of Tr Tc AA9A was more efficient than that of the parent or Tr Ta AA9A for the degradation of Avicel and delignified corncob residue. Thus, Tc AA9A showed a better performance than Ta AA9A in T . reesei cellulase cocktails. This study may offer an innovative solution for improving enzyme cocktail activity for lignocellulosic degradation.