Dihydroartemisinin enhances the anti-tumour effect of photodynamic therapy by targeting PKM2-mediated glycolysis in oesophageal cancer cell.

Photodynamic therapy (PDT) has been demonstrated to provide immediate relief of oesophageal cancer patients' re-obstruction and extend their lifespan. However, tumour regrowth may occur after PDT due to enhanced aerobic glycolysis. Previous research has confirmed the inhibitory effect of Dihydroartemisinin (DHA) on aerobic glycolysis. Therefore, the current study intends to investigate the function and molecular mechanism of DHA targeting tumour cell aerobic glycolysis in synergia PDT. The combined treatment significantly suppressed glycolysis in vitro and in vivo compared to either monotherapy. Exploration of the mechanism through corresponding experiments revealed that pyruvate kinase M2 (PKM2) was downregulated in treated cells, whereas overexpression of PKM2 nullified the inhibitory effects of DHA and PDT. This study proposes a novel therapeutic strategy for oesophageal cancer through DHA-synergized PDT treatment, targeting inhibit PKM2 to reduce tumour cell proliferation and metastasis.

Quantification of intramyocardial blood volume using 99mTc-RBC SPECT/CT: a pilot human study.

BACKGROUND: Quantification of intramyocardial blood volume (IMBV), the fraction of myocardium that is occupied by blood, is a promising Index to measure microcirculatory functions. In previous large animal SPECT/CT studies injected with 99mTc-labeled Red Blood Cell (RBC) and validated by ex vivo microCT, we have demonstrated that accurate IMBV can be measured. In this study, we report the data processing methods and results of the first-in-human pilot study. METHODS: Data from three subjects have been included to date. Each subject underwent rest and adenosine-induced stress 99mTc-RBC SPECT/CT on a dedicated cardiac system with both non-contrast and contrast-enhanced CT acquired. Corrections of attenuation (AC) and scatter (SC), respiratory and cardiac gating, and partial volume correction (PVC) were applied. We also performed automatic segmentation and registration approach based on the blood pool topology in both SPECT and CT images. RESULTS: The quantified IMBV across all subjects under resting conditions were 35.0% ± 3.3% for the end-diastolic phase and 24.1% ± 2.7% for the end-systolic phase. The cycle-dependent change in IMBV (ΔIMBV) between diastolic and systolic phases was 31.5% ± 3.0%. Under stress, IMBV were 40.6% ± 4.2% for the end-diastolic phase and 26.5% ± 2.8% for the end-systolic phase, and ΔIMBV was 34.7% ± 7.4%. CONCLUSIONS: It is feasible to quantify IMBV in resting and stress conditions in human studies using SPECT/CT with 99mTc-RBC.

The intellectual base and global trends in inflammation of diabetic kidney disease: a bibliometric analysis.

Diabetic kidney disease (DKD) is a severe complication of diabetes mellitus (DM). The literature on DKD inflammation research has experienced substantial growth. However, there is a lack of bibliometric analyses. This study aimed to examine the existing research on inflammation in DKD by analyzing articles published in the Web of Science Core Collection (WOSCC) over the past 30 years. We conducted a visualization analysis using several software, including CiteSpace and VOSviewer. We found that the literature on inflammation research in DKD has experienced substantial growth, indicating a rising interest in this developing area of study. In this field, Navarro-Gonzalez, JF is the most frequently cited author, Kidney International is the most frequently cited journal, China had the highest number of publications in the field of DKD inflammation, and Monash University emerged as the institution with the most published research. The research area on inflammation in DKD primarily centers around the investigation of 'Glycation end-products', 'chronic kidney disease', and 'diabetic nephropathy'. The emerging research trends in this field will focus on the 'Gut microbiota', 'NLRP3 inflammasome', 'autophagy', 'pyroptosis', 'sglt2 inhibitor', and 'therapeutic target'. Future research on DKD may focus on further exploring the inflammatory response, identifying specific therapeutic targets, studying biomarkers, investigating stem cell therapy and tissue engineering, and exploring gene therapy and gene editing. In summary, this study examines the main areas of study, frontiers, and trends in DKD inflammation, which have significant implications for future research.

Relevance of the pyroptosis-related inflammasome drug targets in the Chuanxiong to improve diabetic nephropathy.

