BACKGROUND:Primary central nervous system lymphoma (PCNSL) is an aggressive, immune-privileged, large B-cell lymphoma with limited frontline treatment options. METHODS:This study was an open-label, single-arm, phase 1/2 trial evaluating orelabrutinib combined with an anti-programmed cell death protein-1 (PD-1) antibody and a non-methotrexate chemotherapeutic agent (fotemustine) in patients with newly diagnosed PCNSL (ClinicalTrials.gov identifier NCT04831658). Orelabrutinib was tested at three dose levels (100, 150, and 200 mg), and the recommended phase 2 dose was determined as 150 mg. In phase 2, patients received orelabrutinib 150 mg orally once daily, a PD-1 inhibitor 200 mg intravenously on day 1, and fotemustine 100 mg/m2 intravenously on day 2 every 21 days for six cycles. The primary endpoint was the objective response rate. RESULTS:From February 2021 to October 2023, 31 patients (median age, 62 years; age range, 37-70 years) were treated, and 27 were evaluable for efficacy. The objective response rate was 85.2% (complete response, 66.7%; partial response, 18.5%). The median progression-free survival was 9.4 months (95% confidence interval, 5.4-13.4 months), and the median overall survival was 22.8 months (95% confidence interval, 1.1-44.5 months). The 1-year and 2-year overall survival rates were 68.0% and 48.0%, respectively. The most common grade 3/4 adverse events were thrombocytopenia (45.2%), pulmonary infection (38.7%), and leukopenia (25.8%). CONCLUSIONS:Orelabrutinib combined with PD-1 blockade and fotemustine demonstrated high antitumor activity with manageable toxicity, supporting its potential as a frontline regimen for PCNSL.

Natural killer/T-cell lymphoma (NKTCL) is an aggressive haematological malignancy with poor prognosis, particularly in patients with relapsed/refractory (R/R) disease. The mechanisms underlying multidrug resistance in NKTCL remain unclear and present an urgent challenge that must be addressed during clinical treatment. Multidrug-resistant NKTCL models were established using adriamycin (ADM), and cellular senescence was confirmed by markers including P16, P21, and senescence-associated β-galactosidase (SA-β-gal). Proteomic sequencing of plasma from clinical patients and resistant cells identified LCP2 as a key protein. Phosphoproteomics, mass spectrometry, and co-immunoprecipitation analyses revealed LCP2's role in mediating senescence-associated chemoresistance. An in vivo ageing microenvironment model was used to assess whether targeting the LCP2-mediated axis could eliminate chemoresistant senescent cells. Results show that ADM-resistant NKTCL cells exhibited phenotypic and senescence features. Of these, LCP2 expression was significantly reduced in the plasma of R/R NKTCL patients and in chemoresistant cells, correlating inversely with senescence marker SA-β-gal. Moreover, LCP2 knockdown enhanced the chemoresistance, senescent-associated secretory phenotype secretion, and G0/G1 cell cycle arrest in NKTCL cells. Mechanistically, LCP2 deficiency activated the IQGAP2/LaminA/C/SUV39H1 axis, thus driving DNA damage, telomere stress-induced senescence, and facilitating the formation of an immunosuppressive microenvironment. Importantly, targeting this axis with Epitalon and Chaetocin can partially eliminate therapy-induced senescent cells, enhance response to chemotherapeutics, and alleviate the immunosuppressive microenvironment to a certain extent in vivo. In conclusion, this study is the first to uncover LCP2 as a critical biomarker of senescence-related chemoresistance in NKTCL, providing a theoretical basis for the clinical translation of senolytics for treating R/R NKTCL.

