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Background: Serum PSA has been widely applied to monitor prostate cancer after treatment. Compared to a single static value, PSA dynamics may provide additional prognostic information. Although PSA velocity and doubling time have demonstrated prognostic value, their clinical utility is limited by the need for prolonged longitudinal PSA follow-up. This study aimed to evaluate the prognostic potential of early postoperative PSA dynamics following radical prostatectomy. Methods: A retrospective cohort study of 3474 patients who underwent radical prostatectomy with available pre- and postoperative serum PSA measurements was analyzed. A population-based approach was used to characterize early postoperative PSA dynamics and estimate the time required for PSA to decline to undetectable levels (<0.1 ng/mL). Accordingly, patients were classified into early remission (≤60 days), delayed remission (>60 days), or persistent PSA (failure to achieve undetectable PSA). Associations between postoperative PSA dynamics and clinical outcomes were evaluated. Results: Biochemical recurrence rates differed across the early remission, delayed remission, and persistent PSA groups (5.3%, 7.7%, and 24.0%). All-cause mortality similarly increased across these groups (1.1%, 1.9%, and 5.8%, respectively). Cox proportional hazards models confirmed the difference in recurrence-free and overall survival among PSA clearance groups. Conclusions: Early postoperative PSA dynamics after radical prostatectomy are strongly associated with recurrence and survival outcomes. Both PSA trajectory patterns and PSA clearance speed carry important prognostic information. Early postoperative PSA monitoring may support risk stratification and guide individualized postoperative surveillance.

The widespread application of single-cell and spatial omics to models and patient samples has transformed immune cell profiling across physiological conditions. However, knowledge of immune cell states, functions, and gene regulation remains fragmented across publications, limiting our ability to synthesize insights and derive mechanistic understanding from the literature. To address this gap and facilitate literature integration, we constructed Immune Cell Knowledge Graphs (ICKGs)—four cell type-specific graphs derived from over 24,000 cancer immunotherapy-focused PubMed abstracts using large language models (LLMs) with “human verifiable” validation. Unlike conventional databases, which provide context-agnostic pathways, ICKGs capture directed, literature-supported relationships among genes, pathways and immune functions, enabling context-aware reasoning. We validated ICKGs using perturbation datasets from cytokine stimulation and CRISPR experiments, demonstrating that ICKGs contain more accurate and immunologically coherent contexts than canonical databases. As a key application, ICKGs provide interpretable and accurate pathway annotations, including signatures unannotated by canonical databases or used in immuno-oncology. To support community use, we created an interactive portal ( https://kchen-lab.github.io/immune-knowledgegraph.github.io/ ) to perform ICKG-based pathway annotations, allowing researchers to explore immune cell-specific insights grounded in literature. This work establishes ICKGs as a scalable framework for immune-specific functional interpretation and mechanistic hypothesis generation in single-cell and spatial omics.

Mutations in LRRK2 , a leading genetic cause of Parkinson’s disease (PD), are linked to immune dysregulation, but the immune profiles in the periphery and central nervous system (CNS) remain incompletely defined. This study utilized a large cohort of serum samples ( n  = 651) and matched CSF samples ( n  = 129) from LRRK2 mutation carriers and non-carriers, with and without PD, to assess immune regulators using Luminex immunoassay. After correction for multiple comparisons, LRRK2 mutations were associated with significantly elevated serum levels of SDF-1 alpha and TNF-RII, while CSF markers such as BAFF, CD40L, and IL-27 were nominally reduced. Regardless of LRRK2 status, PD was associated with nominally lower levels of inflammatory analytes in CSF, with minimal changes observed in serum. Correlation analyses revealed distinct immune profiles between serum and CSF, suggesting compartmentalized immune responses. These findings highlight immune alterations in LRRK2 mutation carriers and PD, providing potential serum markers for monitoring immune responses and avenues for mechanistic studies.

Mathematical models are widely recognized as an important tool for analyzing and understanding the dynamics of infectious disease outbreaks, predict their future trends, and evaluate public health intervention measures for disease control and elimination. We propose a novel stochastic metapopulation state-space model for COVID-19 transmission, which is based on a discrete-time spatio-temporal susceptible, exposed, infected, recovered, and deceased (SEIRD) model. The proposed framework allows the hidden SEIRD states and unknown transmission parameters to be estimated from noisy, incomplete time series of reported epidemiological data, by application of unscented Kalman filtering (UKF), maximum-likelihood adaptive filtering, and metaheuristic optimization. Experiments using both synthetic data and real data from the Fall 2020 COVID-19 wave in the state of Texas demonstrate the effectiveness of the proposed model.