BACKGROUND: A chronic inflammatory disease caused by disturbances in metabolism, diabetic nephropathy (DN) is a chronic inflammatory disease. Pyroptosis is a novel form of programmed cell death in many inflammation-related diseases, including DN. Therefore, pyroptosis could be a promising target for DN therapy. METHODS: To get the components and pharmacodynamic targets of Chuanxiong, we identified by searching TCMID, TCMSP, ETCM and HERB databases. Then, from the Molecular Signatures Database (MSigDB) and Gene Ontology (GO) database, pyroptosis genes were collected. Identification of critical genes in DN by bioinformatics analysis and then using the ConsensusClusterPlus package to divide the express data of diff genes into some subgroups with different levels of pyroptosis; the WGCNA machine algorithm was used to simulate the mechanism Chuanxiong improving DN. RESULTS: In this study, we found DHCR24, ANXA1, HMOX1, CDH13, ALDH1A1, LTF, CHI3L1, CACNB2, and MTHFD2 interacted with the diff genes of DN. We used GSE96804 as a validation set to evaluate the changes of APIP, CASP6, CHMP2B, CYCS, DPP8, and TP53 in four different cell proapoptotic states. WGCNA analysis showed that DHCR24, CHI3L1, and CACNB2 had significant changes in different cell proapoptotic levels. In the experimental stage, we also confirmed that the active ingredients of Chuanxiong could improve the inflammatory state and the levels of pyroptosis under high glucose. CONCLUSION: The improvement of DN by Chuanxiong is related to the change of pyroptosis.

Tumor-suppressive MEG3 induces microRNA-493-5p expression to reduce arabinocytosine chemoresistance of acute myeloid leukemia cells by downregulating the METTL3/MYC axis.

BACKGROUND: Chemoresistance serves as a huge obstacle for acute myeloid leukemia (AML) patients. To counteract the chemoresistance in AML cells, we discussed the role of maternally expressed gene 3 (MEG3) in arabinocytosine (AraC) chemoresistance in AML cells. METHODS: MEG3, microRNA (miR)-493-5p, methyltransferase-like 3 (METTL3) and MYC expression in AML cells was determined and then their interactions were also analyzed. Then, the viability and apoptosis of AML cells were determined through loss- and gain- function assay. The level of m6A modification in AML cells was examined. AML mouse models were also established to validate the potential roles of MEG3. RESULTS: MEG3 and miR-493-5p were downregulated in AML cells, and they were lower in resistant cells than in parental cells. MEG3 led to elevated expression of miR-493-5p which targeted METTL3. METTL3 increased expression of MYC by promoting its m6A levels. Overexpression of MEG3 and miR-493-5p or knockdown of METTL3 inhibited HL-60 and Molm13 cell proliferation and promoted their apoptosis. Overexpressed MEG3 induced heightened sensitivity of AML cells to AraC. However, the suppression of miR-493-5p reversed the effects of overexpressed MEG3 on AML cells. CONCLUSIONS: Collectively, MEG3 could upregulate miR-493-5p expression and suppress the METTL3/MYC axis through MYC m6A methylation, by which MEG3 promoted the chemosensitivity of AML cells.

Downregulation of SMIM3 inhibits growth of leukemia via PI3K-AKT signaling pathway and correlates with prognosis of adult acute myeloid leukemia with normal karyotype.