Diffuse large B-cell lymphoma (DLBCL) features an immunosuppressive tumor microenvironment (TME), yet the molecular drivers connecting metabolic reprogramming to immune evasion remain poorly defined. Here, we deployed an integrative single-cell transcriptomic analysis combined with a machine learning (ML) framework to systematically identify key immune-suppressive hubs in DLBCL. Through ML-driven prioritization of a 33-gene panel, PAICS emerged as a central node within an immunosuppressive B-cell subgroup. Functional assays confirmed that PAICS promotes lymphoma proliferation, survival, and tumor growth while establishing an immunosuppressive TME-marked by reduced IFN‑γ, elevated TGF‑β and IL‑10, and enhanced CD8⁺ T cell exhaustion. Mechanistically, we uncovered the IRF4-PAICS-LDHA axis: IRF4 transcriptionally activates PAICS, which physically interacts with LDHA to augment its activity, thereby skewing the NAD⁺/NADH balance toward metabolic immunosuppression. Importantly, our AI-aided approach not only identified this axis but also predicted its vulnerability to metabolic intervention: both methotrexate treatment and LDHA knockdown restored metabolic balance, reversed T‑cell exhaustion, and suppressed tumor growth. These findings highlight the power of ML in uncovering multi-targetable metabolic-immune networks and in guiding therapeutic strategies to overcome immune evasion in DLBCL.

Context: Natural killer/T-cell lymphoma (NKTCL) is an aggressive malignancy with a high propensity for drug resistance, particularly to anthracyclines like adriamycin (ADM). The molecular mechanisms of ADM resistance in NKTCL are not fully understood. Objective: In this study, we aimed to elucidate the role of YTHDF2 in the regulation of SLC16A9 mRNA stability and its implications in ADM resistance in NKTCL. Materials & methods: The study examined the expression changes of SLC16A9 under varying concentrations of ADM and manipulated the expression levels of SLC16A9 and YTHDF2 through overexpression and knockdown. By analyzing the levels of m6A modification, it was revealed that YTHDF2 regulates SLC16A9 expression via the m6A pathway, thereby influencing ADM resistance. This conclusion was further validated by in vivo experiments. Results: Our results showed that overexpression of SLC16A9 enhanced ADM resistance, while knockdown of SLC16A9 increased sensitivity to ADM. Further investigation revealed that ADM treatment reduces m6A modification levels on SLC16A9 mRNA, which is associated with decreased binding of YTHDF2 to SLC16A9 mRNA, leading to increased SLC16A9 expression. We also found that overexpression of YTHDF2 reduced SLC16A9 expression and increased ADM sensitivity, whereas knockdown of YTHDF2 had the opposite effect. In vivo experiments using a nude mouse model further confirmed that SLC16A9 knockdown reduces tumor growth and enhances sensitivity to ADM. Conclusions: our study provides direct evidence that YTHDF2 modulates ADM resistance in NKTCL by regulating the m6A modification of SLC16A9. Targeting the YTHDF2-m6A-SLC16A9 axis may offer a novel therapeutic strategy to overcome chemotherapy resistance in NKTCL.

Cellular senescence has a complex role in lymphocyte carcinogenesis and drug resistance of lymphomas. Senescent lymphoma cells combine with immunocytes to create an ageing environment that can be reprogrammed with a senescence‑associated secretory phenotype, which gradually promotes therapeutic resistance. Certain signalling pathways, such as the NF‑κB, Wnt and PI3K/AKT/mTOR pathways, regulate the tumour ageing microenvironment and induce the proliferation and progression of lymphoma cells. Therefore, targeting senescence‑related enzymes or their signal transduction pathways may overcome radiotherapy or chemotherapy resistance and enhance the efficacy of relapsed/refractory lymphoma treatments. Mechanisms underlying drug resistance in lymphomas are complex. The ageing microenvironment is a novel factor that contributes to drug resistance in lymphomas. In terms of clinical translation, some senolytics have been used in clinical trials on patients with relapsed or refractory lymphoma. Combining immunotherapy with epigenetic drugs may achieve better therapeutic effects; however, senescent cells exhibit considerable heterogeneity and lymphoma has several subtypes. Extensive research is necessary to achieve the practical application of senolytics in relapsed or refractory lymphomas. This review summarises the mechanisms of senescence‑associated drug resistance in lymphoma, as well as emerging strategies using senolytics, to overcome therapeutic resistance in lymphoma.