Single-cell omics technologies enable molecular characterization of diverse cell types and states, but how the resulting transcriptional and epigenetic profiles depend on the cell’s genetic background remains understudied. We describe Monopogen, a computational tool to detect single-nucleotide variants (SNVs) from single-cell sequencing data. Monopogen leverages linkage disequilibrium from external reference panels to identify germline SNVs and detects putative somatic SNVs using allele cosegregating patterns at the cell population level. It can identify 100 K to 3 M germline SNVs achieving a genotyping accuracy of 95%, together with hundreds of putative somatic SNVs. Monopogen-derived genotypes enable global and local ancestry inference and identification of admixed samples. It identifies variants associated with cardiomyocyte metabolic levels and epigenomic programs. It also improves putative somatic SNV detection that enables clonal lineage tracing in primary human clonal hematopoiesis. Monopogen brings together population genetics, cell lineage tracing and single-cell omics to uncover genetic determinants of cellular processes. Monopogen identifies single-nucleotide variants in single-cell sequencing data.

Chimeric antigen receptor (CAR) natural killer (NK) cell immunotherapy offers a promising approach against cancer1, 2–3. However, the molecular mechanisms that regulate CAR-NK cell activity remain unclear. Here we identify the transcription factor cyclic AMP response element modulator (CREM) as a crucial regulator of NK cell function. Transcriptomic analysis revealed a significant induction of CREM in CAR-NK cells during the peak of effector function after adoptive transfer in a tumour mouse model, and this peak coincided with signatures of both activation and dysfunction. We demonstrate that both CAR activation and interleukin-15 signalling rapidly induce CREM upregulation in NK cells. Functionally, CREM deletion enhances CAR-NK cell effector function both in vitro and in vivo and increases resistance to tumour-induced immunosuppression after rechallenge. Mechanistically, we establish that induction of CREM is mediated by the PKA–CREB signalling pathway, which can be activated by immunoreceptor tyrosine-based activation motif signalling downstream of CAR activation or by interleukin-15. Finally, our findings reveal that CREM exerts its regulatory functions through epigenetic reprogramming of CAR-NK cells. Our results provide support for CREM as a therapeutic target to enhance the antitumour efficacy of CAR-NK cells.The transcription factor CREM is a pivotal regulator of NK cell function, making CREM a valuable target to increase the efficacy of anticancer immunotherapies based on this cell population and chimeric antigen receptors.

As the core component of printing machinery, the surface finish and geometric accuracy of printing drum will have an important impact on the quality of printed matter. However, the use of acid ink, alcohol and other chemical raw materials corrode the drum, leading to local collapse or spots. How to effectively identify the types of drum defects has become an important issue. To solve this problem, a defect detection and recognition framework based on adaboot-SVM ensemble learning model is proposed. The framework is composed of two parts: feature extraction and classifier design. The first part is feature extraction from directional gradient histogram (HOG). In the second part, we construct an ensemble of different SVM classifiers to identify defects. The validity of the proposed model is verified by nine different defects. The results show that the integrated model of adaboot SVM is helpful to improve the recognition accuracy of defects.

Background: The incidence of venous thromboembolism (VTE) in patients with ovarian cancer is higher than most solid tumors, ranging between 10–30%, and a diagnosis of VTE in this patient population is associated with worse oncologic outcomes. The tumor-specific molecular factors that may lead to the development of VTE are not well understood. Objectives: The aim of this study was to identify molecular features present in ovarian tumors of patients with VTE compared to those without. Methods: We performed a multiplatform omics analysis incorporating RNA and DNA sequencing, quantitative proteomics, as well as immune cell profiling of high-grade serous ovarian carcinoma (HGSC) samples from a cohort of 32 patients with or without VTE. Results: Pathway analyses revealed upregulation of both inflammatory and coagulation pathways in the VTE group. While DNA whole-exome sequencing failed to identify significant coding alterations between the groups, the results of an integrated proteomic and RNA sequencing analysis indicated that there is a relationship between VTE and the expression of platelet-derived growth factor subunit B (PDGFB) and extracellular proteins in tumor cells, namely collagens, that are correlated with the formation of thrombosis. Conclusions: In this comprehensive analysis of HGSC tumor tissues from patients with and without VTE, we identified markers unique to the VTE group that could contribute to development of thrombosis. Our findings provide additional insights into the molecular alterations underlying the development of VTE in ovarian cancer patients and invite further investigation into potential predictive biomarkers of VTE in ovarian cancer.

Large scale application of single-cell and spatial omics in models and patient samples has led to the discovery of many novel gene sets, particularly those from an immunotherapeutic context. However, the biological meaning of those gene sets has been interpreted anecdotally through over-representation analysis against canonical annotation databases of limited complexity, granularity, and accuracy. Rich functional descriptions of individual genes in an immunological context exist in the literature but are not semantically summarized to perform gene set analysis. To overcome this limitation, we constructed immune cell knowledge graphs (ICKGs) by integrating over 24,000 published abstracts from recent literature using large language models (LLMs). ICKGs effectively integrate knowledge across individual, peer-reviewed studies, enabling accurate, verifiable graph-based reasoning. We validated the quality of ICKGs using functional omics data obtained independently from cytokine stimulation, CRISPR gene knock-out, and protein-protein interaction experiments. Using ICKGs, we achieved rich, holistic, and accurate annotation of immunological gene sets, including those that were unannotated by existing approaches and those that are in use for clinical applications. We created an interactive website ( https://kchen-lab.github.io/immune-knowledgegraph.github.io/ ) to perform ICKG-based gene set annotations and visualize the supporting rationale.