BACKGROUND: Acute myeloid leukemia (AML) patients with normal karyotype (NK-AML) have significant variabilities in outcomes. The European Leukemia Net stratification system and some prognostic models have been used to evaluate risk stratification. However, these common standards still have some limitations. The biological functions and mechanisms of Small Integral Membrane Protein 3 (SMIM3) have seldomly been investigated. To this date, the prognostic value of SMIM3 in AML has not been reported. This study aimed to explore the clinical significance, biological effects and molecular mechanisms of SMIM3 in AML. METHODS: RT-qPCR was applied to detect the expression level of SMIM3 in bone marrow specimens from 236 newly diagnosed adult AML patients and 23 healthy volunteers. AML cell lines, Kasumi-1 and THP-1, were used for lentiviral transfection. CCK8 and colony formation assays were used to detect cell proliferation. Cell cycle and apoptosis were analyzed by flow cytometry. Western blot was performed to explore relevant signaling pathways. The biological functions of SMIM3 in vivo were validated by xenograft tumor mouse model. Survival rate was evaluated by Log-Rank test and Kaplan-Meier. Cox regression model was used to analyze multivariate analysis. The correlations between SMIM3 and drug resistance were also explored. RESULTS: Through multiple datasets and our clinical group, SMIM3 was shown to be significantly upregulated in adult AML compared to healthy subjects. SMIM3 overexpression conferred a worse prognosis and was identified as an independent prognostic factor in 95 adult NK-AML patients. Knockdown of SMIM3 inhibited cell proliferation and cell cycle progression, and induced cell apoptosis in AML cells. The reduced SMIM3 expression significantly suppressed tumor growth in the xenograft mouse model. Western blot analysis showed downregulation of p-PI3K and p-AKT in SMIM3-knockdown AML cell lines. SMIM3 may also be associated with some PI3K-AKT and first-line targeted drugs. CONCLUSIONS: SMIM3 was highly expressed in adult AML, and such high-level expression of SMIM3 was associated with a poor prognosis in adult AML. Knockdown of SMIM3 inhibited the proliferation of AML through regulation of the PI3K-AKT signaling pathway. SMIM3 may serve as a potential prognostic marker and a therapeutic target for AML in the future.

Deep learning-based attenuation correction for whole-body PET - a multi-tracer study with 18F-FDG, 68 Ga-DOTATATE, and 18F-Fluciclovine.

A novel deep learning (DL)-based attenuation correction (AC) framework was applied to clinical whole-body oncology studies using 18F-FDG, 68 Ga-DOTATATE, and 18F-Fluciclovine. The framework used activity (λ-MLAA) and attenuation (µ-MLAA) maps estimated by the maximum likelihood reconstruction of activity and attenuation (MLAA) algorithm as inputs to a modified U-net neural network with a novel imaging physics-based loss function to learn a CT-derived attenuation map (µ-CT). METHODS: Clinical whole-body PET/CT datasets of 18F-FDG (N = 113), 68 Ga-DOTATATE (N = 76), and 18F-Fluciclovine (N = 90) were used to train and test tracer-specific neural networks. For each tracer, forty subjects were used to train the neural network to predict attenuation maps (µ-DL). µ-DL and µ-MLAA were compared to the gold-standard µ-CT. PET images reconstructed using the OSEM algorithm with µ-DL (OSEMDL) and µ-MLAA (OSEMMLAA) were compared to the CT-based reconstruction (OSEMCT). Tumor regions of interest were segmented by two radiologists and tumor SUV and volume measures were reported, as well as evaluation using conventional image analysis metrics. RESULTS: µ-DL yielded high resolution and fine detail recovery of the attenuation map, which was superior in quality as compared to µ-MLAA in all metrics for all tracers. Using OSEMCT as the gold-standard, OSEMDL provided more accurate tumor quantification than OSEMMLAA for all three tracers, e.g., error in SUVmax for OSEMMLAA vs. OSEMDL: - 3.6 ± 4.4% vs. - 1.7 ± 4.5% for 18F-FDG (N = 152), - 4.3 ± 5.1% vs. 0.4 ± 2.8% for 68 Ga-DOTATATE (N = 70), and - 7.3 ± 2.9% vs. - 2.8 ± 2.3% for 18F-Fluciclovine (N = 44). OSEMDL also yielded more accurate tumor volume measures than OSEMMLAA, i.e., - 8.4 ± 14.5% (OSEMMLAA) vs. - 3.0 ± 15.0% for 18F-FDG, - 14.1 ± 19.7% vs. 1.8 ± 11.6% for 68 Ga-DOTATATE, and - 15.9 ± 9.1% vs. - 6.4 ± 6.4% for 18F-Fluciclovine. CONCLUSIONS: The proposed framework provides accurate and robust attenuation correction for whole-body 18F-FDG, 68 Ga-DOTATATE and 18F-Fluciclovine in tumor SUV measures as well as tumor volume estimation. The proposed method provides clinically equivalent quality as compared to CT in attenuation correction for the three tracers.

Direct and indirect strategies of deep-learning-based attenuation correction for general purpose and dedicated cardiac SPECT.

