Low dose triptolide reverses chemoresistance in adult acute lymphoblastic leukemia cells via reactive oxygen species generation and DNA damage response disruption

Chemoresistance represents a major challenge for treatment of acute lymphoblastic leukemia (ALL). Thus, new drugs to overcome chemoresistance in ALL are urgently needed. To this end, we established a cytarabine (araC)-resistant ALL cell line (NALM-6/R), which interestingly displayed cross-resistance towards doxorubicin (ADM). Here we report that low dose of triptolide (TPL), a natural product used for treating inflammatory diseases such as arthritis, could reverse araC and ADM resistance and in NALM-6/R cells as well as primary cells from patients with relapsed or refractory (R/R) ALL, reflected by inhibition of cell proliferation and induction of apoptosis in vitro, and repression of tumor growth in vivo in a mouse xenograft model. Mechanistically, these events were associated with impaired mitochondrial membrane potential and increased reactive oxygen species (ROS) production. Co-treatment with TPL and araC or ADM upregulated pro-apoptotic caspase-9 protein, inhibited checkpoint kinase 1 (Chk1) and 2 (Chk2) phosphorylation, and induced γH2A.X (a DNA damage marker). Notably, the combination regimen of TPL and conventional chemotherapeutics also rapidly diminished tumor burden in a patient with R/R ALL. Together, these findings provide preclinical evidence for repurposing use of TPL in combination with chemotherapeutic agents to treat R/R ALL as an alternative salvage regimen

The synergistic effect and mechanism of doxorubicin-ZnO nanocomplexes as a multimodal agent integrating diverse anticancer therapeutics

BACKGROUND: Nanomaterials have emerged as ideal multimodal nanomedicine platforms, each one combining different designs and therapeutic approaches in a single system against cancer. The aim of the current study was to explore the synergistic effect and mechanism of a doxorubicin (Dox)-ZnO nanocomplex as a multimodal drug delivery system, integrating Dox chemotherapy and ZnO-mediated photodynamic therapy, in anticancer therapeutics. METHODS: Dox was loaded onto ZnO nanomaterials, forming complexes with the transition metal Zn to yield the Dox-ZnO nanocomplexes. After culture with SMMC-7721 hepatocarcinoma cells, the cellular uptake was quantitatively detected by flow cytometry and visualized by fluorescence microscopy. The synergistic effects of the different anticancer therapeutic modalities on the proliferation of SMMC-7721 hepatocarcinoma cells were evaluated by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. The expression of B-cell lymphoma 2 protein (Bcl-2), Bcl-2 associated X protein (Bax), caspase 9, and caspase 3 were examined by Western blot, to elucidate the possible molecular mechanisms involved. RESULTS: Our observations demonstrated that Dox-ZnO nanocomplexes could act as an efficient drug delivery system for importing Dox into SMMC-7721 cells, enhancing its potential chemotherapy efficiency by increasing the intracellular concentration of Dox. With the addition of ultraviolet (UV) illumination, the ZnO nanomaterials showed excellent photodynamic therapeutic properties, attacking the cancer cells further. Thus the caspase-dependent apoptosis was synergistically induced, resulting in distinct improvement in anticancer activity. CONCLUSION: The Dox-ZnO nanocomplex presents a promising multimodal agent for comprehensive cancer treatment

GaitGS: Temporal Feature Learning in Granularity and Span Dimension for Gait Recognition

Gait recognition is an emerging biological recognition technology that identifies and verifies individuals based on their walking patterns. However, many current methods are limited in their use of temporal information. In order to fully harness the potential of gait recognition, it is crucial to consider temporal features at various granularities and spans. Hence, in this paper, we propose a novel framework named GaitGS, which aggregates temporal features in the granularity dimension and span dimension simultaneously. Specifically, Multi-Granularity Feature Extractor (MGFE) is proposed to focus on capturing the micro-motion and macro-motion information at the frame level and unit level respectively. Moreover, we present Multi-Span Feature Learning (MSFL) module to generate global and local temporal representations. On three popular gait datasets, extensive experiments demonstrate the state-of-the-art performance of our method. Our method achieves the Rank-1 accuracies of 92.9% (+0.5%), 52.0% (+1.4%), and 97.5% (+0.8%) on CASIA-B, GREW, and OU-MVLP respectively. The source code will be released soon.Comment: 14 pages, 6 figure

OSNet & MNetO: Two Types of General Reconstruction Architectures for Linear Computed Tomography in Multi-Scenarios