In this paper, we propose a high-precision automatic segmentation method for foreign body defects in high-precision small-field TFT-LCD images to segment TFT-LCD foreign body defects and calculate their size accurately, meeting the requirements of foreign matter defect detection in TFT-LCD industrial production. First, using the spatial distribution of screen pixels and considering the dimensional change of defects, we employ the defect extraction method based on spatial information multiscale saliency detection to automatically obtain the defect areas on the image. Next, combining the spatial distribution relationship between the defects and the gap of screen pixels, the corresponding defect block group truncated by pixel gap is found. Finally, a local convex hull fitting algorithm is used to connect the defect areas to realize automatic segmentation for foreign body defects. Experimental results show that the proposed method can segment foreign body defects more accurately, attaining the accuracy and recall rate of 95.36% and 93.34%, respectively. Furthermore, it obtains the correct size calculation rate of 96.5%, which meets the requirements of TFT-LCD foreign body defect size calculation in industrial production stability, reliability, high precision, high accuracy, and other requirements.

Liver segmentation is an important step in computer-aided diagnosis, treatment and surgery of liver diseases. A liver segmentation method based on spatial fuzzy C-means and graph cuts is proposed. Firstly, in order to remove the influence of adjacent organs and tissues on liver segmentation, the spine and ribs are removed from original CT images by thresholding, projection method and 3D region growing, and the right kidney is removed by K-means and binary morphological reconstruction method. Then, liver is segmented by spatial fuzzy C-means from the initial liver slice. The remaining slices are segmented iteratively by graph cuts based on the spatial, shape and gray scale characteristics of CT volumes. Finally, the inferior vena cava is removed by morphological operations and anatomical knowledge. The experimental results show that the proposed method can obtain better segmentation performance than those of other similar methods.

Liver segmentation from abdominal computed tomography (CT) volumes is extremely important for computer-aided liver disease diagnosis and surgical planning of liver transplantation. Due to ambiguous edges, tissue adhesion, and variation in liver intensity and shape across patients, accurate liver segmentation is a challenging task. In this paper, we present an efficient semi-automatic method using intensity, local context, and spatial correlation of adjacent slices for the segmentation of healthy liver regions in CT volumes. An intensity model is combined with a principal component analysis (PCA) based appearance model to exclude complex background and highlight liver region. They are then integrated with location information from neighboring slices into graph cuts to segment the liver in each slice automatically. Finally, a boundary refinement method based on bottleneck detection is used to increase the segmentation accuracy. Our method does not require heavy training process or statistical model construction, and is capable of dealing with complicated shape and intensity variations. We apply the proposed method on XHCSU14 and SLIVER07 databases, and evaluate it by MICCAI criteria and Dice similarity coefficient. Experimental results show our method outperforms several existing methods on liver segmentation.

The purpose of the present study was to perform a meta-analysis to evaluate the diagnostic value of Multidetector computed tomography (MDCT) in the pre-operative lymph node (N) staging in gastric cancer (GC) patients. The Medline, Embase and Web of Knowledge were searched for studies assessing the diagnostic value of MDCT in the pre-operative evaluation of TNM staging in GC patients. We pooled the sensitivity, specificity, positive and negative Likelihood ratio (LR+ and LR-), Diagnostic Odds Ratio (DOR) and constructed summary receiver operating characteristic curves (ROC). A total of 30 studies including 6637 GC patients were analyzed. The pooled estimates of sensitivity, specificity, LR+, LR- and DOR of MDCT in the detection of pre-operative N staging in GC patients were 0.67 (95% CI: 0.66-0.69 ), 0.84 (95% CI: 0.83-0.85), 3.25 (95% CI: 2.69-3.93), 0.36 (95% CI: 0.28-0.46) and 10.31 (95% CI: 7.66-13.88), respectively. The results of a summary ROC showed that the AUC and Q* were 0.8338 and 0.7661, respectively. As a control, the AUC and Q* of endoscopic ultrasonography were 0.8063 and 0.7414, respectively. Currently, it is necessary to recommend the routine clinical application of MDCT in the preoperative evaluation of lymph node status in GC patients.