PURPOSE: Deep-learning-based attenuation correction (AC) for SPECT includes both indirect and direct approaches. Indirect approaches generate attenuation maps (µ-maps) from emission images, while direct approaches predict AC images directly from non-attenuation-corrected (NAC) images without µ-maps. For dedicated cardiac SPECT scanners with CZT detectors, indirect approaches are challenging due to the limited field-of-view (FOV). In this work, we aim to 1) first develop novel indirect approaches to improve the AC performance for dedicated SPECT; and 2) compare the AC performance between direct and indirect approaches for both general purpose and dedicated SPECT. METHODS: For dedicated SPECT, we developed strategies to predict truncated µ-maps from NAC images reconstructed with a small matrix, or full µ-maps from NAC images reconstructed with a large matrix using 270 anonymized clinical studies scanned on a GE Discovery NM/CT 570c SPECT/CT. For general purpose SPECT, we implemented direct and indirect approaches using 400 anonymized clinical studies scanned on a GE NM/CT 850c SPECT/CT. NAC images in both photopeak and scatter windows were input to predict µ-maps or AC images. RESULTS: For dedicated SPECT, the averaged normalized mean square error (NMSE) using our proposed strategies with full µ-maps was 1.20 ± 0.72% as compared to 2.21 ± 1.17% using the previous direct approaches. The polar map absolute percent error (APE) using our approaches was 3.24 ± 2.79% (R2 = 0.9499) as compared to 4.77 ± 3.96% (R2 = 0.9213) using direct approaches. For general purpose SPECT, the averaged NMSE of the predicted AC images using the direct approaches was 2.57 ± 1.06% as compared to 1.37 ± 1.16% using the indirect approaches. CONCLUSIONS: We developed strategies of generating µ-maps for dedicated cardiac SPECT with small FOV. For both general purpose and dedicated SPECT, indirect approaches showed superior performance of AC than direct approaches.

Post-reconstruction attenuation correction for SPECT myocardium perfusion imaging facilitated by deep learning-based attenuation map generation.

BACKGROUND: Attenuation correction can improve the quantitative accuracy of single-photon emission computed tomography (SPECT) images. Existing SPECT-only systems normally can only provide non-attenuation corrected (NC) images which are susceptible to attenuation artifacts. In this work, we developed a post-reconstruction attenuation correction (PRAC) approach facilitated by a deep learning-based attenuation map for myocardial perfusion SPECT imaging. METHODS: In the PRAC method, new projection data were estimated via forwardly projecting the scanner-generated NC image. Then an attenuation map, generated from NC image using a pretrained deep learning (DL) convolutional neural network, was incorporated into an offline reconstruction algorithm to obtain the attenuation-corrected images from the forwardly projected projections. We evaluated the PRAC method using 30 subjects with a DL network trained with 40 subjects, using the vendor-generated AC images and CT-based attenuation maps as the ground truth. RESULTS: The PRAC methods using DL-generated and CT-based attenuation maps were both highly consistent with the scanner-generated AC image. The globally normalized mean absolute errors were 1.1% ± .6% and .7% ± .4% and the localized absolute percentage errors were 8.9% ± 13.4% and 7.8% ± 11.4% in the left ventricular (LV) blood pool, respectively, and - 1.3% ± 8.0% and - 3.8% ± 4.5% in the LV myocardium for PRAC methods using DL-generated and CT-based attenuation maps, respectively. The summed stress scores after PRAC using both attenuation maps were more consistent with the ground truth than those of the NC images. CONCLUSION: We developed a PRAC approach facilitated by deep learning-based attenuation maps for SPECT myocardial perfusion imaging. It may be feasible for this approach to provide AC images for SPECT-only scanner data.

CT-free attenuation correction for dedicated cardiac SPECT using a 3D dual squeeze-and-excitation residual dense network.

BACKGROUND: Attenuation correction (AC) using CT transmission scanning enables the accurate quantitative analysis of dedicated cardiac SPECT. However, AC is challenging for SPECT-only scanners. We developed a deep learning-based approach to generate synthetic AC images from SPECT images without AC. METHODS: CT-free AC was implemented using our customized Dual Squeeze-and-Excitation Residual Dense Network (DuRDN). 172 anonymized clinical hybrid SPECT/CT stress/rest myocardial perfusion studies were used in training, validation, and testing. Additional body mass index (BMI), gender, and scatter-window information were encoded as channel-wise input to further improve the network performance. RESULTS: Quantitative and qualitative analysis based on image voxels and 17-segment polar map showed the potential of our approach to generate consistent SPECT AC images. Our customized DuRDN showed superior performance to conventional network design such as U-Net. The averaged voxel-wise normalized mean square error (NMSE) between the predicted AC images by DuRDN and the ground-truth AC images was 2.01 ± 1.01%, as compared to 2.23 ± 1.20% by U-Net. CONCLUSIONS: Our customized DuRDN facilitates dedicated cardiac SPECT AC without CT scanning. DuRDN can efficiently incorporate additional patient information and may achieve better performance compared to conventional U-Net.