Recently, linear computed tomography (LCT) systems have actively attracted attention. To weaken projection truncation and image the region of interest (ROI) for LCT, the backprojection filtration (BPF) algorithm is an effective solution. However, in BPF for LCT, it is difficult to achieve stable interior reconstruction, and for differentiated backprojection (DBP) images of LCT, multiple rotation-finite inversion of Hilbert transform (Hilbert filtering)-inverse rotation operations will blur the image. To satisfy multiple reconstruction scenarios for LCT, including interior ROI, complete object, and exterior region beyond field-of-view (FOV), and avoid the rotation operations of Hilbert filtering, we propose two types of reconstruction architectures. The first overlays multiple DBP images to obtain a complete DBP image, then uses a network to learn the overlying Hilbert filtering function, referred to as the Overlay-Single Network (OSNet). The second uses multiple networks to train different directional Hilbert filtering models for DBP images of multiple linear scannings, respectively, and then overlays the reconstructed results, i.e., Multiple Networks Overlaying (MNetO). In two architectures, we introduce a Swin Transformer (ST) block to the generator of pix2pixGAN to extract both local and global features from DBP images at the same time. We investigate two architectures from different networks, FOV sizes, pixel sizes, number of projections, geometric magnification, and processing time. Experimental results show that two architectures can both recover images. OSNet outperforms BPF in various scenarios. For the different networks, ST-pix2pixGAN is superior to pix2pixGAN and CycleGAN. MNetO exhibits a few artifacts due to the differences among the multiple models, but any one of its models is suitable for imaging the exterior edge in a certain direction.Comment: 13 pages, 13 figure

Randomized clinical trial comparing abluminal biodegradable polymer sirolimus-eluting stents with durable polymer sirolimus-eluting stents: Nine months angiographic and 5-year clinical outcomes

Background: The biodegradable polymer drug-eluting stents (DES) were developed to improve vascular healing. However, further data and longer-term follow-up are needed to confirm safety and efficacy of these stents. This randomized clinical trial aimed to compare safety and efficacy of 2 sirolimus-eluting stents (SES): Cordimax—a novel abluminal biodegradable polymer SES and Cypher Select—a durable polymer SES, at 9 months angiographic and 5-year clinical follow-up. Methods: We randomized 402 patients with coronary artery disease to percutaneous coronary intervention with Cordimax (n = 202) or Cypher select (n = 200). Angiographic follow-up was performed at 9 months after the index procedure and clinical follow-up annually up to 5 years. The primary endpoint was angiographic in-stent late luminal loss (LLL). Secondary endpoints included angiographic restenosis rate, target vessel revascularization (TVR), and major adverse cardiac events (MACEs; defined as cardiac death, myocardial infarction, or TVR) at 5-year follow-up. Results: Cordimax was noninferior to Cypher select for in-stent LLL (0.25 ± 0.47 vs 0.18 ± 0.49 mm; P = 0.587) and in-stent mean diameter stenosis (22.19 ± 12.21% vs 19.89 ± 10.79%; P = 0.064) at 9 months angiographic follow-up. The MACE rates were not different at 1 year (5.9% vs 4.0%, P = 0.376); however, MACE rates from 2 to 5 years were lower in the Cordimax group (6.8% vs 13.1%; P = 0.039). Conclusion: Abluminal biodegradable polymer SES is noninferior to durable polymer SES at 9-month angiographic and 1-year clinical follow-up. However, MACE rates from 2 to 5 years were less in the abluminal biodegradable polymer group.National Key Technology Research and Development Program in the Twelfth Five-year Plan Period of China (No. 2014BAI11B04)

PCAT19: the role in cancer pathogenesis and beyond

PCAT19, a long non-coding RNA, has attracted considerable attention due to its diverse roles in various malignancies. This work compiles current research on PCAT19’s involvement in cancer pathogenesis and progression. Abnormal expression of PCAT19 has been observed in various cancers, and its correlation with clinical features and prognosis positions it as a promising prognostic biomarker. Additionally, its ability to effectively differentiate between tumor and normal tissues suggests significant diagnostic value. PCAT19 exhibits a dual nature, functioning either as an oncogene or a tumor suppressor, depending on the cancer type. It is implicated in a range of tumor-related activities, including cell proliferation, apoptosis, invasion, migration, metabolism, as well as tumor growth and metastasis. PCAT19 acts as a competing endogenous RNA (ceRNA) or interacts with proteins to regulate critical cancer-related pathways, such as MELK signaling, p53 signaling, and cell cycle pathways. Furthermore, emerging evidence suggests that PCAT19 plays a role in the modulation of neuropathic pain, adding complexity to its functional repertoire. By exploring the molecular mechanisms and pathways associated with PCAT19, we aim to provide a comprehensive understanding of its multifaceted roles in human health and disease, highlighting its potential as a therapeutic target for cancer and pain management