MZB1 targeted by miR-185-5p inhibits the migration of human periodontal ligament cells through NF-κB signaling and promotes alveolar bone loss.

OBJECTIVE: To explore the role of Marginal Zone B and B-1 Cell-Specific Protein (MZB1), a novel molecule associated with periodontitis, in migration of human periodontal ligament cells (hPDLCs) and alveolar bone orchestration. BACKGROUND: MZB1 is an ER-localized protein and its upregulation has been found to be associated with a variety of human diseases. However, few studies have investigated the effect and mechanism of MZB1 on hPDLCs in periodontitis. METHODS: Gene expression profiles in human gingival tissues were acquired from the Gene Expression Omnibus (GEO) database, and candidate molecules were then selected through bioinformatic analysis. Subsequently, we identified the localization and expression of MZB1 in human gingival tissues, mice, and hPDLCs by immunofluorescence, RT-qPCR, and Western blot. Dual-luciferase reporter assay was applied to assess the binding of miR-185-5p to MZB1. Furthermore, the effects of MZB1 on cell migration, proliferation, and apoptosis in vitro were investigated by wound-healing assay, transwell assay, CCK-8 assay, and flow cytometry analysis. Finally, Micro-CT analysis and H&E staining were performed to examine the effects of MZB1 on alveolar bone loss in vivo. RESULTS: Bioinformatic analysis discovered that MZB1 was one of the most significantly increased genes in periodontitis patients. MZB1 was markedly increased in the gingival tissues of periodontitis patients, in the mouse models, and in the hPDLCs treated with lipopolysaccharide of Porphyromonas gingivalis (LPS-PG). Furthermore, in vitro experiments showed that MZB1, as a target gene of miR-185-5p, inhibited migration of hPDLCs. Overexpression of MZB1 specifically upregulated the phosphorylation of p65, while pretreatment of MZB1-overexpressed hPDLCs with PDTC (NF-κB inhibitor) notably reduced the p-p65 level and promoted cell migration. In addition, the mRNA expression levels of alkaline phosphatase (ALP) and Runt-related transcription factor 2 (Runx2) were inhibited in MZB1-overexpressed hPDLCs and miR-185-5p inhibitor treated hPDLCs, respectively. In vivo experiments showed that knockdown of MZB1 alleviated the loss of alveolar bone. CONCLUSION: As a target gene of miR-185-5p, MZB1 plays a crucial role in inhibiting the migration of hPDLCs through NF-κB signaling pathway and deteriorating alveolar bone loss.

Deep learning-based attenuation map generation for myocardial perfusion SPECT.

PURPOSE: Attenuation correction using CT transmission scanning increases the accuracy of single-photon emission computed tomography (SPECT) and enables quantitative analysis. Current existing SPECT-only systems normally do not support transmission scanning and therefore scans on these systems are susceptible to attenuation artifacts. Moreover, the use of CT scans also increases radiation dose to patients and significant artifacts can occur due to the misregistration between the SPECT and CT scans as a result of patient motion. The purpose of this study is to develop an approach to estimate attenuation maps directly from SPECT emission data using deep learning methods. METHODS: Both photopeak window and scatter window SPECT images were used as inputs to better utilize the underlying attenuation information embedded in the emission data. The CT-based attenuation maps were used as labels with which cardiac SPECT/CT images of 65 patients were included for training and testing. We implemented and evaluated deep fully convolutional neural networks using both standard training and training using an adversarial strategy. RESULTS: The synthetic attenuation maps were qualitatively and quantitatively consistent with the CT-based attenuation map. The globally normalized mean absolute error (NMAE) between the synthetic and CT-based attenuation maps were 3.60% ± 0.85% among the 25 testing subjects. The SPECT reconstructed images corrected using the CT-based attenuation map and synthetic attenuation map are highly consistent. The NMAE between the reconstructed SPECT images that were corrected using the synthetic and CT-based attenuation maps was 0.26% ± 0.15%, whereas the localized absolute percentage error was 1.33% ± 3.80% in the left ventricle (LV) myocardium and 1.07% ± 2.58% in the LV blood pool. CONCLUSION: We developed a deep convolutional neural network to estimate attenuation maps for SPECT directly from the emission data. The proposed method is capable of generating highly reliable attenuation maps to facilitate attenuation correction for SPECT-only scanners for myocardial perfusion imaging.

Rapamycin inhibits ox-LDL-induced inflammation in human endothelial cells in vitro by inhibiting the mTORC2/PKC/c-Fos pathway.

Rapamycin and its derivative possess anti-atherosclerosis activity, but its effects on adhesion molecule expression and macrophage adhesion to endothelial cells during atherosclerosis remain unclear. In this study we explored the effects of rapamycin on ox-LDL-induced adhesion molecule expression and macrophage adhesion to endothelial cells in vitro and the underlying mechanisms. Ox-LDL (6-48 µg/mL) dose-dependently increased the protein levels of two adhesion molecules, intercellular adhesion molecule-1 (ICAM-1) and E-selectin, in human umbilical vein endothelial cells (HUVECs), whereas pretreatment with rapamycin (1-10 µmol/L) dose-dependently inhibited ox-LDL-induced increase in the adhesion molecule expression and macrophage adhesion to endothelial cells. Knockdown of mTOR or rictor, rather than raptor, mimicked the effects of rapamycin. Ox-LDL (100 µg/mL) time-dependently increased PKC phosphorylation in HUVECs, which was abolished by rapamycin or rictor siRNA. Pretreatment with PKC inhibitor staurosporine significantly reduced ox-LDL-stimulated adhesion molecule expression and macrophage adhesion to endothelial cells, whereas pretreatment with PKC activator PMA/TPA attenuated the inhibitory effect of rapamycin on adhesion molecule expression. Ox-LDL (100 µg/mL) time-dependently increased c-Fos levels in HUVECs, and pretreatment with rapamycin or rictor siRNA significantly decreased expression of c-Fos. Knockdown of c-Fos antagonized ox-LDL-induced adhesion molecule expression and macrophage adhesion to endothelial cells. Our results demonstrate that rapamycin reduces ox-LDL-stimulated adhesion molecule expression and macrophage adhesion to endothelial cells by inhibiting mTORC2, but not mTORC1, and mTORC2 acts through the PKC/c-Fos signaling pathway.

Prognostic Significance of Estimated Glomerular Filtration Rate and Cystatin C in Patients with Acute Intracerebral Hemorrhage.

BACKGROUND: The effects of the estimated glomerular filtration rate (eGFR) and cystatin C on clinical outcomes on intracerebral hemorrhage (ICH) remain unclear. We investigated the associations of eGFR and cystatin C with 3-month functional outcome and all-cause mortality in acute ICH patients. METHODS: A total of 365 patients with acute ICH were enrolled. Serum creatinine and cystatin C levels were measured within 24 h of admission. Outcomes at 3-month were evaluated by interviews with patients or their family members. Poor functional outcome was defined as a modified Rankin Scale score ≥3. RESULTS: During the 3-month follow-up, 154 patients experienced poor functional outcome, and 48 patients died from all causes. Low eGFR level was associated with poor outcome (adjusted OR 8.95; 95% CI 2.13-37.66; p-trend = 0.045) and all-cause mortality (adjusted hazards ratio (HR) 5.10; 95% CI 2.00-13.03; p-trend = 0.001). Additionally, a high cystatin C level was also found to be associated with all-cause mortality (adjusted HR 4.01; 95% CI 1.09-14.72; p-trend = 0.015). However, no significant association between cystatin C and poor functional outcome was observed (p-trend = 0.615). CONCLUSIONS: Low eGFR at baseline predicts poor functional outcome and all-cause mortality at 3-month in acute ICH patients. Also, high cystatin C was associated with increased risk of mortality but not with poor functional outcome.

SIRT1 regulates accumulation of oxidized LDL in HUVEC via the autophagy-lysosomal pathway.

Autophagy is involved in the degradation of oxidized low-density lipoprotein (ox-LDL) in human umbilical vein endothelial cells (HUVECs). Sirtuin1 (SIRT1), a new anti-atherosclerotic factor, can induce autophagy in cardiac myocytes. In the present study, we observed the effect of SIRT1 on the accumulation of ox-LDL in HUVECs, and elucidated whether its effect is relative with the autophagy-lysosomal pathway. The results showed that treatment with either SIRT1 siRNA or SIRT1 inhibitor nicotinamide (NAM) increased Dil-labelled-ox-LDL (Dil-ox-LDL) accumulation in HUVECs, and the SIRT1 inducer resveratrol (RSV) decreased it. Knockdown of autophagy-related protein 5 or inhibit the lysosomal degradation by chloroquine (CQ) decreased the effect of RSV. In HUVECs with ox-LDL, expression of LC3II and LC3 puncta was decreased by treatment with SIRT1 siRNA or NAM, but increased by RSV treatment; sequestosome 1 p62 expression showed the opposite effects. Moreover, Dil-ox-LDL combined with SIRT1 siRNA or NAM showed a much smaller degree of overlap of Lamp1 or Cathepsin D with Dil-ox-LDL than in cells with Dil-ox-LDL alone, and RSV treatment resulted in a greater degree of overlap. These results suggest that SIRT1 can decrease the accumulation of ox-LDL in HUVECs, and this effect is related to the autophagy-lysosomal pathway.

[Development of a Prognostic Model for Overall Survival Adult Patients with Core Binding Factor Acute Myeloid Leukaemia].

OBJECTIVE: To analyze the factors affecting overall survival (OS) of adult patients with core-binding factor acute myeloid leukemia (CBF-AML) and establish a prediction model. METHODS: A total of 216 newly diagnosed patients with CBF-AML in the First Affiliated Hospital of Zhengzhou University from May 2015 to July 2021 were retrospectively analyzed. The 216 CBF-AML patients were divided into the training and the validation cohort at 7∶3 ratio. The Cox regression model was used to analyze the clinical factors affecting OS. Stepwise regression was used to establish the optimal model and the nomogram. Receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) were used to evaluate the model performance. RESULTS: Age(≥55 years old), peripheral blood blast(≥80%), fusion gene (AML1-ETO), KIT mutations were identified as independent adverse factors for OS. The area under the ROC curve at 3-year was 0.772 and 0.722 in the training cohort and validation cohort, respectively. The predicted value of the calibration curve is in good agreement with the measured value. DCA shows that this model performs better than a single factor. CONCLUSION: This prediction model is simple and feasible, and can effectively predict the OS of CBF-AML, and provide a basis for treatment decision.

Cysteine- and glycine-rich protein 1 predicts prognosis and therapy response in patients with acute myeloid leukemia.

Acute myeloid leukemia (AML) is a heterogeneous disease with a poor prognosis. The current risk stratification system is essential but remains insufficient to select the best schedules. Cysteine-rich protein 1 (CSRP1) is a member of the CSRP family and associated with poor clinicopathological features in many tumors. This study aimed to explore the clinical significance and molecular mechanisms of cysteine- and glycine-rich protein 1 (CSRP1) in AML. RT-qPCR was used to detect the relative expression of CSRP1 in our clinical cohort. Functional enrichment analysis of CSRP1-related differentially expressed genes was carried out by GO/KEGG enrichment analysis, immune cell infiltration analysis, and protein-protein interaction (PPI) network. The OncoPredict algorithm was implemented to explore correlations between CSRP1 and drug resistance. CSRP1 was highly expressed in AML compared with normal samples. High CSRP1 expression was an independent poor prognostic factor. Functional enrichment analysis showed neutrophil activation and apoptosis were associated with CSRP1. In the PPI network, 19 genes were present in the most significant module, and 9 of them were correlated with AML prognosis. The high CSRP1 patients showed higher sensitivity to 5-fluorouracil, gemcitabine, rapamycin, cisplatin and lower sensitivity to fludarabine. CSRP1 may serve as a potential prognostic marker and a therapeutic target for AML in the future.

TAI-GAN: A Temporally and Anatomically Informed Generative Adversarial Network for early-to-late frame conversion in dynamic cardiac PET inter-frame motion correction.

Inter-frame motion in dynamic cardiac positron emission tomography (PET) using rubidium-82 (82Rb) myocardial perfusion imaging impacts myocardial blood flow (MBF) quantification and the diagnosis accuracy of coronary artery diseases. However, the high cross-frame distribution variation due to rapid tracer kinetics poses a considerable challenge for inter-frame motion correction, especially for early frames where intensity-based image registration techniques often fail. To address this issue, we propose a novel method called Temporally and Anatomically Informed Generative Adversarial Network (TAI-GAN) that utilizes an all-to-one mapping to convert early frames into those with tracer distribution similar to the last reference frame. The TAI-GAN consists of a feature-wise linear modulation layer that encodes channel-wise parameters generated from temporal information and rough cardiac segmentation masks with local shifts that serve as anatomical information. Our proposed method was evaluated on a clinical 82Rb PET dataset, and the results show that our TAI-GAN can produce converted early frames with high image quality, comparable to the real reference frames. After TAI-GAN conversion, the motion estimation accuracy and subsequent myocardial blood flow (MBF) quantification with both conventional and deep learning-based motion correction methods were improved compared to using the original frames. The code is available at https://github.com/gxq1998/TAI-GAN.

Alternative splicing of HOXB-AS3 underlie the promoting effect of nuclear m6A reader YTHDC1 on the self-renewal of leukemic stem cells in acute myeloid leukemia.

This research sought to elucidate the mechanism underlying the self-renewal capacity of leukemic stem cells (LSCs) to offer new insights into the treatment of acute myeloid leukemia (AML). The expression of HOXB-AS3 and YTHDC1 in the AML samples was screened and verified in THP-1 cells and LSCs. The relationship between HOXB-AS3 and YTHDC1 was determined. HOXB-AS3 and YTHDC1 were knocked down through cell transduction to examine the effect of HOXB-AS3 and YTHDC1 on LSCs isolated from THP-1 cells. Tumor formation in mice was used to verify fore experiments. HOXB-AS3 and YTHDC1 were robustly induced in AML, in correlation with adverse prognosis in patients with AML. We found YTHDC1 bound HOXB-AS3 and regulated its expression. Overexpression of YTHDC1 or HOXB-AS3 promoted the proliferation of THP-1 cells and LSCs and impaired their apoptosis, increasing the number of LSCs in the blood and bone marrow of AML mice. YTHDC1 could upregulate the expression of HOXB-AS3 spliceosome NR_033205.1 via the m6A modification of HOXB-AS3 precursor RNA. By this mechanism, YTHDC1 accelerated the self-renewal of LSCs and the subsequent AML progression. This study identifies a crucial role for YTHDC1 in the regulation of LSC self-renewal in AML and suggests a new perspective for AML treatment.

Deep learning-based attenuation map generation with simultaneously reconstructed PET activity and attenuation and low-dose application.

Objective. In PET/CT imaging, CT is used for positron emission tomography (PET) attenuation correction (AC). CT artifacts or misalignment between PET and CT can cause AC artifacts and quantification errors in PET. Simultaneous reconstruction (MLAA) of PET activity (λ-MLAA) and attenuation (µ-MLAA) maps was proposed to solve those issues using the time-of-flight PET raw data only. However,λ-MLAA still suffers from quantification error as compared to reconstruction using the gold-standard CT-based attenuation map (µ-CT). Recently, a deep learning (DL)-based framework was proposed to improve MLAA by predictingµ-DL fromλ-MLAA andµ-MLAA using an image domain loss function (IM-loss). However, IM-loss does not directly measure the AC errors according to the PET attenuation physics. Our preliminary studies showed that an additional physics-based loss function can lead to more accurate PET AC. The main objective of this study is to optimize the attenuation map generation framework for clinical full-dose18F-FDG studies. We also investigate the effectiveness of the optimized network on predicting attenuation maps for synthetic low-dose oncological PET studies.Approach. We optimized the proposed DL framework by applying different preprocessing steps and hyperparameter optimization, including patch size, weights of the loss terms and number of angles in the projection-domain loss term. The optimization was performed based on 100 skull-to-toe18F-FDG PET/CT scans with minimal misalignment. The optimized framework was further evaluated on 85 clinical full-dose neck-to-thigh18F-FDG cancer datasets as well as synthetic low-dose studies with only 10% of the full-dose raw data.Main results. Clinical evaluation of tumor quantification as well as physics-based figure-of-merit metric evaluation validated the promising performance of our proposed method. For both full-dose and low-dose studies, the proposed framework achieved <1% error in tumor standardized uptake value measures.Significance. It is of great clinical interest to achieve CT-less PET reconstruction, especially for low-dose PET studies.